Prediction of protein-ligand binding affinities using 3D machine learning methods

3D机器学习方法有可能弥合对接和FEP之间的差距，提供高通量的蛋白质-配体结合亲和力预测，比传统的对接评分函数具有更高的准确性。原子环境矢量(AEV)在快速计算小分子的量子力学构象能和预测蛋白质与配体的结合亲和力方面具有广阔的应用前景。在2022年的旋转项目中，我们应用这些方法来预测非共价相互作用的能量，特别是氢键。未来的发展方向包括共享元件之间的参数，包括作为特征的电荷和杂化状态，以及将学习从非共价相互作用任务转移到蛋白质-配体结合任务。根据Roitberg最近使用ANI模拟蛋白质-配体复合体的结果，探索AEV如何用于分子动力学模拟也是有兴趣的。这些方法可用于包括对结合亲和力的熵贡献以及非共价相互作用的焓贡献。这个项目的目的是测试结合亲和力的模型，并测试这些模型在多大程度上顽强地学习非共价相互作用，并比目前流行的基于配体的模型更具普遍性，用于预测结合亲和力。该项目属于EPSRC生物信息学研究领域，因为使用化学和生物数据对生物系统进行计算建模。葛兰素史克是一家合作公司。

3D machine learning methods offer the potential to bridge the gap between docking and FEP, providing high throughput predictions of protein-ligand binding affinities with higher accuracy than classical docking scoring functions. Atomic environment vectors (AEVs) have been shown to be a promising representation for both rapid calculation of quantum mechanical conformational energies of small molecules, and in predicting protein-ligand binding affinities. In the 2022 rotation project, we applied these methods to predict the energy of of non-covalent interactions, in particular hydrogen bonds. Future directions include sharing parameters between elements, including charge and hybridization state as features, and transfer learning from the non-covalent interaction task to the protein-ligand binding task. It is also of interest to explore how AEVs can be used in molecular dynamics simulations following upon recent results from Roitberg on simulating a protein-ligand complex using ANI. These methods could be useful for including the entropic contributions to binding affinities as well as the enthalpic contributions from non-covalent interactions. This project would aim to test models on binding affinity and test the extent to which these models robustly learn non-covalent interactions and have improved generalizability over the prevailing ligand-based models for predicting binding affinity. This project falls into the EPSRC Biological informatics research area due to the use of computational modeling of biological systems using chemical and biological data. GSK is a collaborating company.