PharmacoNet

Active-learning framework using QSAR models to predict docking scores, enabling 50-100x acceleration of large-library virtual screening.

Classical rigid receptor, flexible ligand docking using empirical and knowledge-based scoring. The most widely used open-source docking tool.

GPU-accelerated molecular docking achieving >2000x speedup over CPU Vina. Enables ultra-large virtual screening of billions of compounds.

Diffusion-based generative model that treats docking as a generative problem over ligand poses. No pre-specified binding pocket needed.

AutoDock Vina-based docking engine augmented with a 3D CNN scoring function. Uses Vina for sampling, CNN for scoring and re-ranking.

Geometric flow matching model that maps apo protein structures to bound complexes for multiple ligands simultaneously. Outputs confidence scores and affinity estimates.