HydraScreen: A Generalizable Structure-Based Deep Learning Approach to Drug Discovery

09 October 2023, Version 2
This content is a preprint and has not undergone peer review at the time of posting.


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 design an end-to-end pipeline for high-throughput screening and lead optimization, targeting applications in structure-based drug design. We assess 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 introduce a novel approach for interaction profiling, aimed at detecting potential biases within both the model and datasets. This approach not only enhances interpretability but also reinforces the impartiality of our methodology. Finally, we demonstrate 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 https://hydrascreen.ro5.ai, provides a user-friendly GUI and a public API, facilitating easy-access assessment of protein–ligand complexes.


Strucure-based drug discovery
Machine Learning Scoring Function
Deep Learning
Pose Estimation
Affinity Prediction
Computational Chemistry
Virtual Screening

Supplementary materials

Supporting Information for “HydraScreen: A Generalizable Structure-Based Deep Learning Approach to Drug Discovery"
Supporting information for “HydraScreen: A Generalizable Structure-Based Deep Learning Approach to Drug Discovery"

Supplementary weblinks


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