-
ATOMICAS AI SOLUTIONS
- Hyderabad, India
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21:27
(UTC +05:30) - sbvs.me
- @suneel_bvs
- https://orcid.org/0000-0001-6782-6446
- https://scholar.google.co.in/citations?user=DlY5NQ4AAAAJ&hl=en
Highlights
- Pro
Starred repositories
MDANCE: O(N) clustering for molecular dynamics. Process 1.5M frames in 40min. 8 specialized algorithms.
Implementation of DiffDock-PP: Rigid Protein-Protein Docking with Diffusion Models in PyTorch (ICLR 2023 - MLDD Workshop)
The topology of molecular machine learning representations
12 Lessons to Get Started Building AI Agents
A quantitative benchmark and analysis of molecular large language models.
🦉 OWL: Optimized Workforce Learning for General Multi-Agent Assistance in Real-World Task Automation
No fortress, purely open ground. OpenManus is Coming.
[ICLR 2024] Protein-Ligand Interaction Prior for Binding-aware 3D Molecule Diffusion Models
Comprehensive benchmarking of protein-ligand structure prediction methods. (ICML 2024 AI4Science)
Official repository for the Boltz biomolecular interaction models
A website displaying hundreds of charts made with Python
A collection of open source, actively maintained web apps for LLM applications
Browse SDFiles for unsanitizable molecules for manual intervention and fix
Robust Molecular Structure Recognition with Image-to-Graph Generation
Protein Ligand INteraction Dataset and Evaluation Resource
Toolkit for open antiviral drug discovery by the ASAP Discovery Consortium
Python-centric Cookiecutter for Molecular Computational Chemistry Packages
RDKit related blog posts, notebooks, and data.
Scientific Computing for Chemists with Python is a free book for teaching basic coding skills to chemists using Python, Jupyter notebooks, and the other Python software. This textbook teaches a var…
Scripts associated with the paper "Characterizing Uncertainty in Machine Learning for Chemistry". They show how the data for this paper were calculated.
Papers about Structure-based Drug Design (SBDD)
13th RDKit UGM. 11-13 September in Zurich, Switzerland
Augmenting a training dataset of the generative diffusion model for molecular docking with artificial binding pockets
Ray is an AI compute engine. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.
Free and open-source application (command line and GUI) providing QSAR models predictions as well as applicability domain and accuracy assessment for physicochemical properties, environmental fate …