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
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Deep probabilistic analysis of single-cell and spatial omics data
Biological foundation modeling from molecular to genome scale
Scientific workflow engine designed for simplicity & scalability. Trivially transition between one off use cases to massive scale production environments
Cloud-native genomic dataframes and batch computing
:christmas_tree:Visualization and annotation of phylogenetic trees
Python package for graph neural networks in chemistry and biology
Official implementation for HyenaDNA, a long-range genomic foundation model built with Hyena
DNABERT: pre-trained Bidirectional Encoder Representations from Transformers model for DNA-language in genome
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.
Official release of the ProGen models
Generation of protein sequences and evolutionary alignments via discrete diffusion models
C++ library and cmdline tools for parsing and manipulating VCF files with python and zig bindings
Generative biopolymer design with rich conditioning.
[ICLR 2024] DNABERT-2: Efficient Foundation Model and Benchmark for Multi-Species Genome
SCENIC is an R package to infer Gene Regulatory Networks and cell types from single-cell RNA-seq data.
Official repository for the ProteinGym benchmarks
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.
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GEARS is a geometric deep learning model that predicts outcomes of novel multi-gene perturbations
Reference mapping for single-cell genomics