Ordaōs in the News

Quercus Biosolutions emerges from stealth to disrupt crop protection with designer mini proteins

Admin — Mon, 10 Mar 2025 04:00:00 GMT

Quercus Biosolutions, an ag biotech startup using generative AI to create a new category of crop protection products based on designer mini proteins, has emerged from stealth with pre-seed funding from undisclosed investors.

The firm, founded last year by ag industry veterans Dr. Jon Lightner and Matt Crisp, has a strategic partnership with drug discovery startup Ordaos Bio to apply the latter’s generative AI platform—originally designed for human medicine—to agriculture.

The approach could potentially deliver a step change in crop protection, Crisp told AgFunderNews.

“We’ve been saying for 20 years that ‘In the next five years, biologicals are going to overtake chemistry-based solutions,’ and we’re all looking at each other right now and saying, ‘Well, we’ve gotten a few points of market share after 20 years and many billions of dollars of investment.’”

With Quercus’ platform, he claimed, “I feel like we’re going to achieve more in crop protection using this technology in the next two years thanwe have in the last 20.”

At its core, said Lightner, “We expect this platform can finally deliver the efficacy that growers and farmers associate with small molecules [and chemical crop inputs] to the biologicals segment.”

Designer proteins

Several firms in ag biologicals now use mini proteins, also known as peptides or micropeptides (small chains of amino acids) rather than living microorganisms for crop protection purposes, citing increased stability, precision, and consistency.

However most players typically screen libraries of naturally occurring proteins to identify those with potential to target pests or perform other crop protection functions, said Lightner, who is working with Solis Agrosciences for testing, data generation, optimization, and validation.

Quercus, by contrast, is using AI to design mini proteins from scratch, he said. “We look at all possible proteins, model all possible structures, and then test and build subsets of those in a days-long process for computational testing. We can then produce them via cell-free expression for testing in vitro the next day.”

Ordaos Bio CEO Dave Longo added: “A lot of people claim they are doing truly de novo protein design, but most AI companies claiming this are taking an existing protein and simply replacing the sequence that makes up that protein to get the same structure.

“For instance, if you have something that binds to HER2 [Human Epidermal Growth Factor Receptor 2, a common target for breast cancer drugs] they would take the original protein and ask the AI, can you give us a different sequence so that we can patent it, with the same structure as this existing protein?

“It’s hard to call that design. What we’re doing is giving the system all it needs to understand what your mini protein has to do such as crossing the plant cuticle or crossing the cell membrane, and then designing a protein truly from scratch.”

‘Head-to-head comparisons with the best chemical solutions’

According to Crisp: “I actually think the same problems persist for many of the biologicals out there, whether it’s peptides, RNAi, small proteins, large proteins, or whole microorganisms. I think a similar set of challenges continues to plague this entire space.”

Developing designer proteins from scratch can solve many of these problems, claimed Lightner.

“We’re building our assays around head-to-head comparisons with the best current chemical solutions. We think we’ll create an equally large crop protection category that’s based entirely on these novel proteins, with all the favored regulatory and environmental advantages of biologicals, but also a rock solid, consistent efficacy of the kind that people have achieved with chemistry in agriculture.”

Designing for multiple factors at once

Typically, said Crisp, firms developing crop protection products look at the problem they are trying to solve in a linear fashion “starting from a mode of action perspective.”

After that, they then tackle all the other challenges that come along with developing a commercial product such as formulation, stability, supply chain constraints, whether it will mix in a tank with other crop protection inputs, and whether it can be cost-effectively manufactured via precision fermentation on a commercial scale, he said.

Quercus can design products that factor in all of these things from the outset, he claimed.

“That’s what gets me so jazzed. We can build all those requirements right into our very early screens and then feed those results back into our AI.”

Go where the money is: drug discovery

To place Quercus in context, Crisp said, “I’d say ag is 5-15 years behind all of the technological enabling platform work that’s done in the most well-funded arena of innovation, which is human medicine.

“We looked across the AI space in agriculture,” said Lightner, “but Matt’s view was, if we really want to see the cutting edge of AI, we need to look at the pharmaceutical sector.”

Crisp explained: “I left [seed giant] Benson Hill a couple years ago and since then I’ve been looking at a number of different innovations and opportunities including newer generation biologicals. But I didn’t see the kind of Gen AI that was beginning to be deployed in the arena of cancer therapeutics, for example.

“So my view is, go where the money is. Companies in human medicine have been receiving millions of dollars to develop some of the most innovative platform technologies to do bespoke biological design, including for peptides and proteins. So could we repurpose one of those platforms for agriculture?

“So Jon and I did some diligence on companies in human healthcare and were really impressed with Ordaos Bio’s platform, and pretty soon came to the conclusion that it could accelerate a quantum of value creation in crop protection.”

The attractive thing about some of the bespoke mini proteins Quercus is developing is that they could be as effective as synthetic chemicals, but will be regulated as biopesticides and will not persist in the environment for decades, claimed Lightner.

“Bt technologies [deployed in GM maize and cotton for example] are beginning to fail and so we need innovation desperately. And these protein crop protectants can operate under the biopesticide framework with the EPA that’s typically a million dollars to an approval, and less than two years. We can create solutions that we can bring to market on a really quick timeframe.”

Manufacturing the mini proteins at scale

So can Quercus’s mini proteins be chemically synthesized at scale or would they be expressed in microbial cells via precision fermentation?

According to Lightner: “Many of the proteins we’re working with in the R&D phase we’ve had built synthetically, but for large-scale production we would be looking at recombinant protein expression in microbial systems.”

The proteins would then be extracted, he said, but this would not involve super-costly downstream processing steps. “We believe at scale we can compete head-to-head with small molecule chemistry on a cost basis, with attractive margins.”

‘The models get better and better’

According to Lightner: “What we’re doing in the next six to 12 months is focusing on known sites of action for particular crop protection problems. So that’s a place where we might have a small molecule [chemical] inhibitor today or we might have a naturally-derived molecule like a Bt protein, and we know the receptor or the enzyme that those actives inhibit to get the crop protective effect.”

Quercus knows the proteins the receptors interact with and usually already has crystal structures “as those proteins have been around for decades,” he said. “We then predict structures for our [designer] proteins to fit the active site or inhibit particular catalytic residues, for example.

“We do that computationally first and then test in vitro for those actual interactions. One of the unique things about Ordaos Bio’s approach is that they’ve built a connection between the computational prediction models and the in vitro testing, so the test results inform the model, and the models get better and better.”

He added: “One thing we’ve learned across statistical and genomic selection models over the last 20 years in ag is that the best model is the one we built today from all the available data… and that model will be obsolete tomorrow.”

Field level validation ‘in as little as six months’

In the next three to six months, said Crisp, Quercus aims to start conducting “greenhouse and growth chamber level studies with the potential to move to field level validation in as little as six months, given the speed of the platform.”

He added: “We see huge opportunity for IP in the space, so we’re looking to establish a strong IP portfolio founded on a unique data set around the application of AI protein design to this crop protection space.”

Defining mini proteins

As for what constitutes a “mini” protein, there’s no firm definition, said Lightner.

“The important thing is we’re looking at proteins with tertiary structure. In other words, not just a linear sequence of amino acids that’s wiggling around, but proteins large enough to have structural attributes. I haven’t seen any hard and fast definitions, but we’re looking at a minimum of 15-20 amino acids and probably far more likely to be 25-50 amino acids.”

Ordaōs Opens Lab Space in JLABS@NYC

Admin — Thu, 24 Aug 2023 04:00:00 GMT

We are pleased to announce that Ordaōs has opened a lab space at JLABS@NYC as a JLABS resident company to ultimately optimize our R&D efforts and advance our existing capabilities in mini-protein drug design from in silico to in vitro.

This residency represents a significant milestone for Ordaōs as it provides an invaluable support to expedite our research and development programs, expand our technology, and establish our in-house wet lab. Using high throughput data generation, we are able to advance our capabilities from in silico to in vitro allowing us to close our continuous learning loop and feed key learnings back to the Ordaōs Design Engine, expanding our proprietary data and improving the design performance. At our core, we are an asset foundry, and this lab space allows us to accelerate asset development for both our internal and external pipelines using in-house binding and functional assays. Although our approach to drug design is unique to others in the drug discovery space, we validate through the most stringent industry-standards to demonstrate that our proteins have therapeutic value.

The stimulating and cutting-edge environment at JLABS which provides a vast array of expansive resources, entrepreneurial programs, and collaboration opportunities with seasoned experts, will undoubtedly enhance our ability to address challenges with greater efficiency and effectiveness while fueling our growth trajectory.

At Ordaōs, we are harnessing the power of generative AI to create novel and customized mini-proteins to help our biopharma partners develop safer and more effective life-saving treatments, that are easier and less expensive to develop and manufacture than traditional antibodies. Seeking the right molecules to develop a protein drug has traditionally required a search through existing molecules. Unlike this conventional method, Ordaōs designs mini-proteins from scratch and thus is not limited by what exists but rather by our imagination, opening up a world of unique and limitless possibilities. This JLABS residency and lab space will immeasurably help to advance our novel approach to drug design.

To learn more about Ordaōs’ novel approach to drug design, delivery of customized mini-proteins, and our JLABS residency, please visit J&J Innovation’s “Ordaōs Bio Spotlight”.

Ordaōs Uses AI to Generate Therapeutic Mini-Proteins

Admin — Tue, 08 Aug 2023 04:00:00 GMT

My graduate advisor used to say that I could do all the work I wanted on a computer to develop a hypothesis, but no matter what, I would have to work with living things to prove it. By developing its own in silico assets, the generative AI company Ordaōs is ready to do the functional work independently.

After using contract research organizations (CROs) for several years to assist in bringing these in silico mini-proteins to life, Ordaōs officially opened its lab space at JLABS @ NYC in June 2023 to kick off its partnership with Johnson & Johnson Innovation. As a JLABS resident company, the lab space will optimize Ordaōs’ R&D efforts and grow its capabilities to fuel its novel approach to drug design. Ordaōs works on overlooked rare diseases, primarily focusing on cancer, with programs in triple negative breast cancer and pancreatic cancer.

Ziwei Liang, PhD, chief science officer at Ordaōs said that the residency at the JLABs facility in the SoHo district of Manhattan will add three main streams of value. First, it will close the continuous learning loop from in silico to in vitro by providing high throughput and quality data generation; this can feed back to the in silico engine at Ordaōs with proprietary data for training and improvement. Second, it will fast track asset development by bringing in binding and functional assays designed in-house to fit specific targets. And third, it will provide the opportunity for technological innovation from the biological side.

“We are open for business,” said David Longo, CEO of Ordaōs. “We’re an asset foundry that has both an internal and an external pipeline that we are happy to partner with both biotech and pharma. And we recently opened our lab and are excited to continue to grow and serve the world’s ailing populations.”

The in silico funnel

Longo isn’t the first CEO I’ve met recently who notes that there is a sentiment in the drug development industry that nobody is actually using AI and that they’re one of the few actually using AI in the industry (Alex Zhavoronkov of Insilico Medicine is the other).

“Ordaōs is a company that brings the order of digital bits to the chaos of physical atoms, using generative AI technology in protein design,” said Longo.

But it’s not the garish and borderline-gimmicky elevator pitch that convinces me that Ordaōs, which is a portamento of “order” and “chaos”, is actually using AI. Rather, it is how Longo explains the AI-driven drug discovery funnel at his company. Ordaōs is designing mini-proteins, which Longo said is a greenfield modality that provides many advantages over antibodies. “They’re twenty times smaller, more stable, easier to produce and manufacture—and we’re creating them from scratch with AI,” said Longo.

All of their internal and external programs pass through stages of QC (for purity and aggregation) and characterization for binding to the target epitope, proteins, and cells before ever going into functional testing.

“This approach is antithetical to the way that most in pharma do it, because it’s going from a component perspective, where it’s typically done starting with functional assays,” said Longo. “You’re starting with the end goal, and the only thing you can really see is success, which is a very coarse signal because you don’t really know where it’s binding, and the failures are also very coarse and opaque.”

Ordaōs makes predictions on every assay at the component level so that as it moves to functional assays, the company hypothesizes what would cause that functional result.

“We can confirm every step of the way if it’s the binding affinity that’s off, or the epitope that’s off, or maybe it’s something in the purity or aggregation,” said Longo. “The false positives are far more costly than the false negatives in AI-driven drug design, but as we are collecting so much negative data, we have a smaller likelihood of false positives.”

Hotdog, not-hot dog

To explain how generative AI works, Longo recalls a skit in the television show Silicon Valley: developer Jìan-Yáng (played by comedian Jimmy O. Yang), unveils his new app. Using his phone, he takes a picture of a hot dog, and the app spits back a green check mark next to the word “hotdog.” Then, he takes a picture of a pizza, and the app spits back a red X next to the words “not hotdog.” This kind of neural network results from training an algorithm to detect images of hotdogs—after being fed many images of hotdogs.

For generative AI, the neural networks have to come up with what they are “seeing” on their own. After being fed pictures of hotdogs and pepperoni pizza, the generative AI may come up with two classes of items: one a red cylinder, the other a circular or slice shape with spots of red amongst a white or yellow background.

Another metaphor that Longo leans on is two “children” that walk into a zoo: one is a “predictive” child, and the other a “generative” child. After walking past the tigers and elephants, they come across a zebra. The “predictive” child will classify the zebra as most likely a tiger because both have stripes, whereas the “generative” child will compare the zebra to the tiger and elephant and say that, though this has four legs like the others, it has stripes and hooves, doesn’t have a mane or a trunk, and it doesn’t roar or trumpet but instead brays—though it’s kind of like a tiger, this must be something else.

From bits to benchtop

Over the past three years, Ordaōs has been designing, testing, and back-training the development of mini-proteins in silico, and testing them functionally, using CROs.

In December 2022, Ordaōs Liang became CSO to drive the drug discovery strategy, manage multiple research programs, and lead a team of machine learning and AI scientists to develop an internal in silico pipeline for de novo protein generation. The mini-proteins generated by the AI engine at Ordaōs are not constrained to have a particular mechanism of action. For that reason, the lab will allow Ordaōs to design and execute the right assay to drive the asset forward.

“It’s very important for us to control the quality and assay development in-house, so we can really fast track this process and maintain the high-quality control,” said Liang.

With JLABS, Ordaōs will have access to facilities like tissue culture rooms and key equipment, such as an HPLC or flow cytometer, that may be difficult for an early-stage company to obtain but is necessary for high throughput and high-quality data generation and characterization. In addition to the quality, care, and safety of the established structure at Johnson & Johnson, the collaboration provided a mentor.

While JLABS provides flexibility and control over complicated assays, Ordaōs has not fully detached from its use of CROs. “We are not eliminating the option of using CROs for very productionized standard experiments, like standard protein production that is cell-based,” said Longo. “They do it very fast and have very high purity and quality.”

That doesn’t mean that the lab at Ordaōs will not look into protein production. Longo mentions that he’d like to explore cell-free protein production because it’s a unique technology that can give them a real advantage in bringing down the cost and increasing the production quality for mini-proteins.

Running of the bulls

The appetites of biotech investors for companies expanding in AI have been picking up in 2023. In mid-July, Recursion Pharmaceuticals (RXRX), an AI-focused drug developer, announced a $50-million investment by Nvidia (NVDA), a company increasingly focused on AI. This triggered a doubling of the stock price of RXRX, jumping from $6.78 on July 11 to close last month at $14.12.

While Insilico Medicine hasn’t gone public, they announced the investigational new drug (IND) filing of another preclinical candidate developed leveraging its proprietary AI drug discovery platform, Pharma.AI. The IND application is for ISM001-055, an anti-fibrotic small molecule inhibitor developed for treating Idiopathic Pulmonary Fibrosis (IPF). In the past year, Insilico nominated a handful of preclinical candidates and generated positive topline Phase I data in human clinical trials with ISM018-055 for IPF received Orphan Drug Designation from the FDA, and is nearing Phase II clinical trials.

On top of that, Moderna and IBM are teaming up to use generative AI and quantum computing to advance mRNA technology, the development at the core of Moderna’s COVID vaccine. The agreement allows Moderna to access IBM’s quantum computing systems and generative AI model. The agreement comes as Moderna navigates its post-pandemic boom driven by its successful mRNA vaccines.

By working on mini-proteins, Ordaōs may be able to cut a slice of the AI generative pie that no one else will touch. However, if Insilico Medicine and Recursion Pharmaceuticals are real generative AI companies, they may have gotten hits for mini proteins. And according to ChatGPT3, they’re not alone; there are a few other AI companies in biotech: Atomwise, Deep Genomics, BenevolentAI, Berg Health, Zymergen, and Ginkgo Bioworks. But if they are truly generative AI and are using their own programs and data sets, they are unlikely to have generated identical assets.

AI has secured a footing in drug discovery. Where does it go from here?

Admin — Fri, 30 Jun 2023 04:00:00 GMT

AI has secured a footing in drug discovery. Where does it go from here?

Ordaōs Selected to Participate in the AWS Generative AI Accelerator

Admin — Fri, 09 Jun 2023 04:00:00 GMT

NEW YORK, May 31, 2023 /PRNewswire/ -- Ordaōs, a human-enabled, machine-driven drug design company that creates novel and customized mini-proteins, today announced it has been selected to participate in the inaugural cohort of the AWS Generative AI Accelerator, a technical, business, and mentorship program for generative AI-focused startups to help accelerate their cloud-native business. This opportunity will support Ordaōs' efforts to help biopharma partners to create better, safer, and more effective drugs, particularly in difficult-to-treat disease areas with patients who have limited treatment options.

The AWS Generative AI Accelerator is a technical and business mentorship program focused on providing founders of early-stage startups using generative AI to solve complex problems with access to training, mentorship, tools, and resources to turbocharge their cloud-native businesses.

Ordaōs' proprietary Design Engine leverages multitask meta-learning to create novel mini-proteins from scratch. Mini-proteins are a new class of proteins that can revolutionize the role of proteins in drug discovery because they have several advantages over larger proteins, including penetrability, stability, and solubility. They can also be designed to minimize immunogenicity, and they are more easily manufactured. The AWS Generative AI Accelerator program will help fuel Ordaōs' efforts to enhance its technological capabilities, while also exploring new collaborations with organizations working to positively impact human health.

"We are thrilled to join the select group of innovative companies invited to the AWS Generative AI Accelerator program as we all seek to leverage the power and promise of AI to advance discovery and innovation across industries," said David Longo, CEO of Ordaōs. "At Ordaōs, we believe that the evolution of AI and machine learning will ultimately lead to nothing less than a true paradigm shift in how early drug design, no longer discovery, is done. Our mission at Ordaōs is to define the design standard and we are excited to learn from other visionaries in the program to help advance our efforts."

As one of 21 companies chosen for this opportunity from more than 1,200 applicants worldwide, Ordaōs will receive up to $300,000 in AWS credits, extensive training, mentoring and technical guidance, as well as introductions to industry leaders, networking opportunities with potential investors, and ongoing advisory support.

"Generative AI is one of those game-changing tech innovations that will rapidly redefine how we work across a variety of industries," said Howard Wright, Vice President and Global Head of Startups at AWS. "The startup community will be the driving force moving these innovations forward. We're excited to welcome the most promising companies in this space and to provide them with access to the tools and mentors they need to make their ideas a reality."

For more information on the AWS Generative AI Accelerator, visit https://aws-startup-lofts.com/amer/program/accelerators/generative-ai.

About Ordaōs:Ordaōs is a human-enabled, machine-driven drug design company that helps birth novel therapies to reduce patient suffering, improve health, and extend life. Our flagship solution, miniPRO™ mini-proteins, enable drug hunters to deliver safer and more effective treatments in a fraction of the time of traditional discovery methods.

Can drug design replace drug discovery? Current JLABS resident Ordaos thinks so and explains how

Admin — Thu, 09 Mar 2023 05:00:00 GMT

Tell us what challenge Ordaōs is trying to solve and how?

Ultimately, Ordaos is trying to find new ways to enhance the speed and efficiency of bringing safe and effective treatments to patients in need. And how? While monoclonal antibodies and the larger proteins have had success and have been approved for treatment across a wide variety of diseases, they do have their limitations. The emergence of mini-proteins is addressing these shortcomings and offers some advantages over larger proteins. For instance, mini-proteins can deliver the power and performance of antibodies, while being more stable, configurable, and easier to manufacture. Their size is actually their greatest strength; typically twenty times smaller than antibodies. They can thus go where other antibodies cannot. These smaller packages also have a tightly packed internal core that improves thermostability and solubility, while minimizing immunogenicity. Given this, mini-proteins provide an effective alternative to the biologic standard of care. They have the potential to make what was once undruggable, druggable. They are also faster to make, easier to produce, and if done right, a lot less costly. And because we design rather than discover them, they can meet the specific target product profiles as specified by the drug hunter. These advantages help Ordaos to de-risk the drug development process, with the aim of getting treatments to patients that are truly effective. In short, Ordaos is in many ways a generative mini-protein asset foundry that is not replacing monoclonal antibodies but rather offering an effective alternative and complement.

Where did the name ‘Ordaōs’ come from?

Our novel technology, the Ordaōs Design Engine, leverages multitask meta-learning and continuous learning loops to translate human-targeted product criteria into machine-designed mini-proteins. It is thus befitting that the Ordaōs name evolved out of the concept that we bring Order to the Chaos of drug discovery and development. This name reflects who we are and what our mission is.

We see several dimensions for Artificial Intelligence (AI) to create value in drug discovery, including greater productivity (faster speed and/or lower cost), broader molecular diversity and improved chances of clinical success. What is Ordaōs’ approach to the drug discovery process? How is it unique compared to other AI/Machine Learning companies?

Ordaōs uses the Ordaōs Design Engine to deliver true protein property design. Starting with amino acids, the Design Engine generates, appraises, and ranks billions of protein sequences and hundreds of thousands of protein structures and properties to create customized miniPRO™ proteins. These proteins are then rapidly evaluated in silico to provide intelligent feedback on multiple design objectives including protein structure, binding specificity and affinity, solubility, stability, immunogenicity, and developability, followed by in vitro validations. This iterative process delivers optimized mini-proteins to meet the partner’s specific molecular target product profile (mTPP). Using this approach, the Ordaōs Design Engine creates more ideal, unseen protein leads than others and can accelerate drug candidate development, increasing the probability of more therapeutically effective candidates.
More simply put, other companies start with words where Ordaōs starts with letters. This allows us to find more protein candidates and ensures that mini-PRO ™ proteins has the ideal physiochemical properties and are better customized.

When you say that future therapeutic advances may come from design rather than discovery, how will the design be applied? Are there therapeutic areas where this approach is more (or less) applicable?

Seeking the desirable requires more than a search through the known. Protein drug hunters today search through vast databases of existing molecules to find a starting point that is “good enough.” The result is molecules riddled with side effects and limited impact. With discovery we are limited by what we can see, what already exists. With design, we are limited only by our imagination. When it comes to the applicability: we started with aggressive cancers that have high unmet needs, including triple-negative breast cancer, esophageal cancer, and AML, and currently have five programs in total in oncology. We aim to widely serve overlooked patients with limited treatment options, and we seek to achieve that goal through partnerships that are currently rapidly developing.

Since you can generate lead assets very quickly and amass a large portfolio of protein binders in many therapeutic areas, how do you prioritize which projects to advance?

Because the Design Engine allows for extremely effective, highly repeatable and rapid results, we have the capacity to run projects concurrently; this means we can help partners address many complex projects at once. For that reason, we haven’t needed to prioritize one project over another and we can truly expand the reach of this amazing science. Any prioritization is based on the needs of our partners and we work with them to deliver on their requirements using our technology to generate, appraise, and rank hundreds of thousands of proteins on their structures and drug-like properties for novel miniPRO™ candidates. Ultimately, Ordaos is most interested in working on challenging targets with equally interesting scientists!

What has been an intractable drug target thus far that you would like Ordaos to successfully develop a therapeutic against?

We recently partnered with Yatiri Bio to create new therapeutics for two novel targets in acute myeloid leukemia. Yatiri Bio has a base of global proteomics from AML patient samples and matched model systems to identify two cell surface therapeutic targets for high-risk AML. Ordaōs is using its proprietary Ordaōs Design Engine to create a panel of in silico de novo protein candidates and will be responsible for in vitro validation of those leads. While this will not be easy, recent advances in machine learning, mass spectrometry, bioinformatics, and ex-vivo model systems have the potential to drive a new era of precision oncology for AML patients and the development of therapeutics specifically for relevant patient subsets. We are really excited for the promise of our engagement with Yatiri for that reason.

How do you see machine learning evolving and advancing drug discovery?

AI and machine-driven systems provide the biopharma industry with the promise to increase their speed and efficacy, while mitigating risks as far down the pipeline as patient side effects. In the area Ordaos works in, our machine-driven technology allows us to leverage multitask meta-learning and continuous learning loops to translate human-targeted product criteria into machine-designed mini-proteins at an accelerated pace. Our human-enabled machine learning (ML) system can generate, appraise, and rank large amounts of protein sequences, structures and properties to create and deliver optimized mini-proteins easier, faster, and more customized than if we used human talent alone. The introduction of AI/ML has truly transformed the way we conduct drug discovery and development and we expect that it will continue to evolve, improve and expand its applications in the biopharma industry and beyond. We believe that this evolution will ultimately lead to nothing less than a true paradigm shift in how early design, no longer discovery, is done. We see the industry moving to a world of design, to a world of customized creation. Our mission at Ordaōs is to define the design standard.

You announced a couple collaborations last year (congrats!). What type of partnerships are you looking for going forward?

Ordaōs is focusing on difficult-to-drug targets in our internal R&D that serve the overlooked patient population. To date, besides developing in-house its proprietary AI/ML-driven Design Engine, the company has benchmarked using the HER2 protein target in a partnership with Vyant Bio, Inc. and conducted a COVID-19 program with Twist Bioscience Corporation to discover and optimize nanobodies. In addition, in September of last year, Ordaōs announced a third partnership to design therapeutics against three never-before-seen targets with specific product profiles with NonExomics, a Massachusetts biotech focused on determining whether novel proteins from the ‘dark’ genome have therapeutic or diagnostic utility.

What are the current challenges you face as a startup company?

We are growing fast and furious and we hope we can recruit the type of talents excited by this relatively new and emerging area of drug development – the creation of mini-proteins. What keeps me up at night is the dichotomy of the speed and virality of progress witnessed this year in machine-driven tech-bio versus the severe contraction in biotech funding and dealmaking. It is clear that with the right support and the right timing, the technology is at the inflection point between pipe dream and true, value-added reality. We hope that investors will see the positive outlook and want to invest in this intelligent reality.

How are you hoping JLABS can help you accelerate?

We are thrilled to be working with Johnson & Johnson Innovation as a JLABS resident. JLABS offers us the support we need to grow and optimize our research and development. They also provide us with their industry connections, access to their entrepreneurial programs and the resources and lab space to support our upcoming in-house wet lab. With their years of experience, they are able to provide us with wisdom and know-how that can help us catapult over what could be major challenges. We feel privileged to be a resident and thankful that we have this opportunity to scale our science to meet this moment in our company’s evolution.

Ordaōs and FatiAbGen Partner to Optimize Therapeutics for Challenging Oncology Conditions

Admin — Wed, 22 Feb 2023 05:00:00 GMT

Ordaōs to Leverage the Speed and Efficiency of AI and Machine Learning to Improve the Safety and Efficacy of FatiAbGen's Monoclonal Antibodies

NEW YORK, Feb. 22, 2023 /PRNewswire/ -- Ordaōs, a biotechnology company designing novel mini-proteins to help drug hunters deliver life-saving treatments, today announced a collaboration with FatiAbGen, a South Korea-based biopharma company developing monoclonal antibody-based therapies. As a part of their partnership, Ordaōs will use their in silico Design Engine to optimize antibodies for several oncology conditions including pancreatic and ovarian cancer, of which there is a high unmet need.

"Ordaōs is pleased to be collaborating with FatiAbGen to optimize their monoclonal antibodies for the treatment of these debilitating cancers," said Ziwei Liang, CSO of Ordaōs. "Together in partnership, we can address many of the challenges faced in difficult-to-treat diseases and help develop therapeutics for those patients who currently have limited options."

As part of this collaboration, Ordaōs will employ their proprietary in silico Design Engine to optimize the overall safety and efficacy of FatiAbGen's existing monoclonnal anitbodies. The Ordaōs Design Engine, which leverages the power of multitask, metalearning AI, can rapidly enhance FatiAbGen's existing antibodies indicated for a number of oncology conditions. FatiAbGen will then be responsible for developing these antibodies in pre-clinical study.

"We remain committed to meeting the unmet needs of patients by exploring new and challenging technologies and markets," Said JayJay Lee, CEO of FatiAbGen. "Combining our capabilities with Ordaōs and their proprietary Design Engine will further our efforts in creative research, development and delivery to patients in need of a treatment."

FatiAbGen will utilize their capabilities in manufacturing antibodies in therapeutic markets in tandem with their fusion protein antibody drug conjugate(ADC) platform technology. FatiAbGen currently has developed novel antibodies for treating autoimmune diseases that cause allergies, and has previously used their proprietary technology to develop treatments for Mesothelioma and pancreatic cancer.

To learn more, visit the Ordaōs website, or email contact@ordaos.bio.

About Ordaōs
Ordaōs is a human-enabled, machine-driven drug design company that helps birth novel therapies to reduce patient suffering, improve health, and extend life. Our flagship solution, miniPRO™ mini-proteins, enable drug hunters to deliver safer and more effective treatments in a fraction of the time of traditional discovery methods.

About FatiAbGenFatiabgen is a preclinical-stage company that discovers and develops novel and first-in-class therapeutic antibodies for targeted therapy such as fusion protein ADC and Bispecific for the treatment of Mesothelioma, Pancreatic and Ovarian cancer.

More About The Ordaōs Design Engine:Ordaōs uses The Ordaōs Design Engine, to deliver true protein property design - leveraging continuous learning loops and proprietary data sets to translate human-targeted product criteria into machine-designed mini-proteins. Starting with amino acids, the Design Engine generates, appraises, and ranks billions of protein sequences and hundreds of thousands of protein structures and properties to create customized miniPRO™ proteins. These proteins are then rapidly evaluated in vitro to provide intelligent feedback on multiple design objectives including protein structure, binding specificity and affinity, solubility, stability, immunogenicity, and developability. This iterative process delivers optimized mini-proteins to meet the client's specific molecular target product profile (mTPP). They are also less likely to cause adverse side effects and are easier and less expensive to test, develop, and manufacture than traditional proteins. Using this approach, the Ordaōs Design Engine creates more ideal, unseen protein leads than others and can accelerate drug candidate development, increasing the probability of more therapeutically effective candidates. All of this provides clients with a high level of confidence in their investigational new drug (IND) applications.

Scrip Asks…What Does 2023 Hold For Biopharma? Part 6: Artificial Intelligence

Admin — Fri, 10 Feb 2023 05:00:00 GMT

Scrip Asks…What Does 2023 Hold For Biopharma? Part 6: Artificial Intelligence

ChatGPT Is Just The 'Tip Of The Iceberg' In Content-Creating Artificial Intelligence; Get Ready For 'A Lot Of Disruption'

Admin — Mon, 06 Feb 2023 05:00:00 GMT

Artificial intelligence-powered ChatGPT arrived in a firestorm nine weeks ago, wowing the tech world with its ability to do everything from composing a high school essay to debugging complex computer code. Investors quickly fastened on how the groundbreaking content-creation tool from startup OpenAI posed a threat to Google stock and the search giant's market share.

But ChatGPT is much more than just a peril to Google and its parent company Alphabet (GOOGL). And it's only one of many "generative AI" technologies that could roil a host of industries by creating text, images, video and computer programming code on their own.

The technology quickly captured the attention of tech giants. One was Microsoft (MSFT), which doubled down on its early investment in the company. Microsoft said Jan. 23 that it's making a "multiyear, multibillion-dollar investment" in OpenAI. It invested $1 billion in OpenAI in 2019.

OpenAI's Nov. 30 release of ChatGPT to the public marked the first time such a powerful AI tool was at the disposal of ordinary internet users. Yet generative AI technology already is finding applications in marketing, advertising, drug development, legal contracts, video gaming, customer support and digital art.

Disruption Seen Coming

Some Wall Street analysts compare the arrival of generative AI that creates content to Apple's (AAPL) launch of the iPhone in 2008.

"It feels like a moment where there's a lot of disruption coming," said Sridhar Ramaswamy, co-founder and chief executive of internet search startup Neeva and a former Google senior vice president. Ramaswamy oversaw Google's digital advertising business for five years.

ChatGPT uses AI to produce humanlike responses to search queries based on data scooped up from the internet. Ask it to solve a complex math question and it's done in seconds. You can get instant translation into English of text in a foreign language. Or give it an inventory of what's in your refrigerator and it can produce more than a dozen recipes from those contents. The list is endless.

And it's no wonder that within a few days of its release, ChatGPT had more than 1 million users.

ChatGPT Challenges

To be sure, the impact of artificial intelligence on human society and the business world has been overhyped before. Other potential buzz-killers include huge cloud computing costs associated with ChatGPT. OpenAI uses Microsoft's cloud computing arm to process search requests.

Further, ChatGPT is not updated in real time through internet connections. It has limited knowledge about things that occurred after 2021, when its training period ended. OpenAI also cautions that ChatGPT is not perfect, particularly for students planning to use it to cheat on schoolwork.

Notice from ChatGPT site telling users that it had reached full capacity and could not accommodate any more users.

Meanwhile, intellectual property issues arise since generative AI models scrape the internet for content they need to create new material. And generative AI technology could be used for nefarious purposes, say, "deepfakes" in video or helping computer hackers generate code. At the least, OpenAI concedes that ChatGPT sometimes generates incorrect or biased content.

Still, the frenzy over ChatGPT is real. Early users experimented with ChatGPT, asking it serious and not-so-serious questions, probing for strengths and weaknesses.

In its first month, ChatGPT scored nearly half the unique visitors that Microsoft's Bing search engine drew.

OpenAI's computing resources hit a wall following the explosion in usage. By mid-January, OpenAI said ChatGPT was "at capacity" and connections to its cloud system were at times unavailable.

Add up the pros and cons, though, and generative AI looks like it will make its mark. And Google stock, among others, is in its path.

Artificial Intelligence: Natural Language Models

"Internet search really has not been innovated on since the pay-per-click model that Google pioneered almost 20 years ago," Ramaswamy told Investor's Business Daily. "So the business model is ripe for disruption. I think we're on the threshold of massive changes in how search is going to operate and how AI is going to produce better experiences for users."

By mid-January, OpenAI said ChatGPT was "at capacity" and connections to its cloud system were at times unavailable. (Tada Images/Shutterstock)

While Neeva's search engine is ad-free, it charges a $5.99 monthly subscription for access. It's in the early stages of using generative AI.

On Google's fourth quarter earnings call, meanwhile, management touted its AI investments and made clear it'll respond to the huge popularity of ChatGPT.

"On the AI side, it is a really exciting time," Google Chief Executive Sundar Pichai said on the earnings call with analysts on Feb. 2. "Consumers are interested in trying out new experiences."

In a blog posted Feb. 6, Pichai disclosed that Google has made an experimental conversational AI service, called Bard, available to "trusted testers" ahead of making it more widely available to the public in the coming weeks.

Making AI Tools Available To Developers

OpenAI generates most revenue currently by making AI tools available to developers. The company has been testing ChatGPT Plus, a chatbot version for business users. In trials, OpenAI charges $20 monthly for ChatGPT Plus.

Key to the rise of generative AI are improved natural language processing models that help computers understand the way that humans write and speak. OpenAI is part of a wave of NLP startups that includes AI21 Labs, Anthropic, Cohere and others.

Google and Anthropic on Feb. 3 announced a partnership. As part of the deal, Google invested $300 million in Anthropic, said a report.

Large language models provide the building blocks to develop applications. These models process "prompts," such as an internet search query, that describe what a user wants to get.

Microsoft plans to integrate AI tools like ChatGPT into all of its products, including its Bing search engine.

AI Startups Leverage Generative AI

Microsoft has already integrated OpenAI's Dall-E2 tools into its graphics design software. Dall-E2 converts written text into pictures. OpenAI's generative AI could also improve email platform Outlook, presentation tools and office productivity tools such as Word.

While Neeva's search engine is ad-free, it charges a $5.99 monthly subscription for access. (Poetra.RH/Shutterstock)

Many technology companies are racing to integrate generative AI into products.

Austin, Texas-based Jasper, a marketing software company, pays OpenAI for access to GPT3, its older language model. Jasper's marketing department customers describe in natural language what they want Jasper to write.

Jasper's AI platform generates content for blog articles, social media posts, website copy and more. It recently raised $125 million at a price valuing the company at $1.5 billion.

Meanwhile, startup companies including Adept, Inflection AI, Character.AI and others aim to automate software tasks and integrate generative AI into third-party applications.

ChatGPT: Enterprise Software Transformed?

At Wolfe Research, analyst Alex Zukin expects a wide range of software companies such as HubSpot (HUBS), Intuit (INTU) and Paycom (PAYC) to build generative AI into their products.

One analyst says software giant Microsoft is betting the farm on OpenAI's artificial intelligence platform. (Ascannio/Shutterstock)

"I think that the rise of generative AI has the potential to be as impactful, if not way more impactful, to enterprise software as cloud computing and SaaS (software-as-a-service)," Zukin told IBD. "Microsoft is betting the farm on OpenAI, and we think this is definitely just the tip of the iceberg."

Meanwhile, Barclays sees upside for website developers such as GoDaddy (GDDY), Shopify (SHOP) and Wix (WIX).

Generative AI technology is finding applications in legal contracts, copywriting, video gaming, customer support, computer programming and digital art. Marketing departments are among the early adopters, says Thomas Davenport, a Babson College professor.

"For creative work there are going to be companies that adopt it in a transformative way," said Davenport, co-author of "All-in on AI: How Smart Companies Win Big With Artificial Intelligence."

He added, "A lot of companies are doing generative AI models. Some are open-sourced, while some are proprietary. It's changing very rapidly."

Venture Capital Jumps Into Generative AI

Some builders of large language models, such as Cohere and AI21 Labs, sell their own products in the enterprise market. Others make their models freely available to software developers.

Aside from OpenAI, another well-funded startup is Stability AI, the developer of open-source text-to-image generator Stable Diffusion.

Startups targeting fashion, e-commerce, drug design, personalized videos and computer vision include Veesual AI, Runway, Ordaos Bio, Paige.AI, Synthesis AI, Character.AI and Tavus.

Gartner forecasts that generative AI will be used to discover 30% of new drugs and industrial materials by 2025. (©AndSus — stock.adobe.com)

According to PitchBook, venture capital investment in generative AI jumped to $1.37 billion in 2022 — almost as much as was invested in all the previous five years combined.

Big Pharma Targets Drug Discovery

Research firm Gartner forecasts that generative AI will be used to discover 30% of new drugs and industrial materials by 2025, up from zero last year.

Gartner analyst Brian Burke told IBD that drug companies are using generative AI to design properties or functions of protein models that target diseases.

"Virtually all the large pharma and lots of small pharma startups are working on generative AI," he said. "It's been in development for a couple of years. Some drugs are in clinical trials now. It's going to be a major shift in the pharma industry."

How Did Your Online Broker Do In IBD's 2023 Best Online Broker Survey?

BioXcel Therapeutics (BTAI) is among biotech companies using AI in drug discovery.

Generative AI requires massive computing power — data-intensive capabilities such as machine learning and neural networks to analyze patterns in text, images, voice recordings and other digital sources. Neural networks are modeled on the human brain.

Machine learning systems analyze huge troves of data to train the software algorithms that underlie large language models. Deep-learning AI tools fine tune algorithms for specific tasks, such as generating realistic human text.

SambaNova Backers Include Intel, Google

A fourth-generation version of OpenAI's large language model, GPT-4, is expected to be released in 2023. OpenAI has stated that the foundational data of GPT-4 won't be much bigger than GPT-3, which was trained on 175 billion "parameters." But GPT-4 will use more computing power.

SambaNova, a well-funded startup chip company, is targeting the banking, energy and health care industries using AI. (SambaNova)

Meanwhile, chipmaker Nvidia (NVDA) provides software development tools to build artificial intelligence applications. Nvidia also has developed its own large language models. Earnings for NVDA stock are due Feb. 22.

In November, Nvidia agreed to partner with Microsoft to build a new cloud computing supercomputer to process AI workloads.

Other chipmakers also could get a boost from ChatGPT-type apps. Advanced Micro Devices (AMD), for one, is leaning into AI projects.

One Nvidia chip rival, well-funded startup SambaNova, is targeting the banking, energy and health care industries. Palo Alto-based SambaNova's investors include BlackRock, Intel's (INTC) Intel Capital unit and Alphabet venture arm GV, formerly Google Ventures. It builds pretrained, custom models using OpenAI's GPT-3.

SambaNova's biggest customers are U.S. government research labs. It's building an AI supercomputer with Hungary's OTP Bank.

"We are now as a world moving from thinking about what the possibilities are in using AI to productivity," Chief Executive Rodrigo Liang told IBD. "We are taking ChatGPT and these large language models and, in banking and other applications, turning them into useful things that save businesses money, that engage customers better."

Meanwhile, Babson professor Davenport says generative AI could finally help push AI technology into the mainstream in the enterprise market. Most companies use AI tools to spot patterns and make predictions for apps such as fraud detection and energy exploration.

ChatGPT: Existential Threat To Google Stock?

"Aggressive AI adoption has gone more slowly than some people may have thought," he said. "A lot of companies have experimented but (the number of) those having large numbers of deployed applications is still quite small. It's not easy."

Amid the sudden rise of the OpenAI/Microsoft partnership, the hot topic on Wall Street is what it means for Google stock. (Google)

Cloud computing companies such as Amazon Web Services, part of Amazon.com (AMZN), and Google sell AI analytical services to business customers. That makes it easier for companies to bootstrap AI applications if they do not have enough internal resources to train models on foundational data.

Cloud computing companies are anxious to leverage generative AI's potential. Google's cloud computing arm has partnered with Cohere to offer AI services. Meanwhile, stability AI and search engine startup Neeva both use Amazon Web Services.

Amid the sudden rise of the OpenAI/Microsoft partnership, the hot topic on Wall Street is what it means for Google stock. Google garners about 57% of its revenue from search products. Google controls 75% of the paid search market while Microsoft's Bing has 5%, said a Barclay's report.

ChatGPT Providing The Answer

ChatGPT poses a threat to Google stock for some queries, D.A. Davidson analyst Gil Luria said in a note.

Google-parent Alphabet uses artificial intelligence in its core business and long-range projects such as autonomous vehicle unit Waymo. (Waymo)

"Google helps you find an answer; ChatGPT provides the answer," the Google stock analyst said. As critics point out, though, ChatGPT sometimes provides wrong answers or biased results.

Meanwhile, Google-parent Alphabet uses artificial intelligence in its core business and long-range projects such as its autonomous vehicle unit Waymo. Google also uses AI to deliver web search results and relevant digital advertising.

In a blog, Google recently touted its AI innovations. "We expect a launch of new AI prototypes, and products could be showcased as early as the next major Google IO event in May 2023," said a Bank of America report.

Also, Google owns DeepMind Technologies, a U.K.-based AI research laboratory. Alphabet acquired DeepMind for $500 million in 2014.

According to Bank of America, DeepMind had about $1.05 billion in revenue in 2020 and turned profitable that year. DeepMind in 2021 spun off a new Alphabet venture called Isomorphic Laboratories, dedicated to drug discovery.

Will Google Risk Adding Chatbots To Search?

In addition, Google has developed generative AI tools called Language Model for Dialog Applications, or LaMDA, which use technology similar to OpenAI's. LaMDA is designed for dialogue while ChatGPT is text-based.

"On the AI side, it is a really exciting time," Google Chief Executive Sundar Pichai said on the company's fourth-quarter earnings call with analysts Thursday.

"I think we've been investing for a while, and it's clear that the market is ready," Pichai said on Google's Q4 earnings call with analysts. "I think I feel comfortable with all the investments we've made in making sure we can develop AI responsibly. And we'll be careful."

Google plans to make its own large language models, such as LaMDA, publicly available. Google says it will provide tools for software developers, content creators and partners that enable them to build artificial intelligence applications.

Pichai said consumer products, such as conversational AI enhancements to search, will be rolled out in the "coming weeks and months."

Heavy Investment From Google

"Google has invested heavily in machine learning and artificial intelligence," said Google stock analyst Mark Mahaney from Evercore ISI. "It has a ChatGPT application that is just as good if not better. I could be wrong — maybe this is the beginning of the end for Google, but I'm skeptical of that."

Some of the offerings on OpenAI's web site. (Davor Puklavec/Pixsell/Newscom)

Despite the buzz about OpenAI and Microsoft, Google stock has advanced nearly 20% in 2023, ahead of all the major stock averages.

According to one report, Google now plans to embed chatbot features into its search engine. But Google cannot launch flawed products that use generative AI, said a UBS report on ChatGPT.

"Google cannot afford to sully its search results with error prone, biased or toxic results that can occur with these models," UBS analyst Lloyd Walmsley said in the note.

"Search is Google's core product and moneymaker. Smaller players like Bing, DuckDuckGo and Neeva can take risks that Google would traditionally avoid," the Google stock analyst went on to say. "This puts Google at risk to decide between either losing query share or rushing a competing product to market that could reduce quality."

Another view is that putting chatbot features into its search products will lower Google's profit margins because of the computing required. Morgan Stanley analyst Brian Nowak estimated in a report that ChatGPT's cost per query is about seven times higher than Google's search costs.

Can OpenAI Generate Revenue, Or Just Content?

Still, Nowak expects generative AI to take off.

"We believe AI tools will be key to driving the next generation of consumer and enterprise apps and tools somewhat similar to the mobile ecosystem 10-plus years ago," he said in a report.

Still, OpenAI's economics could be a hurdle. Some analysts estimate OpenAI's annual costs to run ChatGPT on Microsoft's cloud computing infrastructure at $1 billion. "The compute costs are eye-watering," OpenAI Chief Executive Sam Altman once posted on Twitter.

With its $1 billion investment in 2019, Microsoft snatched OpenAI away from Google as a cloud customer. OpenAI agreed to use Microsoft's Azure instead. Microsoft recently issued weak guidance for its cloud computing unit.

Microsoft also gained exclusive rights to some underlying AI technology. Meanwhile, Microsoft's 2023 investment could give it a 49% stake in OpenAI, according to reports. Microsoft did not disclose the financial terms of the OpenAI partnership.

OpenAI aims to generate $200 million in revenue in 2023 and $1 billion in 2024.

Follow Reinhardt Krause on Twitter @reinhardtk_tech for updates on 5G wireless, artificial intelligence, cybersecurity and cloud computing.

The Generative AI Revolution is Creating The Next Phase Of Autonomous Enterprise

Admin — Wed, 01 Feb 2023 05:00:00 GMT

According to the Financial Times, investments in generative AI in 2022 exceeded $2 billion. OpenAI's valuation for a potential sale of some shares was set at an impressive $29 billion by the Wall Street Journal. Clearly, this indicates the enormity of interest from investors and corporations in generative AI technology. As the world continues to embrace technology and automation, businesses are beginning to explore the infinite possibilities of Generative AI. This type of Artificial Intelligence is on the cusp of creating autonomous, self-sustaining digital-only enterprises that can interact with humans without the active need for human interaction.

Generative AI is quickly becoming more widely adopted as enterprises are beginning to utilize it for a variety of tasks, including marketing, customer service, sales, learning and client relationships. This type of AI can create marketing content, generate pitch documents and product ideas and craft sophisticated advertising campaigns – all custom driven to help improve conversion rates and drive more revenue.

Generative AI companies are beginning to see massive success in venture capital, with many raising large sums of money and achieving high valuations. As per TechCrunch, Jasper, a copywriter assistant, recently raised $125 million at a $1.5 billion valuation, while Hugging Face raised $100 million at a $2 billion valuation, and Stability AI raised $101 million at a $1 billion valuation. In addition, Inflection AI raised $225 million at a post-money valuation of $1 billion according to TechCrunch. These successes can be compared to OpenAI, who in 2019, received more than $1 billion from Microsoft in funding with a $25 billion valuation.

Some major Generative AI platforms include DeepMind’s Alpha Code (GoogleLab), OpenAI’s ChatGPT, GPT-3.5, DALL-E, MidJourney, Jasper and Stable Diffusion.

The use cases for this technology are vast, from business and consumer applications to customer management systems, digital health, and automated software engineering.

As this type of AI technology continues to develop, the opportunities for autonomous enterprises are endless. The following sections will dive deeply into the Generative AI revolution and explore how large and small businesses can leverage this technology to build robust, self-sustaining enterprises.

Imagine a future where software engineering is no longer dependent on expensive human programmers. A future where intelligent assistants are the ones who take care of all the technical aspects related to software development. AI-powered automated software engineering is the future, and it will transform how businesses and individuals create digital solutions.

Generative AI is driving automated software engineering forward. GitHub’s CoPilot uses GPT-3 for code generation. It uses OpenAI's advanced machine learning models, such as GPT-3 and Codex, to streamline coding processes. Another startup that is pioneering in this space is Debuild. It allows users to design and deploy web applications by explaining what they want using their voice. It then automatically translates the instructions into code and deploys it for use. Debuild is open source, and its engine allows users to develop complex apps from just a few lines of commands.

Automated software engineering makes it easier and faster for companies to develop digital solutions. AI could generate unlimited engineering designs. It could generate test cases and create test automation. This reduces the costs associated with engineering development while also increasing its speed and effectiveness.

Content Generation and Management

The demand for new digital content has never been higher, and Generative AI technology is making it easier to meet that demand. Autonomous content generation enables businesses to create high-quality content quickly and efficiently using intelligent bots.

Services such as Omneky, Grammarly, DeepL and Hypotenuse are leading the way in this space:

Omneky: Omneky harnesses the power of cutting-edge deep learning to customize advertising creative across all digital platforms. With its AI algorithms, Omneky examines what design and messaging are engaging with potential buyers and creates ads with a higher probability of increasing sales.
Grammarly: Grammarly’s AI-powered writing assistant helps people create better content faster. From basic grammar and spelling corrections to stylistic advice, Grammarly enables anyone to write confidently.
DeepL:DeepL is a natural language processing platform that uses deep learning algorithms to generate optimized copy for any project. With its unique language understanding capabilities, DeepL can quickly and accurately generate content from any source.
Hypotenuse:Hypotenuse is a platform that automates the process of creating product descriptions, blog articles, and advertising captions. Its AI-driven algorithms allow businesses to create high-quality content in a fraction of the time it would typically take to write manually.

There are several use cases for autonomous content generation. This includes creating better-performing digital ads, producing optimized copy for websites and apps, and creating content quickly for marketing pitches.

Digital Ads:Autonomous content generation can be used to create better-performing digital ads. Businesses can use AI algorithms to optimize their ad creative and messaging to engage with potential customers more effectively.
Copy Optimization:AI-driven copy optimization helps businesses create optimized copy tailored to their readers' needs. This ensures that websites are engaging and easy to read and that mobile applications are intuitive.
Content Research: Research is essential to content marketing, and autonomous content generation can help businesses reduce research time. AI algorithms can quickly analyze large amounts of data and generate insights to inform content strategies.
Marketing Pitches: AI can understand the context and generate content quickly, making it ideal for creating marketing pitches. Utilizing AI-driven algorithms, businesses can create persuasive copy and targeted messaging in a fraction of the time it would take to do manually.

By leveraging Generative AI technology, businesses can create high-quality content more efficiently and faster than ever. This can also augment human creativity by providing an initial template from which content authors can draw inspiration. An autonomous content generation is a powerful tool for any business, whether you’re looking to create digital ads or produce optimized copy for your website.

Marketing and Customer Experience

Customers want a personalized and tailored experience, which is why businesses are increasingly leveraging AI in marketing and customer experience. Autonomous content generation can be used for marketing campaigns, copywriting, true personalization, assessing user insights, and creating high-quality user content quickly. This can include blog articles, add captions, product descriptions, and more.

Here are some of the innovative AI-powered startups that are changing the way businesses market and engage with customers:

Kore.ai uses Generative AI models to create contextual content tailored to the needs of its customers. This simplifies virtual assistant development and ensures that customer experiences are optimized.
Copy.ai is an AI-powered digital advertising and marketing company that helps businesses save time and increase conversion rates.
Jasper is an AI platform that generates marketing materials, making it easier and faster for businesses to promote their products and services.
Andiis a conversational search engine with an AI helper that can accurately answer complex questions. It provides customers with the most helpful links while summarizing complex topics.

Mongoose Media, a web design and digital marketing agency, uses Jasper AI to scale web traffic. By leveraging the Jasper program, Mongoose experienced a remarkable 166% rise in web traffic within two months and saved 240 hours of blog post writing while working 400% faster. Lauren Petrullo, Founder of Mongoose, stated, "We’re writing anywhere from 1,500 to 3,000 words in less than 2 hours. That same process used to take 8-10 hours.”

Healthcare

Developing new drugs and medical instruments often requires years of clinical trials at substantial cost. But what if generative AI could be used to speed up the process? Artificial intelligence has revolutionized the way physicians diagnose patients, making screening more effortless than ever before. Further, AI can use information like vocal tone, posture and facial expressions to generate accurate assessments - adding another layer of accuracy to patient evaluations. By leveraging this powerful technology in drug development processes, we can drastically reduce the time-to-market for treatments and associated costs.

In healthcare, Generative AI is being used to accelerate the mini protein drug discovery and development process, create more accurate cancer diagnosis algorithms, develop novel deep learning algorithms for diagnostically challenging tasks, and even assist with day-to-day medical tasks. Companies such as Ansible Health, Ordaos Bio, Standigm, and Paige AI are already leveraging Generative AI to revolutionize the healthcare industry.

Mini protein drug discovery and development:

Generative AI is being used to accelerate the process of mini protein drug discovery and development. Companies such as Ordaos Bio use their proprietary AI engine to uncover critical patterns in drug discovery, helping researchers come up with a successful result.

Cancer diagnostics:

AI-driven algorithms are also used to develop more accurate models for cancer diagnostics. Paige AI has developed generative models to assist with cancer diagnostics, creating more accurate algorithms and increasing the accuracy of diagnosis.

Diagnostically challenging tasks:

Generative AI technology is also used to develop novel deep-learning algorithms for diagnostically challenging tasks. Ansible Health utilizes its ChatGPT program for functions that would otherwise be difficult for humans.

Day-to-day medical tasks:

Generative AI is even used for day-to-day medical tasks such as wellness checks and general practitioner tasks. AI technology can also include additional data such as vocal tone, body language, and facial expressions to determine a patient’s condition. This can lead to quicker and more accurate diagnoses for medical professionals.

Antibody therapeutics:

Moreover, Absci Corporation (ABSI) is using machine learning to transform the field of antibody therapeutics. Their Integrated Drug Creation platform uses AI to predict antibodies’ specificity, structure, and binding energy. This allows for a faster and more efficient development process for therapeutic antibodies.

Product Design and Development

What if AI could help with product innovation? Generative AI is revolutionizing how products are innovated, developed, and manufactured. It can generate new ideas that would be too complex for humans to create. Automated data analysis approaches can help identify trends in customer behavior and preferences to inform product design. It could improve the speed and accuracy of product research and development.

Generative AI technology can also help with product engineering by allowing teams to simulate products in virtual environments. This allows for complex problems to be solved more quickly and efficiently, leading to improved design accuracy. Companies such as UIzard, Ideeza and Neural Concept are at the forefront of this revolution, providing platforms to help optimize product engineering.

UIzard is a collaborative design and prototyping tool powered by machine learning and computer vision. It allows teams to create interactive user interfaces with minimal effort quickly.
Ideeza is an AI-powered platform that helps identify optimal therapeutic antibodies for drug development.
Neural Concept provides deep learning algorithms for enhanced engineering, helping companies speed up their R&D cycles and optimize product performance.

Generative AI benefits G7 Economies

Generative AI has the potential to benefit significantly G7 countries, enabling them to to remain competitive with emerging offshore/outsourcing talent destinations in BRICS (Brazil, Russia, India, China, and South Africa) countries. It could reduce the need for manual labor and skilled talent in certain areas, allowing companies to focus on innovation and creativity instead. Moreover, it can also help optimize production processes by identifying trends and potential improvements.

Generative AI can also help bridge the gap between human creativity and technological innovation. Companies can maximize potential top results, quality assurance, and customer satisfaction by combining creative mindsets with powerful algorithms. With the advent of Generative AI technology, G7 countries have an opportunity to remain competitive in a fragmented world.

The Way Forward

We will soon be living in an era where generative AI is the fuel that powers autonomous enterprises. Companies are already leveraging the power of generative AI to deploy, maintain, and monitor complex systems with minimal manual effort. Generative AI allows companies to make decisions faster, take risks more effectively, and respond quickly to changing customer needs.

As we continue to explore the potential of generative AI, its applications will become an integral part of our lives. We expect to see it used in medicine, product R&D and engineering, finance, logistics, and transportation. Companies will reap the rewards of increased innovation, efficiency, speed and accuracy thanks to this powerful technology. The generative AI revolution is here and it’s only just beginning.