TorsionNet: A Deep Neural Network to Rapidly Predict Small-Molecule Torsional Energy Profiles with the Accuracy of Quantum Mechanics
TorsionNet: A Deep Neural Network to Rapidly Predict Small-Molecule Torsional Energy Profiles with the Accuracy of Quantum Mechanics
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DOI：
10.1021/acs.jcim.1c01346
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发表时间：
2022-02-28
影响因子：
5.6
通讯作者：
Bakken, Gregory A.
中科院分区：
文献类型：
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作者：
Rai, Brajesh K.;Sresht, Vishnu;Bakken, Gregory A.
快速准确地评估小分子二面角能是药物化学中分子设计和优化的关键。然而，准确预测扭转能分布仍然具有挑战性，因为目前的分子力学（MM）方法受到药物样化学空间覆盖不足和精确的量子力学（QM）方法过于昂贵的限制。为了解决这一限制，我们引入了TorsionNet，这是一种专门开发的深度神经网络（DNN）模型，用于预测具有QM级精度的小分子扭转能量分布。我们应用主动学习来识别近5万个片段（具有元素H，C，N，O，F，S和Cl），最大限度地提高了我们公司化合物库的覆盖率，并利用大规模并行云计算资源对这些片段进行密度泛函理论（DFT）扭转扫描，生成了120万个DFT能量的训练数据集。在此数据集上训练TorsionNet后，我们获得了一个模型，该模型可以快速预测典型药物样片段的扭转能量分布，具有DFT级的准确性。重要的是，我们的方法还提供了一个不确定性估计预测的配置文件，而无需任何额外的计算。在这份报告中，我们表明，TorsionNet可以准确地识别晶体结构中观察到的优选二面角几何形状。我们基于TorsionNet的分析了一组不同的蛋白质-配体复合物，测量了结合亲和力，显示了高配体菌株和低效力之间的强关联。我们还介绍了TorsionNet的实际应用，展示了考虑基于DNN的应变能如何导致现有潜在客户发现和设计工作流程的实质性改进。TorsionNet 500是一个基准数据集，包括500个具有DFT扭转曲线（12 k MM和DFT优化的几何形状和能量）的化学多样性片段，已经创建并公开提供。
Fast and accurate assessment of small-molecule dihedral energetics is crucial for molecular design and optimization in medicinal chemistry. Yet, accurate prediction of torsion energy profiles remains challenging as the current molecular mechanics (MM) methods are limited by insufficient coverage of drug-like chemical space and accurate quantum mechanical (QM) methods are too expensive. To address this limitation, we introduce TorsionNet, a deep neural network (DNN) model specifically developed to predict small-molecule torsion energy profiles with QM-level accuracy. We applied active learning to identify nearly 50k fragments (with elements H, C, N, O, F, S, and Cl) that maximized the coverage of our corporate compound library and leveraged massively parallel cloud computing resources for density functional theory (DFT) torsion scans of these fragments, generating a training data set of 1.2 million DFT energies. After training TorsionNet on this data set, we obtain a model that can rapidly predict the torsion energy profile of typical drug-like fragments with DFT-level accuracy. Importantly, our method also provides an uncertainty estimate for the predicted profiles without any additional calculations. In this report, we show that TorsionNet can accurately identify the preferred dihedral geometries observed in crystal structures. Our TorsionNet-based analysis of a diverse set of protein-ligand complexes with measured binding affinity shows a strong association between high ligand strain and low potency. We also present practical applications of TorsionNet that demonstrate how consideration of DNN-based strain energy leads to substantial improvement in existing lead discovery and design workflows. TorsionNet500, a benchmark data set comprising 500 chemically diverse fragments with DFT torsion profiles (12k MM- and DFT-optimized geometries and energies), has been created and is made publicly available.