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- W4247487912 abstract "The 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 at 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 the well-being of human beings. 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 compound. Based on the published results, 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 is higher than previous reports. The successful implementation of NN to predict the CNS drug-likeness indicated that NN could be a powerful tool for the prediction. Moreover, we further constructed a neural network based on the chemical structure, and the accuracy has reached 86%. We hope that these methods can serve as an applicable set of protocols for virtual drug screening." @default.
- W4247487912 created "2022-05-12" @default.
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- W4247487912 date "2020-08-28" @default.
- W4247487912 modified "2023-10-17" @default.
- W4247487912 title "Prediction of Drug-likeness of Central Nervous System Drug Candidates Using a Feed-Forward Neural Network Based on Chemical Structure" @default.
- W4247487912 doi "https://doi.org/10.26434/chemrxiv.12887102.v1" @default.
- W4247487912 hasPublicationYear "2020" @default.
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