Environmental chemicals may affect endocrine systems through multiple mechanisms, one of which is via effects on aromatase (also known as CYP19A1), an enzyme critical for maintaining the normal balance of estrogens and androgens in the body. Therefore, rapid and efficient identification of aromatase-related endocrine disrupting chemicals (EDCs) is important for toxicology and environment risk assessment. In this study, on the basis of the Tox21 10K compound library, in silico classification models for predicting aromatase binders/nonbinders were constructed by machine learning methods. To improve the prediction ability of the models, a combined classifier (CC) strategy that combines different independent machine learning methods was adopted. Performances of the models were measured by test and external validation sets containing 1336 and 216 chemicals, respectively. The best model was obtained with the MACCS (Molecular Access System) fingerprint and CC method, which exhibited an accuracy of 0.84 for the test set and 0.91 for the external validation set. Additionally, several representative substructures for characterizing aromatase binders, such as ketone, lactone, and nitrogen-containing derivatives, were identified using information gain and substructure frequency analysis. Our study provided a systematic assessment of chemicals binding to aromatase. The built models can be helpful to rapidly identify potential EDCs targeting aromatase.

The cytochromes CYP3A4 and CYP3A5 share 84% sequence identity, but they exhibit different catalytic activities toward some substrates. Schisantherin E (SE) was recently identified as a selective substrate of CYP3A5, which exhibited catalytic efficiency that was more than 23 times higher than CYP3A4. At present, however, the structural determinants responsible for the different catalytic activities of the two enzymes toward SE have not been fully understood. In this study, a combination of molecular docking, molecular dynamic simulations, and binding free energy calculation was performed on the CYP3A4/CYP3A5‐SE systems to investigate the issue. The results demonstrate that Ser119 in CYP3A4 and Glu374 in CYP3A5 formed direct hydrogen bonding with SE, respectively. Additionally, one water molecule located between the B‐C loop and the I helix mediated different hydrogen‐bonding networks between CYP3A4/3A5 and SE. The residue differences (Phe/Leu108 and Leu/Phe210) triggered the distinct conformational changes of the Phe‐cluster residues, especially Phe213 and Phe215, which formed stronger hydrophobic interactions with SE in CYP3A5. The calculated binding free energies were consistent with the experimental results.