Publicaciones de Luciano Esteban Peña-Tejo
2024
Bedoya, Mauricio; Adasme-Carreño, Francisco; Peña-Martínez, Paula Andrea; Muñoz-Gutiérrez, Camila; Peña-Tejo, Luciano; Montesinos, José C. E. Márquez; Hernández-Rodríguez, Erix W.; González, Wendy; Martínez, Leandro; Alzate-Morales, Jans
Moltiverse: Molecular Conformer Generation Using Enhanced Sampling Methods Miscelánea
2024.
@misc{bedoya_moltiverse_2024,
title = {Moltiverse: Molecular Conformer Generation Using Enhanced Sampling Methods},
author = {Mauricio Bedoya and Francisco Adasme-Carreño and Paula Andrea Peña-Martínez and Camila Muñoz-Gutiérrez and Luciano Peña-Tejo and José C. E. Márquez Montesinos and Erix W. Hernández-Rodríguez and Wendy González and Leandro Martínez and Jans Alzate-Morales},
url = {https://chemrxiv.org/engage/chemrxiv/article-details/67621d996dde43c908780be8},
doi = {10.26434/chemrxiv-2024-qs0pc-v2},
year = {2024},
date = {2024-12-01},
urldate = {2025-01-03},
publisher = {Chemistry},
abstract = {Accurately predicting the diverse bound-state conformations of small molecules is crucial for successful drug discovery and design, particularly when detailed protein-ligand interactions are unknown. Established tools exist, but efficiently exploring the vast conformational space remains challenging. This work introduces Moltiverse, a novel protocol using enhanced sampling molecular dynamics (MD) simulations for conformer generation. The extended adaptive biasing force (eABF) algorithm combined with metadynamics, guided by a single collective variable (radius of gyration, RDGYR), efficiently samples the conformational landscape of a small molecule. Moltiverse demonstrates comparable accuracy and, in some cases, superior quality when benchmarked against established software like RDKit, CONFORGE, ConfGenX, Torsional diffusion, and Conformator. We present an exhaustive ranking based on eight quantitative metrics and statistical analysis for robust conformer generation algorithms comparison and provide recommendations for their improvement based on our findings. We introduce the Cofactorv1 dataset, a complementary resource for conformer generator evaluation. Unlike traditional datasets with thousands of single-conformer molecules, the Cofactorv1 dataset features only seven small molecule cofactors but with hundreds to thousands of experimental conformers per molecule (sourced from the PDB). This diversity, encompassing 15-29 rotatable bonds, poses a significant challenge for conformer generation benchmarks. Cofactorv1 is a complementary dataset that serves as a valuable resource for developing and evaluating conformer generation methods like Moltiverse, pushing the boundaries of accuracy and diversity in this relevant field.},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
Accurately predicting the diverse bound-state conformations of small molecules is crucial for successful drug discovery and design, particularly when detailed protein-ligand interactions are unknown. Established tools exist, but efficiently exploring the vast conformational space remains challenging. This work introduces Moltiverse, a novel protocol using enhanced sampling molecular dynamics (MD) simulations for conformer generation. The extended adaptive biasing force (eABF) algorithm combined with metadynamics, guided by a single collective variable (radius of gyration, RDGYR), efficiently samples the conformational landscape of a small molecule. Moltiverse demonstrates comparable accuracy and, in some cases, superior quality when benchmarked against established software like RDKit, CONFORGE, ConfGenX, Torsional diffusion, and Conformator. We present an exhaustive ranking based on eight quantitative metrics and statistical analysis for robust conformer generation algorithms comparison and provide recommendations for their improvement based on our findings. We introduce the Cofactorv1 dataset, a complementary resource for conformer generator evaluation. Unlike traditional datasets with thousands of single-conformer molecules, the Cofactorv1 dataset features only seven small molecule cofactors but with hundreds to thousands of experimental conformers per molecule (sourced from the PDB). This diversity, encompassing 15-29 rotatable bonds, poses a significant challenge for conformer generation benchmarks. Cofactorv1 is a complementary dataset that serves as a valuable resource for developing and evaluating conformer generation methods like Moltiverse, pushing the boundaries of accuracy and diversity in this relevant field.
