COSMIC: Molecular Conformation Space Modeling in Internal Coordinates with an Adversarial Framework

• Published in: Journal of chemical information and modeling 2024-05, Vol.64 (9), p.3610-3620
• Main Authors: Kuznetsov, Maksim, Ryabov, Fedor, Schutski, Roman, Shayakhmetov, Rim, Lin, Yen-Chu, Aliper, Alex, Polykovskiy, Daniil
• Format: Article
• Language: English
• Subjects: Algorithms; Drug Discovery; Ligands; Machine Learning and Deep Learning; Models, Molecular; Molecular Conformation; Random noise; Sampling
• ISSN: 1549-9596; 1549-960X
• DOI: 10.1021/acs.jcim.3c00989

The fast and accurate conformation space modeling is an essential part of computational approaches for solving ligand and structure-based drug discovery problems. Recent state-of-the-art diffusion models for molecular conformation generation show promising distribution coverage and physical plausibility metrics but suffer from a slow sampling procedure. We propose a novel adversarial generative framework, COSMIC, that shows comparable generative performance but provides a time-efficient sampling and training procedure. Given a molecular graph and random noise, the generator produces a conformation in two stages. First, it constructs a conformation in a rotation and translation invariant representationinternal coordinates. In the second step, the model predicts the distances between neighboring atoms and performs a few fast optimization steps to refine the initial conformation. The proposed model considers conformation energy, achieving comparable space coverage, and diversity metrics results.