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In this research, we developed the initial, attention-based, interpretable model that predicts hERG blockers and catches important hERG-related compound substructures. To accomplish this, we first amassed different datasets, including community databases to publicly available personal datasets, to teach and test the model. Then, we created a precise and interpretable hERG blocker prediction model using deep discovering with a self-attention method that includes a suitable molecular descriptor, Morgan finge
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