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• W4200024659 abstract "Since the successful debut of AlphaGo, deep learning (DL) techniques have been applied to almost all areas of data sciences and are achieving remarkable milestones. For example, in predicting cytochrome P450 (CYP450) inhibition, DL and other machine learning techniques applied were proven significantly useful. However, currently, most models are focused on how much they can improve compared to previously published methods by using different methodologies and larger data sets without considering bio-selectivity. This study provides a multitask classification model for five important isoforms of CYP450. Our model uses the most extensive data set of over 150 000 molecules ever used. To consider the stereoisomerism of bio-selectivity, new descriptors have been developed. We illustrated the discriminating power of the new descriptors with some examples that demonstrated the improvement in the model's performance using three-dimensional descriptors, which account for the stereoselectivity of drug–protein/enzymes that two-dimensional models cannot achieve." @default.
• W4200024659 created "2021-12-31" @default.
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• W4200024659 date "2021-12-07" @default.
• W4200024659 modified "2023-10-14" @default.
• W4200024659 title "Prediction of human cytochrome <scp>P450</scp> inhibition using bio‐selectivity induced deep neural network" @default.
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• W4200024659 doi "https://doi.org/10.1002/bkcs.12445" @default.
• W4200024659 hasPublicationYear "2021" @default.
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