Philip W. Lee
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View article: Chemical rules for optimization of chemical mutagenicity via matched molecular pairs analysis and machine learning methods
Chemical rules for optimization of chemical mutagenicity via matched molecular pairs analysis and machine learning methods Open
Chemical mutagenicity is a serious issue that needs to be addressed in early drug discovery. Over a long period of time, medicinal chemists have manually summarized a series of empirical rules for the optimization of chemical mutagenicity.…
View article: Chemical Rules for Optimization of Chemical Mutagenicity via Matched Molecular Pairs Analysis and Machine Learning Methods
Chemical Rules for Optimization of Chemical Mutagenicity via Matched Molecular Pairs Analysis and Machine Learning Methods Open
Chemical mutagenicity is a serious issue that needs to be addressed in early drug discovery. Over a long period of time, medicinal chemists have manually summarized a series of empirical rules for the optimization of chemical mutagenicity.…
View article: In silico prediction of UGT-mediated metabolism in drug-like molecules via graph neural network
In silico prediction of UGT-mediated metabolism in drug-like molecules via graph neural network Open
UDP-glucuronosyltransferases (UGTs) have gained increasing attention as they play important roles in the phase II metabolism of drugs. Due to the time-consuming process and high cost of experimental approaches to identify the metabolic fat…
View article: <i>In silico</i> prediction of chemical aquatic toxicity for marine crustaceans <i>via</i> machine learning
<i>In silico</i> prediction of chemical aquatic toxicity for marine crustaceans <i>via</i> machine learning Open
Six machine learning methods combined with descriptors or fingerprints were employed to predict chemical toxicity on marine crustaceans.
View article: In silico prediction of hERG potassium channel blockage by chemical category approaches
In silico prediction of hERG potassium channel blockage by chemical category approaches Open
A series of models of hERG blockage were built using five machine learning methods based on 13 molecular descriptors, five types of fingerprints and molecular descriptors combining fingerprints at four blockage thresholds.
View article: In silico prediction of chemical aquatic toxicity with chemical category approaches and substructural alerts
In silico prediction of chemical aquatic toxicity with chemical category approaches and substructural alerts Open
Aquatic toxicity is an important endpoint in the evaluation of chemically adverse effects on ecosystems.