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