@inproceedings{
crivelli-decker2023machine,
title={Machine Learning Guided {AQFEP}: A Fast \& Efficient Absolute Free Energy Perturbation Solution for Virtual Screening},
author={Jordan Crivelli-Decker and Zane Beckwith and Gary Tom and Ly Le and Sheenam Khuttan and Romelia Salomon-Ferrer and Jackson Beall and Andrea Bortolato},
booktitle={NeurIPS 2023 Workshop on New Frontiers of AI for Drug Discovery and Development},
year={2023},
url={https://openreview.net/forum?id=NYbnVDuKuC}
}
Structure-based methods in drug discovery have become an integral part of the modern drug discovery process. The power of virtual screening lies in its ability to rapidly and cost-effectively explore enormous chemical spaces to select promising ligands for further experimental investigation. Relative Free Energy Perturbation (RFEP) and similar methods are the gold standard for binding affinity prediction in drug discovery hit-to-lead and lead optimization phases, but have high computational cost and the requirement of a structural analog with a known activity. Without a reference molecule requirement, Absolute FEP (AFEP) has, in theory, better accuracy for hit ID, but in practice, the slow throughput is not compatible with VS, where fast docking and unreliable scoring functions are still the standard. Here, we present an integrated workflow to virtually screen large and diverse chemical libraries efficiently, combining active learning with a physics-based scoring function based on a fast absolute free energy perturbation method. We validated the performance of the approach in the ranking of structurally related ligands, virtual screening hit rate enrichment, and active learning chemical space exploration; disclosing the largest reported collection of free energy simulations to date.
Machine Learning Guided AQFEP: A Fast     Efficient Absolute Free Energy Perturbation Solution for Virtual Screening
Jordan Crivelli-Decker Zane Beckwith Gary Tom Ly Le Sheenam Khuttan Romelia Salomon-Ferrer Jackson Beall Andrea Bortolato