Polarized Surface Area and Oil-Water Partition Coefficient Predicted through the Machine Learning Based on Deepchem
DOI: 10.23977/acss.2021.050204 | Downloads: 9 | Views: 229
Jin Li 1
1 Sichuan Normal University, Chengdu, Sichuan 610101, China
Corresponding AuthorJin Li
Accurate prediction of the chemical information of compounds played a vital role in the discovery of drug-like properties. The Polarized surface area (PSA) and oil-water partition coefficient (AlogP) of drug-like compounds properties were predicted based on DeepChem. By comparing the four models (Random Forest, Deep Neural Network, Convolutional Neural Network, Graphic Convolution), it is shown that CNN has 94% accuracy in PSA, DNN has 81% accuracy in AlogP. DeepChem can easily build a platform for molecular machine learning to predict specific characteristic attributes by selecting data sets, which may provide the possibility for drug prediction.
KEYWORDSMachine learning, Polarized surface area, Oil-water partition coefficient, Deepchem, Drug-like
CITE THIS PAPER
Jin Li, Polarized Surface Area and Oil-Water Partition Coefficient Predicted through the Machine Learning Based on Deepchem. Advances in Computer, Signals and Systems (2021) Vol. 5: 14-21. DOI: http://dx.doi.org/10.23977/acss.2021.050204.
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