Moltiverse: Molecular Conformer Generation Using Enhanced Sampling Methods

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, Balloon, iCon, and Conformator in the Platinum Diverse Data set for drug-like small molecules and the Prime data set for macrocycles. We present multiple quantitative metrics and statistical analysis for robust conformer generation algorithm comparisons and provide recommendations for their improvement based on our findings. Our extensive evaluation shows that Moltiverse is particularly effective for challenging systems with high conformational flexibility, such as macrocycles, where it achieves the highest accuracy among the tested algorithms. The physics-based approach employed by Moltiverse effectively handles a wide range of molecular complexities, positioning it as a valuable tool for computational drug discovery workflows requiring accurate representation of molecular flexibility.