Development and large-scale benchmarks of a protein--ligand absolute binding free energy toolkit

TL;DR

Felis is an open-source toolkit that enables high-throughput, accurate protein-ligand binding free energy calculations, demonstrating robust performance across diverse datasets without custom modifications.

Contribution

The paper introduces Felis, a scalable, automated ABFE toolkit paired with ByteFF, achieving competitive ranking performance without force-field tuning.

Findings

Felis achieves ranking performance comparable to state-of-the-art RBFE methods.

Felis demonstrates robust convergence on challenging charged ligand datasets.

All predictions were generated in a zero-shot manner without custom modifications.

Abstract

Absolute binding free energy (ABFE) calculations offer a theoretically rigorous approach for predicting protein--ligand binding affinities without the scaffold constraints of relative binding free energy (RBFE) perturbations. However, broad adoption of ABFE in high-throughput hit discovery campaigns has been hindered by high computational costs and a lack of large-scale validation. Here, we present Felis, an open-source, automated, and scalable toolkit designed for high-throughput ABFE calculations. Paired with ByteFF, a previously developed data-driven molecular mechanics force field for drug-like molecules, Felis achieves ranking performance comparable to state-of-the-art RBFE methods on a diverse dataset comprising 43 protein targets and 859 ligands. Furthermore, we demonstrate robust convergence and ranking performance of Felis on a more challenging KRAS(G12D) dataset, where some ligands and the cofactor are highly charged. Crucially, all Felis predictions in this study were generated in a strict zero-shot manner, eschewing custom force-field modifications and alchemical schedule fine-tuning. This demonstrates the viability of Felis as an effective, ready-to-use tool for computational structure-based drug design.