| Benchmarking spliced alignment programs including Spaln2, an extended version of Spaln that incorporates additional species-specific features H Iwata, O Gotoh Nucleic acids research 40 (20), e161-e161, 2012 | 213 | 2012 |
| CGBVS‐DNN: Prediction of Compound‐protein Interactions Based on Deep Learning M Hamanaka, K Taneishi, H Iwata, J Ye, J Pei, J Hou, Y Okuno Molecular informatics 36 (1-2), 1600045, 2017 | 80 | 2017 |
| kGCN: a graph-based deep learning framework for chemical structures R Kojima, S Ishida, M Ohta, H Iwata, T Honma, Y Okuno Journal of Cheminformatics 12, 1-10, 2020 | 79 | 2020 |
| Systematic drug repositioning for a wide range of diseases with integrative analyses of phenotypic and molecular data H Iwata, R Sawada, S Mizutani, Y Yamanishi Journal of chemical information and modeling 55 (2), 446-459, 2015 | 79 | 2015 |
| Target-based drug repositioning using large-scale chemical–protein interactome data R Sawada, H Iwata, S Mizutani, Y Yamanishi Journal of chemical information and modeling 55 (12), 2717-2730, 2015 | 73 | 2015 |
| Elucidating the modes of action for bioactive compounds in a cell-specific manner by large-scale chemically-induced transcriptomics M Iwata, R Sawada, H Iwata, M Kotera, Y Yamanishi Scientific reports 7 (1), 40164, 2017 | 71 | 2017 |
| Structure-Based de Novo Molecular Generator Combined with Artificial Intelligence and Docking Simulations B Ma, K Terayama, S Matsumoto, Y Isaka, Y Sasakura, H Iwata, M Araki, ... Journal of Chemical Information and Modeling 61 (7), 3304-3313, 2021 | 49 | 2021 |
| Comparative analysis of information contents relevant to recognition of introns in many species H Iwata, O Gotoh BMC genomics 12, 1-17, 2011 | 46 | 2011 |
| Large-scale prediction of beneficial drug combinations using drug efficacy and target profiles H Iwata, R Sawada, S Mizutani, M Kotera, Y Yamanishi Journal of chemical information and modeling 55 (12), 2705-2716, 2015 | 39 | 2015 |
| The apoptotic initiator caspase-8: its functional ubiquity and genetic diversity during animal evolution K Sakamaki, K Shimizu, H Iwata, K Imai, Y Satou, N Funayama, M Nozaki, ... Molecular biology and evolution 31 (12), 3282-3301, 2014 | 37 | 2014 |
| Inferring protein domains associated with drug side effects based on drug-target interaction network H Iwata, S Mizutani, Y Tabei, M Kotera, S Goto, Y Yamanishi BMC systems biology 7, 1-11, 2013 | 33 | 2013 |
| Machine learning accelerates MD-based binding pose prediction between ligands and proteins K Terayama, H Iwata, M Araki, Y Okuno, K Tsuda Bioinformatics 34 (5), 770-778, 2018 | 31 | 2018 |
| Prediction of total drug clearance in humans using animal data: proposal of a multimodal learning method based on deep learning H Iwata, T Matsuo, H Mamada, T Motomura, M Matsushita, T Fujiwara, ... Journal of Pharmaceutical Sciences 110 (4), 1834-1841, 2021 | 27 | 2021 |
| Predicting total drug clearance and volumes of distribution using the machine learning-mediated multimodal method through the imputation of various nonclinical data H Iwata, T Matsuo, H Mamada, T Motomura, M Matsushita, T Fujiwara, ... Journal of Chemical Information and Modeling 62 (17), 4057-4065, 2022 | 22 | 2022 |
| Classification of scanning electron microscope images of pharmaceutical excipients using deep convolutional neural networks with transfer learning H Iwata, Y Hayashi, A Hasegawa, K Terayama, Y Okuno International Journal of Pharmaceutics: X 4, 100135, 2022 | 20 | 2022 |
| Hydrophobic interactions at subsite S1′ of human dipeptidyl peptidase IV contribute significantly to the inhibitory effect of tripeptides M Araki, N Kanegawa, H Iwata, Y Sagae, K Ito, K Masuda, Y Okuno Heliyon 6 (6), 2020 | 17 | 2020 |
| Identification of a new class of non-electrophilic TRPA1 agonists by a structure-based virtual screening approach M Araki, N Kanda, H Iwata, Y Sagae, K Masuda, Y Okuno Bioorganic & Medicinal Chemistry Letters 30 (11), 127142, 2020 | 13 | 2020 |
| An in silico Approach for Integrating Phenotypic and Target‐based Approaches in Drug Discovery H Iwata, R Kojima, Y Okuno Molecular informatics 39 (1-2), 1900096, 2020 | 12 | 2020 |
| Improving the Accuracy of Protein‐Ligand Binding Mode Prediction Using a Molecular Dynamics‐Based Pocket Generation Approach M Araki, H Iwata, B Ma, A Fujita, K Terayama, Y Sagae, F Ono, K Tsuda, ... Journal of computational chemistry 39 (32), 2679-2689, 2018 | 10 | 2018 |
| Conservation of structure and function in vertebrate c-FLIP proteins despite rapid evolutionary change K Sakamaki, N Iwabe, H Iwata, K Imai, C Takagi, K Chiba, C Shukunami, ... Biochemistry and Biophysics Reports 3, 175-189, 2015 | 9 | 2015 |
