标题： 看妈妈，没有实验数据！ 从模拟中学习给蛋白质-配体相互作用打分 显示英文标题

标题： Look mom, no experimental data! Learning to score protein-ligand interactions from simulations

摘要： 尽管近年来蛋白质-配体结构预测取得了进展，但深度学习方法在准确预测结合亲和力方面仍然有限，尤其是对于与训练集不同的新型蛋白质靶点。 相比之下，基于物理的结合自由能计算在化学空间中提供了很高的精度，但在大规模筛选中计算量过大。 我们提出了一种混合方法，通过在蛋白质与随机小分子复合物的分子动力学模拟中训练靶标特异性神经网络，来近似基于物理方法的精度。 我们的方法利用力匹配来学习每个靶点的配体结合的隐式自由能图。 经对六种蛋白质进行评估，我们的方法在每个靶点上使用 100-500 μs 的分子动力学模拟，实现了具有竞争力的虚拟筛选性能。 值得注意的是，当使用活性化合物的真实姿态时，该方法实现了最先进的早期富集效果。 这些结果凸显了基于物理的学习在新型靶点虚拟筛选中的潜力。 我们在 https://github.com/molecularmodelinglab/lfm 公开发布了该论文的代码。 显示英文摘要

摘要： Despite recent advances in protein-ligand structure prediction, deep learning methods remain limited in their ability to accurately predict binding affinities, particularly for novel protein targets dissimilar from the training set. In contrast, physics-based binding free energy calculations offer high accuracy across chemical space but are computationally prohibitive for large-scale screening. We propose a hybrid approach that approximates the accuracy of physics-based methods by training target-specific neural networks on molecular dynamics simulations of the protein in complex with random small molecules. Our method uses force matching to learn an implicit free energy landscape of ligand binding for each target. Evaluated on six proteins, our approach achieves competitive virtual screening performance using 100-500 $\mu$s of MD simulations per target. Notably, this approach achieves state-of-the-art early enrichment when using the true pose for active compounds. These results highlight the potential of physics-informed learning for virtual screening on novel targets. We publicly release the code for this paper at https://github.com/molecularmodelinglab/lfm.