Schifferstein Jordy, Bernatavicius Andrius, Janssen Antonius P A
Department of Molecular Physiology, Leiden Institute of Chemistry, Leiden University, Leiden 2333CC, The Netherlands.
Oncode Institute, Utrecht 3521AL, The Netherlands.
J Chem Inf Model. 2024 Dec 23;64(24):9196-9204. doi: 10.1021/acs.jcim.4c01260. Epub 2024 Dec 10.
Kinase inhibitors are an important class of anticancer drugs, with 80 inhibitors clinically approved and >100 in active clinical testing. Most bind competitively in the ATP-binding site, leading to challenges with selectivity for a specific kinase, resulting in risks for toxicity and general off-target effects. Assessing the binding of an inhibitor for the entire kinome is experimentally possible but expensive. A reliable and interpretable computational prediction of kinase selectivity would greatly benefit the inhibitor discovery and optimization process. Here, we use machine learning on docked poses to address this need. To this end, we aggregated all known inhibitor-kinase affinities and generated the complete accompanying 3D interactome by docking all inhibitors to the respective high-quality X-ray structures. We then used this resource to train a neural network as a kinase-specific scoring function, which achieved an overall performance () of 0.63-0.74 on unseen inhibitors across the kinome. The entire pipeline from molecule to 3D-based affinity prediction has been fully automated and wrapped in a freely available package. This has a graphical user interface that is tightly integrated with PyMOL to allow immediate adoption in the medicinal chemistry practice.
激酶抑制剂是一类重要的抗癌药物，有80种抑制剂已获临床批准，超过100种正处于临床活性测试阶段。大多数抑制剂在ATP结合位点竞争性结合，导致对特定激酶的选择性面临挑战，从而产生毒性风险和一般脱靶效应。通过实验评估抑制剂与整个激酶组的结合是可行的，但成本高昂。可靠且可解释的激酶选择性计算预测将极大地有益于抑制剂的发现和优化过程。在此，我们利用对接构象的机器学习来满足这一需求。为此，我们汇总了所有已知的抑制剂-激酶亲和力，并通过将所有抑制剂对接至各自的高质量X射线结构生成了完整的伴随3D相互作用组。然后，我们利用这一资源训练神经网络作为激酶特异性评分函数，该函数在激酶组中对未见抑制剂的总体性能（）为0.63 - 0.74。从分子到基于3D的亲和力预测的整个流程已完全自动化，并封装在一个免费可用的软件包中。该软件包有一个与PyMOL紧密集成的图形用户界面，以便在药物化学实践中立即采用。
