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

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Abstract

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.

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Kuznetsov, M., Ryabov, F., Schutski, R., Shayakhmetov, R., Lin, Y. C., Aliper, A., & Polykovskiy, D. (2024). COSMIC: Molecular Conformation Space Modeling in Internal Coordinates with an Adversarial Framework. Journal of Chemical Information and Modeling, 64(9), 3610–3620. https://doi.org/10.1021/acs.jcim.3c00989

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