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hERG-Att: Self-Attention-Based Deep Neural Network for Predicting hERG Blockers

Kim Hyunho, Nam Hojung
2020 Computational biology and chemistry  
Then, we developed a precise and interpretable hERG blocker prediction model by using deep learning with a self-attention approach that has an appropriate molecular descriptor, Morgan fingerprint.  ...  In this study, we developed the first, attention-based, interpretable model that predicts hERG blockers and captures important hERG-related compound substructures.  ...  Figure 1 shows the interpretable self-attention-based deep neural network constructed in this study.  ... 
doi:10.1016/j.compbiolchem.2020.107286 pmid:32531518 fatcat:2epje6ftercwjpkd4bgsu4ub7y

Artificial Intelligence in Drug Discovery: A Comprehensive Review of Data-driven and Machine Learning Approaches

Hyunho Kim, Eunyoung Kim, Ingoo Lee, Bongsung Bae, Minsu Park, Hojung Nam
2020 Biotechnology and Bioprocess Engineering  
This review provides a comprehensive, organized summary of the recent research trends in AI-guided drug discovery process including target identification, hit identification, ADMET prediction, lead optimization  ...  Since artificial intelligence (AI) is leading the fourth industrial revolution, AI can be considered as a viable solution for unstable drug research and development.  ...  Neither ethical approval nor informed consent was required for this study.  ... 
doi:10.1007/s12257-020-0049-y pmid:33437151 pmcid:PMC7790479 fatcat:wqdmkkas2nb65gy3pymlgisuwi