Moldrug algorithm for an automated ligand binding site exploration by 3D aware molecular enumerations

Abstract

We present Moldrug, a computational tool for accelerating the hit-to-lead phase in structure-based drug design. Moldrug explores the chemical space using structural modifications suggested by the CReM library and by optimizing an adaptable fitness function with a genetic algorithm. Moldrug is complemented by Moldrug-Dashboard, a cross-platform and user-friendly graphical interface tailored for the analysis of Moldrug simulations. To illustrate Moldrug, we designed new potential inhibitors targeting the main protease (M^Pro) of SARS-CoV-2 by optimizing a consensus fitness function that balances binding affinity, drug-likeness, and synthetic accessibility. The designed molecules exhibited high chemical diversity. A subset of the designed molecules were ranked using MM/GBSA and alchemical binding free energy calculations, revealing predicted affinities as low as $-10\text{kcal}{\text{mol}}^{-1}$ . Moldrug is distributed as a Python package under the Apache 2.0 license. It offers pre-configured multi-parameter fitness functions for molecular design, while being highly adaptable for integrating functionalities from external software. Documentation and tutorials are available at https://moldrug.rtfd.io .

Keywords: Binding free energy calculations; Chemical space exploration; Docking and scoring; Fragment-based drug design; Ligand optimization; Molecular dynamics simulations; Open-source drug design tools; SARS-CoV-2 inhibitors; Structure-based drug design.