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
DeepVariant is an analysis pipeline that uses a deep neural network to call genetic variants from next-generation DNA sequencing data.
NeuroKit2: The Python Toolbox for Neurophysiological Signal Processing
Deep probabilistic analysis of single-cell and spatial omics data
Validated, scalable, community developed variant calling, RNA-seq and small RNA analysis
Official release of the ProGen models
Generation of protein sequences and evolutionary alignments via discrete diffusion models
Spatial Single Cell Analysis in Python
Sequential regulatory activity predictions with deep convolutional neural networks.
Schema and generated objects for biolink data model and upper ontology
Bi-Directional Equivariant Long-Range DNA Sequence Modeling
Ankh: Optimized Protein Language Model
A Python package for the identification, characterization and comparison of spatial clusters from spatial -omics data.
Learning cell communication from spatial graphs of cells
CASSIA: A Multi-Agent LLM-Based Single-Cell Cell Type Annotation Framework
Toolbox for the analysis of smFISH images.
Run scripts, explore your genome, and get personalized explanations — all in natural dialogue.
Adaptive Graph Attention Auto-encoder for Spatial Domain Identification of Spatial Transcriptomics
MCP server for spatial transcriptomics analysis through natural language interfaces.
A versatile single-cell cross-modality translation method via dual-aligned variational autoencoders
Fast, deterministic cell-type deconvolution for million-bin spatial transcriptomics
PyTorch implementation of "TGSA: Protein-Protein Association-Based Twin Graph Neural Networks for Drug Response Prediction with Similarity Augmentation"