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...
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| Format: | Article |
| Language: | English |
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BMC
2025-05-01
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| Series: | Journal of Cheminformatics |
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| Online Access: | https://doi.org/10.1186/s13321-025-01022-3 |
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| author | Alejandro Martínez León Benjamin Ries Jochen S. Hub Aniket Magarkar |
| author_facet | Alejandro Martínez León Benjamin Ries Jochen S. Hub Aniket Magarkar |
| author_sort | Alejandro Martínez León |
| collection | DOAJ |
| description | 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 $$-10\,~\hbox {kcal}\,\hbox {mol}^{-1}$$ - 10 kcal 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 . |
| format | Article |
| id | doaj-art-0fb5ea7535c1432a809cc4f5934feecd |
| institution | DOAJ |
| issn | 1758-2946 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | BMC |
| record_format | Article |
| series | Journal of Cheminformatics |
| spelling | doaj-art-0fb5ea7535c1432a809cc4f5934feecd2025-08-20T03:16:51ZengBMCJournal of Cheminformatics1758-29462025-05-0117112210.1186/s13321-025-01022-3Moldrug algorithm for an automated ligand binding site exploration by 3D aware molecular enumerationsAlejandro Martínez León0Benjamin Ries1Jochen S. Hub2Aniket Magarkar3Theoretical Physics and Center for Biophysics, Universität des SaarlandesMedicinal Chemistry, Boehringer Ingelheim Pharma GmbH & Co KGTheoretical Physics and Center for Biophysics, Universität des SaarlandesMedicinal Chemistry, Boehringer Ingelheim Pharma GmbH & Co KGAbstract 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 $$-10\,~\hbox {kcal}\,\hbox {mol}^{-1}$$ - 10 kcal 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 .https://doi.org/10.1186/s13321-025-01022-3Structure-based drug designChemical space explorationOpen-source drug design toolsLigand optimizationSARS-CoV-2 inhibitorsFragment-based drug design |
| spellingShingle | Alejandro Martínez León Benjamin Ries Jochen S. Hub Aniket Magarkar Moldrug algorithm for an automated ligand binding site exploration by 3D aware molecular enumerations Journal of Cheminformatics Structure-based drug design Chemical space exploration Open-source drug design tools Ligand optimization SARS-CoV-2 inhibitors Fragment-based drug design |
| title | Moldrug algorithm for an automated ligand binding site exploration by 3D aware molecular enumerations |
| title_full | Moldrug algorithm for an automated ligand binding site exploration by 3D aware molecular enumerations |
| title_fullStr | Moldrug algorithm for an automated ligand binding site exploration by 3D aware molecular enumerations |
| title_full_unstemmed | Moldrug algorithm for an automated ligand binding site exploration by 3D aware molecular enumerations |
| title_short | Moldrug algorithm for an automated ligand binding site exploration by 3D aware molecular enumerations |
| title_sort | moldrug algorithm for an automated ligand binding site exploration by 3d aware molecular enumerations |
| topic | Structure-based drug design Chemical space exploration Open-source drug design tools Ligand optimization SARS-CoV-2 inhibitors Fragment-based drug design |
| url | https://doi.org/10.1186/s13321-025-01022-3 |
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