Ultra-large library screening with an evolutionary algorithm in Rosetta (REvoLd)

TL;DR

This paper introduces REvoLd, an evolutionary algorithm integrated with Rosetta, that efficiently explores ultra-large make-on-demand chemical libraries for drug discovery, significantly improving hit rates over random screening.

Contribution

The paper presents REvoLd, a novel evolutionary algorithm that efficiently searches vast chemical spaces by leveraging library construction features, outperforming traditional methods in hit rate enhancement.

Findings

REvoLd achieved 869-1622 times higher hit rates than random selection.

The algorithm effectively explores billions of compounds with full ligand and receptor flexibility.

REvoLd is integrated into the Rosetta suite, facilitating widespread use in drug discovery.

Abstract

Ultra-large make-on-demand compound libraries now contain billions of readily available compounds. This represents a golden opportunity for in-silico drug discovery. One challenge, however, is the time and computational cost of an exhaustive screen of such large libraries when receptor flexibility is taken into account. We propose an evolutionary algorithm to search combinatorial make-on-demand chemical space efficiently without enumerating all molecules. We exploit the feature of make-on-demand compound libraries, namely that they are constructed from lists of substrates and chemical reactions. Our algorithm RosettaEvolutionaryLigand (REvoLd) explores the vast search space of combinatorial libraries for protein-ligand docking with full ligand and receptor flexibility through RosettaLigand. A benchmark of REvoLd on five drug targets showed improvements in hit rates by factors between 869 and 1622 compared to random selections. REvoLd is available as an application within the Rosetta software suite (https://docs.rosettacommons.org/docs/latest/revold). This work formulates an evolutionary algorithm for optimization and exploration of ultra-large make-on-demand libraries. We demonstrate that our approach results in strong and stable enrichment, offering the most efficient algorithm for drug discovery in ultra-large chemical space to date.