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submitted on 28.10.2020, 08:08 and posted on 30.10.2020, 07:45by Yan Yang, Yanmin Zhang, Yihang Zhang, Xingye Chen, Yi Hua, Guomeng Xing, Chenglong Deng, Li Liang, Tao Lu, Zhengda He, Yadong Chen, Haichun Liu
cardiotoxicity has become one of the major reasons leading to drug withdrawal in
past decades, which is closely related to the blockade of human
Ether-a-go-go-related gene (hERG) potassium channel. Developing reliable hERG predicting
model and optimizing model can greatly reduce the risk faced in drug discovery.
In this study, we constructed eight hERG classification models, the best of which
shows desirable generalization ability on low-similarity clinical compounds, as
well as advantages in perceiving activity gap caused by small structural
changes. Furthermore, we developed a hERG optimizer based on fragment grow
strategy. Results reveal that after reinforcement learning, our model can provide
reasonable optimizing direction to reduce hERG toxicity, especially when hERG
risk is corresponding to lipophilicity, basicity and pi-pi interactions. We also
prove its usage in helping chemists quickly pick out core fragments and fix on
the region to be optimized. Overall, we demonstrate our model as a promising
tool for medicinal chemists in hERG optimization attempts.