Democratizing Deep-Learning for Drug Discovery, Quantum Chemistry, Materials Science and Biology
- drug-discovery
- quantum-chemistry
- deep-learning
- biology
47,133 repositories indexed
1–24 of 111 · Most stars
Democratizing Deep-Learning for Drug Discovery, Quantum Chemistry, Materials Science and Biology
Official git repository for Biopython (originally converted from CVS)
Trainable, memory-efficient, and GPU-friendly PyTorch reproduction of AlphaFold 2
This package contains deep learning models and related scripts for RoseTTAFold
Deep probabilistic analysis of single-cell and spatial omics data
The repository host gives no description.
Biological foundation modeling from molecular to genome scale
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.
A Deep Learning Toolkit for DTI, Drug Property, PPI, DDI, Protein Function Prediction (Bioinformatics)
🦖 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
Cloud-native genomic dataframes and batch computing
ADAM is a genomics analysis platform with specialized file formats built using Apache Avro, Apache Spark, and Apache Parquet. Apache 2 licensed.
Validated, scalable, community developed variant calling, RNA-seq and small RNA analysis
C library for high-throughput sequencing data formats
Foundation Models for Genomics & Transcriptomics
:christmas_tree:Visualization and annotation of phylogenetic trees
Precision Medicine Knowledge Graph (PrimeKG)
Python package for graph neural networks in chemistry and biology
Official implementation for HyenaDNA, a long-range genomic foundation model built with Hyena
In-memory nucleotide sequence k-mer counting, filtering, graph traversal and more
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.