Medicinal Chemistry, Research and Early Development, Cardiovascular, Renal and Metabolism (CVRM), BioPharmaceuticals R&D, AstraZeneca, Pepparedsleden 1, SE-43183 Gothenburg, Sweden.
Department of Life Science Informatics and Data Science, B-IT, Lamarr Institute for Machine Learning and Artificial Intelligence, LIMES Program Chemical Biology and Medicinal Chemistry, Rheinische Friedrich-Wilhelms-Universität, Friedrich-Hirzebruch-Allee 5/6, 53115 Bonn, Germany.
Mol Pharm. 2024 Oct 7;21(10):4849-4859. doi: 10.1021/acs.molpharmaceut.4c00659. Epub 2024 Sep 6.
Given their central role in signal transduction, protein kinases (PKs) were first implicated in cancer development, caused by aberrant intracellular signaling events. Since then, PKs have become major targets in different therapeutic areas. The preferred approach to therapeutic intervention of PK-dependent diseases is the use of small molecules to inhibit their catalytic phosphate group transfer activity. PK inhibitors (PKIs) are among the most intensely pursued drug candidates, with currently 80 approved compounds and several hundred in clinical trials. Following the elucidation of the human kinome and development of robust PK expression systems and high-throughput assays, large volumes of PK/PKI data have been produced in industrial and academic environments, more so than for many other pharmaceutical targets. In addition, hundreds of X-ray structures of PKs and their complexes with PKIs have been reported. Substantial amounts of PK/PKI data have been made publicly available in part as a result of open science initiatives. PK drug discovery is further supported through the incorporation of data science approaches, including the development of various specialized databases and online resources. Compound and activity data wealth compared to other targets has also made PKs a focal point for the application of artificial intelligence (AI) in pharmaceutical research. Herein, we discuss the interplay of open and data science in PK drug discovery and review exemplary studies that have substantially contributed to its development, including kinome profiling or the analysis of PKI promiscuity versus selectivity. We also take a close look at how AI approaches are beginning to impact PK drug discovery in light of their increasing data orientation.
鉴于其在信号转导中的核心作用，蛋白激酶（PKs）最初与癌症的发生有关，这是由异常的细胞内信号事件引起的。从那时起，PKs 已成为不同治疗领域的主要靶点。治疗干预依赖 PK 的疾病的首选方法是使用小分子来抑制其催化磷酸基团转移活性。PK 抑制剂（PKIs）是最受关注的药物候选物之一，目前有 80 种批准的化合物和数百种处于临床试验阶段。随着人类激酶组的阐明以及强大的 PK 表达系统和高通量测定法的发展，在工业和学术环境中产生了大量的 PK/PKI 数据，比许多其他药物靶点都要多。此外，已经报道了数百种 PK 及其与 PKI 复合物的 X 射线结构。由于开放科学倡议，大量 PK/PKI 数据已经公开提供。通过纳入数据科学方法，包括开发各种专门的数据库和在线资源，进一步支持 PK 药物发现。与其他靶点相比，化合物和活性数据的丰富程度也使 PK 成为人工智能（AI）在药物研究中应用的焦点。本文讨论了开放科学和数据科学在 PK 药物发现中的相互作用，并回顾了对其发展做出重大贡献的典范研究，包括激酶组分析或 PKI 广谱性与选择性的分析。我们还仔细研究了 AI 方法如何开始根据其日益增加的数据导向来影响 PK 药物发现。
