Authors - Harshwardhan Singh Rathore, Dev Krishan, Amit, Abhinav Vyas, Harshit Choudhary, Kunal Chittora, Vishal Shrivastava, Ram Babu Buri, Akhil Pandey, Mukesh Mishra Abstract - Predicting protein–ligand binding affinity is an essential step in early drug discovery. We present Alchemy, a ligand-centric Graph Neural Network (GNN) framework for predicting binding affinities (pKd/pKi) from molecular graphs and a production-ready web interface for easy inference. Using a curated subset of the PDBbind dataset for prototyping and RDKit for cheminformatics preprocessing [6], we implement a message-passing GCN model with global pooling and train it using MSE regression. We evaluate model performance using RMSE, MAE, Pearson and Spearman correlations, and Concordance Index, and compare against docking scores and classical ML baselines. On the demo subset our model achieves an RMSE of X (±Y) and Pearson r of Z (±W) — results that highlight the potential and limitations of ligand-only approaches. We discuss data-scaling, protein incorporation strategies, ablation studies, and provide reproducible code and a web app to facilitate adoption.
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