Drug-induced transformations in disease characteristics at the cellular and molecular level offers the opportunity to predict and evaluate the efficacy of pharmaceutical ingredients whilst enabling the optimal design of new and improved drugs with enhanced pharmacokinetics and pharmacodynamics. Machine learning is a promising in-silico tool used to simulate cells with specific disease properties and to determine their response toward drug uptake. Differences in the properties of normal and infected cells, including biophysical, biochemical and physiological characteristics, plays a key role in developing fundamental cellular probing platforms for machine learning applications. Cellular features can be extracted periodically from both the drug treated, infected, and normal cells via image segmentations in order to probe dynamic differences in cell behavior. Cellular segmentation can be evaluated to reflect the levels of drug effect on a distinct cell or group of cells via probability scoring. This article provides an account for the use of machine learning methods to probe differences in the biophysical, biochemical and physiological characteristics of infected cells in response to pharmacokinetics uptake of drug ingredients for application in cancer, diabetes and neurodegenerative disease therapies.

在细胞和分子水平上，药物引起的疾病特征转化为预测和评估药物成分的功效提供了机会，同时能够优化设计并增强药代动力学和药效学的新药和改良药。机器学习是一种有前途的计算机模拟工具，可用于模拟具有特定疾病特性的细胞并确定其对药物吸收的反应。正常细胞和感染细胞的特性差异（包括生物物理，生化和生理特性）在开发用于机器学习应用程序的基本细胞探测平台中起着关键作用。细胞特征可周期性地从处理过的，被感染的两种药物，和正常细胞中提取通过图像分割，以探测细胞行为的动态差异。可以通过概率评分来评估细胞分割，以反映药物对不同细胞或一组细胞的作用水平。本文介绍了使用机器学习方法来探查感染细胞的生物物理，生化和生理特征的差异，以响应用于癌症，糖尿病和神经退行性疾病治疗的药物成分的药代动力学摄取。