Kumar Rajnish, Sharma Anju, Siddiqui Mohammed Haris, Tiwari Rajesh Kumar
Amity Institute of Biotechnology, Amity University Uttar Pradesh, Lucknow, 226028, Uttar Pradesh. India.
Department of Bioengineering, Integral University, Dasauli, P.O. Basha, Kursi Road, Lucknow, Uttar Pradesh. India.
Mini Rev Med Chem. 2018;18(3):196-207. doi: 10.2174/1389557517666170315150116.
The Machine Learning (ML) is one of the fastest developing techniques in the prediction and evaluation of important pharmacokinetic properties such as absorption, distribution, metabolism and excretion. The availability of a large number of robust validation techniques for prediction models devoted to pharmacokinetics has significantly enhanced the trust and authenticity in ML approaches. There is a series of prediction models generated and used for rapid screening of compounds on the basis of absorption in last one decade. Prediction of absorption of compounds using ML models has great potential across the pharmaceutical industry as a non-animal alternative to predict absorption. However, these prediction models still have to go far ahead to develop the confidence similar to conventional experimental methods for estimation of drug absorption. Some of the general concerns are selection of appropriate ML methods and validation techniques in addition to selecting relevant descriptors and authentic data sets for the generation of prediction models. The current review explores published models of ML for the prediction of absorption using physicochemical properties as descriptors and their important conclusions. In addition, some critical challenges in acceptance of ML models for absorption are also discussed.
机器学习（ML）是预测和评估诸如吸收、分布、代谢和排泄等重要药代动力学性质方面发展最快的技术之一。大量用于药代动力学预测模型的强大验证技术的出现，显著增强了人们对机器学习方法的信任和认可度。在过去十年中，基于吸收情况生成了一系列预测模型，并用于化合物的快速筛选。使用机器学习模型预测化合物的吸收情况，作为一种预测吸收的非动物替代方法，在整个制药行业具有巨大潜力。然而，这些预测模型在建立与传统实验方法类似的药物吸收估计置信度方面仍有很长的路要走。除了为生成预测模型选择相关描述符和可靠数据集外，一些普遍关注的问题还包括选择合适的机器学习方法和验证技术。本综述探讨了已发表的以物理化学性质为描述符预测吸收的机器学习模型及其重要结论。此外，还讨论了接受机器学习吸收模型的一些关键挑战。
