| DeepConv-DTI: Prediction of drug-target interactions via deep learning with convolution on protein sequences I Lee, J Keum, H Nam PLoS computational biology 15 (6), e1007129, 2019 | 378 | 2019 |
| Artificial intelligence in drug discovery: a comprehensive review of data-driven and machine learning approaches H Kim, E Kim, I Lee, B Bae, M Park, H Nam Biotechnology and Bioprocess Engineering 25, 895-930, 2020 | 74 | 2020 |
| Identification of drug-target interaction by a random walk with restart method on an interactome network I Lee, H Nam BMC bioinformatics 19, 9-18, 2018 | 53 | 2018 |
| Sequence-based prediction of protein binding regions and drug–target interactions I Lee, H Nam Journal of cheminformatics 14 (1), 5, 2022 | 32 | 2022 |
| AMP‐BERT: Prediction of antimicrobial peptide function based on a BERT model H Lee, S Lee, I Lee, H Nam Protein Science 32 (1), e4529, 2023 | 16 | 2023 |
| BayeshERG: a robust, reliable and interpretable deep learning model for predicting hERG channel blockers H Kim, M Park, I Lee, H Nam Briefings in Bioinformatics 23 (4), bbac211, 2022 | 8 | 2022 |
| AI-based prediction of new binding site and virtual screening for the discovery of novel P2X3 receptor antagonists KM Kang, I Lee, H Nam, YC Kim European Journal of Medicinal Chemistry 240, 114556, 2022 | 6 | 2022 |
| Evaluation of large language models for discovery of gene set function M Hu, S Alkhairy, I Lee, RT Pillich, R Bachelder, T Ideker, D Pratt Research Square, 2023 | 4 | 2023 |
| Infusing linguistic knowledge of SMILES into chemical language models I Lee, H Nam arXiv preprint arXiv:2205.00084, 2022 | 2 | 2022 |
| G2PT: Mechanistic genotype-phenotype translation using hierarchical transformers I Lee, S Park, H Nam, T Ideker Cancer Research 84 (6_Supplement), 7383-7383, 2024 | | 2024 |
