Democratizing Deep-Learning for Drug Discovery, Quantum Chemistry, Materials Science and Biology
- drug-discovery
- quantum-chemistry
- deep-learning
- biology
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Democratizing Deep-Learning for Drug Discovery, Quantum Chemistry, Materials Science and Biology
Official git repository for Biopython (originally converted from CVS)
Official repository for the Boltz biomolecular interaction models
Trainable, memory-efficient, and GPU-friendly PyTorch reproduction of AlphaFold 2
RNA-seq aligner
Chai-1, SOTA model for biomolecular structure prediction
Deep probabilistic analysis of single-cell and spatial omics data
ProtTrans is providing state of the art pretrained language models for proteins. ProtTrans was trained on thousands of GPUs from Summit and hundreds of Google TPUs using Transformers Models.
🦖 ClawBio - The first bioinformatics-native AI agent skill library. Local-first. Reproducible. Open. Free.
Scientific workflow engine designed for simplicity & scalability. Trivially transition between one off use cases to massive scale production environments
Foundation Models for Genomics & Transcriptomics
:christmas_tree:Visualization and annotation of phylogenetic trees
Python package for graph neural networks in chemistry and biology
Tasks Assessing Protein Embeddings (TAPE), a set of five biologically relevant semi-supervised learning tasks spread across different domains of protein biology.
A Go package for engineering organisms.
Fast, sensitive and accurate integration of single-cell data with Harmony
Lightweight, super fast C/C++ (& Python) library for sequence alignment using edit (Levenshtein) distance.
A tool for semi-automatic cell type classification
A Practical and Efficient NCBI Taxonomy Toolkit, also supports creating NCBI-style taxdump files for custom taxonomies like GTDB/ICTV
A dependency-free cross-platform swiss army knife for PDB files.
GEARS is a geometric deep learning model that predicts outcomes of novel multi-gene perturbations
Genomic Pretrained Network - GPN, GPN-MSA, PhyloGPN, GPN-Star
UCE is a zero-shot foundation model for single-cell gene expression data
Native R and Python sample-deletion robustness for omics: candidate certificates, minimum deletion bounds, Bioconductor model refits and donor-level pseudobulk