Repository Index

Interpretable bilinear attention network with domain adaptation improves drug-target prediction.

Rails frontend to The Genome Institute's drug gene interaction database.

TypeDB Bio: Biomedical Knowledge Graph

• bioinformatics
• knowledge-graph
• knowledge-base
• drug-discovery

NeoDTI: Neural integration of neighbor information from a heterogeneous network for discovering new drug-target interactions

• computational-biology
• bioinformatics
• graph-convolution
• deep-learning

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

CancerFoundation: A single-cell RNA sequencing foundation model to decipher drug resistance in cancer

• cancer
• foundation-model
• single-cell

PyTorch implementation of "TGSA: Protein-Protein Association-Based Twin Graph Neural Networks for Drug Response Prediction with Similarity Augmentation"

UV-first benchmark for protein-ligand docking with reproducible annotation, pocket similarity, and HPC workflows.

• benchmark
• bioinformatics
• cheminformatics
• computational-chemistry

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

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

Interpretable modeling of cellular responses to drug perturbations using cycle consistence learning

DeepAEG: A model for predicting cancer drug response based on data enhancement and edge-collaborative update strategies

Interpretable drug response prediction with graph neural networks over drugs, cell lines, and genes.

GNN-based gene-selection deep learning model for drug response prediction