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05:44
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- https://orcid.org/0000-0001-7786-2109
- @br_jimenez
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Contains the digitized chapters of the book "System Design with Ada"
AutoML system for building trustworthy peptide bioactivity predictors
Sidechain conditioning and modeling for full-atom protein sequence design
Pen and paper exercises in machine learning
It's a package for evaluation of predicted poses, right?
List of papers about Proteins Design using Deep Learning
Protein Ligand INteraction Dataset and Evaluation Resource
PINDER: The Protein INteraction Dataset and Evaluation Resource
Creating a diffusion model from scratch in PyTorch to learn exactly how they work.
Protein structure diffusion model for unconditional protein generation and motif scaffolding
A Rust library for calculating the SASA for a protein. 5x faster than Freesasa. Based on the Shrake & Rupley algorithm.
[ACL 2024] ProtLLM: An Interleaved Protein-Language LLM with Protein-as-Word Pre-Training
Developing classification models for DNA-Binding proteins through machine learning and large language models
Multi-target de novo molecular generator conditioned on AlphaFold's latent protein embeddings.
Learning Joint 2D & 3D Diffusion Models for Complete Molecule Generation
Deep Generative Models: Diffusion Models for Molecule Generation
Code and Pre-Trained Models for "AttnPacker: An end-to-end deep learning method for protein side-chain packing"
oxpig / AbLang2
Forked from TobiasHeOl/AbLang2An antibody-specific language model focusing on NGL prediction
Implementation code for the paper "Aggregating Residue-Level Protein Language Model Embeddings with Optimal Transport"
Transformer-based model for chemical reactions
Structure-conditioned masked language modeling for protein sequence design
FlexPose, a framework for AI-based flexible modeling of protein-ligand binding pose.
Sandbox for Deep-Learning based Computational Protein Design
A rust crate to interface with the freesasa library for protein surface area calculations
De novo peptide sequencing with InstaNovo: Accurate, database-free peptide identification for large scale proteomics experiments