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 (MPro) 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 kcal/mol. 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.
Cite
@article{leon2025moldrug,
title = {Moldrug algorithm for an automated ligand binding site exploration by {3D} aware molecular enumerations},
volume = {17},
issn = {1758-2946},
url = {https://doi.org/10.1186/s13321-025-01022-3},
doi = {10.1186/s13321-025-01022-3},
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 (MPro) 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{\textbackslash},{\textasciitilde}{\textbackslash}hbox \{kcal\}{\textbackslash},{\textbackslash}hbox \{mol\}{\textasciicircum}\{-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.},
number = {85},
journal = {Journal of Cheminformatics},
author = {Martínez León, Alejandro and Ries, Benjamin and Hub, Jochen S. and Magarkar, Aniket},
month = may,
year = {2025},
}