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Prediction of CNS Drug-likeness.pdf (796.26 kB)
Prediction of Drug-likeness of Central Nervous System Drug Candidates Using a Feed-Forward Neural Network Based on Chemical Structure
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revised on 30.08.2020 and posted on 31.08.2020by Yi-Gao Yuan, Xiao Wang
Modern medical science
has been greatly advanced by the development of new drugs, despite the fact
that the process of developing new drugs is costly and time-consuming. An
accurate prediction method for the drug-likeness in the early stage of drug
discovery is highly desirable, as it will facilitate the discovery process and
reduce the overall cost and eventually contribute to human well-being. Based on
a central nervous system (CNS) drug dataset, we constructed an artificial
neural network (NN) to predict the CNS drug-likeness of a given bioactive compound.
We first constructed a simple feed-forward neural network, to learn and predict
the possible correlations between twelve physiochemical properties and the CNS
drug-likeness. The accuracy of prediction has reached 80%, which has been
improved from previous reports. We further constructed a neural network based
on chemical structure, and the accuracy has increased to 86%. The successful
prediction of the CNS drug-likeness renders this NN a powerful tool for virtual