基于深度学习和多种机器学习算法预测人体细胞色素P450抑制剂活性

人体细胞色素P450（CYP）受到抑制会导致药物-药物相互作用，从而产生严重的不良反应。因此，准确预测给定化合物对特定CYP亚型的抑制能力至关重要。本研究基于不同的分子表征，比较了11种机器学习方法和2种深度学习模型，实验结果表明，基于RDKit_2d + Morgan的CatBoost机器学习模型在准确率和马修斯系数方面优于其他模型，甚至优于先前发表的模型。此外，实验结果还显示，CatBoost模型不仅性能佳，而且计算资源消耗较低。最后，本文将表现较好的前3名模型结合为co_model，其在性能方面稍微优于单独使用CatBoost模型。

Activity prediction of human cytochrome P450 inhibitors based on multiple deep learning and machine learning methods

Inhibition of human cytochrome P450 (CYP) can lead to drug-drug interactions, resulting in serious adverse reactions.It is therefore crucial to accurately predict the inhibitory power of a given compound against a particular CYP isoform.This study compared 11 machine learning methods and 2 deep learning models based on different molecular representations.The experimental results showed that the CatBoost machine learning model based on RDKit_2d+Morgan outperformed other models in terms of accuracy and Mathews coefficient, and even outperformed previously published models.Moreover, the experimental results also showed that the CatBoost model not only had superior performance, but also consumed less computational resources.Finally, this study combined the top 3 performing models as co_model, which slightly outperformed the CatBoost model alone in terms of performance.