Novartis Institutes for BioMedical Research, NIBR Global Discovery Chemistry, Computer-Aided Drug Discovery, Novartis Pharma AG, Novartis Campus, 4056, Basel, Switzerland.
Novartis Institutes for BioMedical Research, NIBR Translational Medicine, Modeling and Simulations, Novartis Pharma AG, Novartis Campus, 4056, Basel, Switzerland.
Mol Inform. 2022 Jun;41(6):e2100277. doi: 10.1002/minf.202100277. Epub 2022 Jan 22.
The ability to predict chemical reactivity of a molecule is highly desirable in drug discovery, both ex vivo (synthetic route planning, formulation, stability) and in vivo: metabolic reactions determine pharmacodynamics, pharmacokinetics and potential toxic effects, and early assessment of liabilities is vital to reduce attrition rates in later stages of development. Quantum mechanics offer a precise description of the interactions between electrons and orbitals in the breaking and forming of new bonds. Modern algorithms and faster computers have allowed the study of more complex systems in a punctual and accurate fashion, and answers for chemical questions around stability and reactivity can now be provided. Through machine learning, predictive models can be built out of descriptors derived from quantum mechanics and cheminformatics, even in the absence of experimental data to train on. In this article, current progress on computational reactivity prediction is reviewed: applications to problems in drug design, such as modelling of metabolism and covalent inhibition, are highlighted and unmet challenges are posed.
预测分子的化学反应性在药物发现中非常重要，无论是在体外（合成路线规划、制剂、稳定性）还是体内：代谢反应决定了药效学、药代动力学和潜在的毒性作用，早期评估不良反应对于降低开发后期的淘汰率至关重要。量子力学提供了对电子和轨道在新键的断裂和形成过程中相互作用的精确描述。现代算法和更快的计算机允许以准确和精确的方式研究更复杂的系统，并且现在可以提供有关稳定性和反应性的化学问题的答案。通过机器学习，可以从量子力学和化学信息学中得出的描述符构建预测模型，即使没有实验数据可用于训练。本文综述了计算反应性预测的最新进展：突出了其在药物设计问题中的应用，例如代谢和共价抑制的建模，并提出了尚未解决的挑战。
