Predicting Molecular Ground-State Conformation via Conformation Optimization

• URL: http://arxiv.org/abs/2410.09795v2
• Date: Wed, 30 Oct 2024 14:33:37 GMT
• Title: Predicting Molecular Ground-State Conformation via Conformation Optimization
• Authors: Fanmeng Wang, Minjie Cheng, Hongteng Xu,
• Abstract summary: We propose a novel framework called ConfOpt to predict molecular ground-state conformation from the perspective of conformation optimization. During training, ConfOpt concurrently optimize the predicted atomic 3D coordinates and the corresponding interatomic distances, resulting in a strong predictive model.
• Score: 24.18678055892153
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Predicting ground-state conformation from the corresponding molecular graph is crucial for many chemical applications, such as molecular modeling, molecular docking, and molecular property prediction. Recently, many learning-based methods have been proposed to replace time-consuming simulations for this task. However, these methods are often inefficient and sub-optimal as they merely rely on molecular graph information to make predictions from scratch. In this work, considering that molecular low-quality conformations are readily available, we propose a novel framework called ConfOpt to predict molecular ground-state conformation from the perspective of conformation optimization. Specifically, ConfOpt takes the molecular graph and corresponding low-quality 3D conformation as inputs, and then derives the ground-state conformation by iteratively optimizing the low-quality conformation under the guidance of the molecular graph. During training, ConfOpt concurrently optimizes the predicted atomic 3D coordinates and the corresponding interatomic distances, resulting in a strong predictive model. Extensive experiments demonstrate that ConfOpt significantly outperforms existing methods, thus providing a new paradigm for efficiently and accurately predicting molecular ground-state conformation.

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