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.

翻译：从分子图预测其基态构象对于许多化学应用至关重要，例如分子建模、分子对接和分子性质预测。近年来，已有许多基于学习的方法被提出，以替代耗时的模拟来完成此任务。然而，这些方法通常效率低下且效果欠佳，因为它们仅依赖分子图信息从头开始进行预测。在本工作中，考虑到低质量分子构象易于获取，我们提出了一种名为ConfOpt的新框架，从构象优化的角度来预测分子基态构象。具体而言，ConfOpt以分子图及其对应的低质量三维构象作为输入，然后在分子图的指导下，通过迭代优化低质量构象来推导出基态构象。在训练过程中，ConfOpt同时优化预测的原子三维坐标和相应的原子间距离，从而得到一个强大的预测模型。大量实验表明，ConfOpt显著优于现有方法，从而为高效、准确地预测分子基态构象提供了一种新范式。