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Official repository for the Boltz biomolecular interaction models

  • binding-prediction
  • drug-discovery
  • protein-structure

Tasks Assessing Protein Embeddings (TAPE), a set of five biologically relevant semi-supervised learning tasks spread across different domains of protein biology.

  • deep-learning
  • protein-sequences
  • protein-structure
  • semi-supervised-learning

Generation of protein sequences and evolutionary alignments via discrete diffusion models

  • discrete-diffusion
  • generative-model
  • multiple-sequence-alignment
  • protein-sequences

A Python Package for Protein Dynamics Analysis

  • python
  • protein
  • dynamics

TransformerCPI: Improving compound–protein interaction prediction by sequence-based deep learning with self-attention mechanism and label reversal experiments(BIOINFORMATICS 2020) https://doi.org/10.1093/bioinformatics/btaa524

  • self-attention
  • compound-protein-interaction
  • transformercpi
  • structure-free-virtual-sreening

Multi-stage Riemannian flow matching for physically valid molecular docking, with GNINA scoring, PoseBusters filtering, CLI inference, and benchmarks.

  • alphafold3
  • docking
  • drug-design
  • drug-discovery

Generative models for transcriptomic-driven or protein-driven molecular design (PaccMann^RL).

  • deep-reinforcement-learning
  • generative-model
  • vae
  • molecule-generation