Promises of Machine Learning Approaches in Prediction of Absorption of Compounds

Title:Promises of Machine Learning Approaches in Prediction of Absorption of Compounds

Volume: 18 Issue: 3

Author(s): Rajnish Kumar, Anju Sharma, Mohammed Haris Siddiqui and Rajesh Kumar Tiwari*

Affiliation:

• Amity Institute of Biotechnology, Amity University Uttar Pradesh, Lucknow, 226028, Uttar Pradesh,India

Keywords: Absorption, drug, machine learning, models, pharmacokinetics, prediction.

Abstract: 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.

Cite this article as:

Kumar Rajnish, Sharma Anju , Siddiqui Haris Mohammed and Tiwari Kumar Rajesh *, Promises of Machine Learning Approaches in Prediction of Absorption of Compounds, Mini-Reviews in Medicinal Chemistry 2018; 18 (3) . https://dx.doi.org/10.2174/1389557517666170315150116