HydraScreen: A Generalizable Structure-Based Deep Learning Approach to Drug Discovery
Alvaro Prat, Hisham Abdel Aty, Orestis Bastas, Gintautas Kamuntavičius, Tanya Paquet, Povilas Norvaišas, Piero Gasparotto, Roy Tal
Abstract
We propose HydraScreen, a deep-learning framework for safe and robust accelerated drug discovery. HydraScreen utilizes a state-of-the-art 3D convolutional neural network designed for the effective representation of molecular structures and interactions in protein–ligand binding. We designed an end-to-end pipeline for high-throughput screening and lead optimization, targeting applications in structure-based drug design. We assessed our approach using established public benchmarks based on the CASF-2016 core set, achieving top-tier results in affinity and pose prediction (Pearson’s r = 0.86, RMSE = 1.15, Top-1 = 0.95). We introduced a novel approach for interaction profiling, aimed at detecting potential biases within both the model and data sets. This approach not only enhanced interpretability but also reinforced the impartiality of our methodology. Finally, we demonstrated HydraScreen’s ability to generalize effectively across novel proteins and ligands through a temporal split. We also provide insights into potential avenues for future development aimed at enhancing the robustness of machine learning scoring functions. HydraScreen (accessible at http://hydrascreen.ro5.ai/paper ) provides a user-friendly GUI and a public API, facilitating the easy-access assessment of protein–ligand complexes.