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Kumar R, Sharma A, Siddiqui MH, et al. Promises of Machine Learning Approaches in Prediction of Absorption of Compounds. Mini Rev Med Chem. 2018;18(3):196-207doi: 10.2174/1389557517666170315150116.
Kumar, R., Sharma, A., Siddiqui, M. H., & Tiwari, R. K. (2018). Promises of Machine Learning Approaches in Prediction of Absorption of Compounds. Mini reviews in medicinal chemistry, 18(3), 196-207. https://doi.org/10.2174/1389557517666170315150116
Kumar, Rajnish, et al. "Promises of Machine Learning Approaches in Prediction of Absorption of Compounds." Mini reviews in medicinal chemistry vol. 18,3 (2018): 196-207. doi: https://doi.org/10.2174/1389557517666170315150116
Kumar R, Sharma A, Siddiqui MH, Tiwari RK. Promises of Machine Learning Approaches in Prediction of Absorption of Compounds. Mini Rev Med Chem. 2018;18(3):196-207. doi: 10.2174/1389557517666170315150116. PMID: 28302041.
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Mini Rev Med Chem. 2018;18(3):196-207. doi: 10.2174/1389557517666170315150116.

Promises of Machine Learning Approaches in Prediction of Absorption of Compounds.

Mini reviews in medicinal chemistry

Rajnish Kumar, Anju Sharma, Mohammed Haris Siddiqui, Rajesh Kumar Tiwari

Affiliations

  1. Amity Institute of Biotechnology, Amity University Uttar Pradesh, Lucknow, 226028, Uttar Pradesh. India.
  2. Department of Bioengineering, Integral University, Dasauli, P.O. Basha, Kursi Road, Lucknow, Uttar Pradesh. India.

PMID: 28302041 DOI: 10.2174/1389557517666170315150116

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.

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Keywords: Absorption; drug; machine learning; models; pharmacokinetics; prediction

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