Codebase and CLI for PLAPT: A state-of-the-art protein-ligand binding affinity model for drug discovery
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Updated
Mar 27, 2025 - Mathematica
Codebase and CLI for PLAPT: A state-of-the-art protein-ligand binding affinity model for drug discovery
GPU-accelerated protein-ligand docking with automated pocket detection, exploring through multi-pocket conditioning. Official Implementation of PocketVina
[ICML 2024] Interaction-based Retrieval-augmented Diffusion Models for Protein-specific 3D Molecule Generation
LINKER: Learning Interactions Between Functional Groups and Residues With Chemical Knowledge-Enhanced Reasoning and Explainability
Binding affinity prediction for drug discovery
ENS-Score is machine learning-based scoring function, which applies a probabilistic approach to estimate protein-ligand binding affinity.
R shiny app to analyse microscale thermophoresis (MST) data
Winner for Kaggle competition "Predicting Protein-Ligand Interactions"
🚀 Install the Affinity Universal app on Linux effortlessly with Affinity CLI, while easily managing Wine environments and installation profiles.
Deep-ProLiPrint: A cutting-edge deep learning framework that generates compact, information-rich fingerprints from protein-ligand complexes, augmenting machine learning-driven drug discovery and molecular design.
A graphical user interface (GUI) and web application to facilitate the usage of ENS-Score.
A comparative and ablation study exploring different models for protein-ligand binding affinity prediction. The model categories studied in this project include traditional machine learning, graph neural networks, and structural deep learning models.
Ranking of all the ligands in molecular docking by their AutoDock Vina scores, an approximation to their free energy of binding to the protein.
MCP_PYKINGENIE is a MCP server that provides tools for the analysis of surface-based binding kinetics data.
Deep neural network for protein-ligand docking and scoring with feature extraction, prediction, and benchmark results.
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