标题： SurGBSA：从分子动力学模拟中学习表示 显示英文标题

标题： SurGBSA: Learning Representations From Molecular Dynamics Simulations

摘要： 从药物样化合物和蛋白质的静态结构进行自监督预训练，可以实现强大的学习特征表示。 学习到的特征在一系列预测任务中表现出最先进的性能，包括分子性质、结构生成和蛋白质-配体相互作用。 大多数方法受到其使用静态结构的限制，如何最好地利用原子级分子动力学（MD）模拟来开发更通用的模型以提高新型分子结构的预测准确性，仍然是一个开放问题。 我们提出了SURrogate mmGBSA（SurGBSA）作为基于MD的表示学习的新建模方法，该方法学习分子力学广义 Born 表面面积（MMGBSA）的代理函数。 我们首次展示了物理信息预训练的好处，即在从CASF-2016基准的MD模拟中收集的超过140万条3D轨迹的集合上训练代理MMGBSA模型。 SurGBSA在挑战性的姿态排序问题上，与传统的基于物理的单点MMGBSA计算相比，速度提高了6,497倍，同时在正确姿态识别的问题上几乎达到单点MMGBSA的精度（差异为-0.4%）。 我们的工作通过展示在MD模拟上训练时模型的改进，推动了分子基础模型的发展。 模型、代码和训练数据已公开提供。 显示英文摘要

摘要： Self-supervised pretraining from static structures of drug-like compounds and proteins enable powerful learned feature representations. Learned features demonstrate state of the art performance on a range of predictive tasks including molecular properties, structure generation, and protein-ligand interactions. The majority of approaches are limited by their use of static structures and it remains an open question, how best to use atomistic molecular dynamics (MD) simulations to develop more generalized models to improve prediction accuracy for novel molecular structures. We present SURrogate mmGBSA (SurGBSA) as a new modeling approach for MD-based representation learning, which learns a surrogate function of the Molecular Mechanics Generalized Born Surface Area (MMGBSA). We show for the first time the benefits of physics-informed pre-training to train a surrogate MMGBSA model on a collection of over 1.4 million 3D trajectories collected from MD simulations of the CASF-2016 benchmark. SurGBSA demonstrates a dramatic 6,497x speedup versus a traditional physics-based single-point MMGBSA calculation while nearly matching single-point MMGBSA accuracy on the challenging pose ranking problem for identification of the correct top pose (-0.4% difference). Our work advances the development of molecular foundation models by showing model improvements when training on MD simulations. Models, code and training data are made publicly available.