HydraScreen: A Generalizable Structure-Based Deep Learning Approach to
  Drug Discovery
Alvaro Prat Hisham Abdel Aty Gintautas Kamuntavičius Tanya Paquet Povilas Norvaišas Piero Gasparotto Roy Tal
We propose HydraScreen, a deep-learning approach that aims to provide a
framework for more robust machine-learning-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).
Furthermore, we utilize a novel interaction profiling approach to identify
potential biases in the model and dataset to boost interpretability and support
the unbiased nature of our method. Finally, we showcase HydraScreen's capacity
to generalize across unseen proteins and ligands, offering directions for
future development of robust machine learning scoring functions. HydraScreen
(accessible at https://hydrascreen.ro5.ai) provides a user-friendly GUI and a
public API, facilitating easy assessment of individual protein-ligand
complexes.