Barsainyan, A. A.; Panda, R.; Siguenza, J.; Merico, D.; Ramsundar, B.

The problem of identifying which protein target a potential drug-like molecule interacts with is crucial for both the study of existing drugs and the design of new therapeutic compounds. Despite the importance of target identification, existing computational approaches remain limited in terms of speed, accuracy, and protein target coverage. We introduce ProteomeScan, a large-scale, gene-driven computational toolkit for systematic proteome-wide scanning to uncover hidden or previously uncharacterized protein-ligand interactions. ProteomeScan leverages cloud-scale high performance computing to perform extensive molecular docking simulations across the human proteome to rank candidate targets based on binding affinities. After filtering promiscuous targets, we found that ProteomeScan ranks known target significantly better than a random baseline for a set of control compounds. Furthermore, we performed physical analyses of predicted binding modes for both promiscuous and known protein-ligand binding pairs to validate that ProteomeScan identifies interactions with valid binding pockets. In addition, we conducted experiments using mutant variants of proteins to study how mutations affect binding behavior. We have open sourced the core ProteomeScan algorithm as part of the DeepChem ecosystem to enhance transparency and reproducibility.