| In silico toxicology protocols GJ Myatt, E Ahlberg, Y Akahori, D Allen, A Amberg, LT Anger, A Aptula, ... Regulatory Toxicology and Pharmacology 96, 1-17, 2018 | 196 | 2018 |
| Big data in chemical toxicity research: the use of high-throughput screening assays to identify potential toxicants H Zhu, J Zhang, MT Kim, A Boison, A Sedykh, K Moran Chemical research in toxicology 27 (10), 1643-1651, 2014 | 150 | 2014 |
| Critical evaluation of human oral bioavailability for pharmaceutical drugs by using various cheminformatics approaches MT Kim, A Sedykh, SK Chakravarti, RD Saiakhov, H Zhu Pharmaceutical research 31, 1002-1014, 2014 | 103 | 2014 |
| Developing enhanced blood–brain barrier permeability models: integrating external bio-assay data in QSAR modeling W Wang, MT Kim, A Sedykh, H Zhu Pharmaceutical research 32, 3055-3065, 2015 | 84 | 2015 |
| Predicting opioid receptor binding affinity of pharmacologically unclassified designer substances using molecular docking CR Ellis, NL Kruhlak, MT Kim, EG Hawkins, L Stavitskaya PloS one 13 (5), e0197734, 2018 | 79 | 2018 |
| Mechanism profiling of hepatotoxicity caused by oxidative stress using antioxidant response element reporter gene assay models and big data MT Kim, R Huang, A Sedykh, W Wang, M Xia, H Zhu Environmental health perspectives 124 (5), 634-641, 2016 | 70 | 2016 |
| Predicting chemical ocular toxicity using a combinatorial QSAR approach R Solimeo, J Zhang, M Kim, A Sedykh, H Zhu Chemical research in toxicology 25 (12), 2763-2769, 2012 | 48 | 2012 |
| Evaluating kratom alkaloids using PHASE CR Ellis, R Racz, NL Kruhlak, MT Kim, AV Zakharov, N Southall, ... PloS one 15 (3), e0229646, 2020 | 46 | 2020 |
| Predictive modeling of estrogen receptor binding agents using advanced cheminformatics tools and massive public data K Ribay, MT Kim, W Wang, D Pinolini, H Zhu Frontiers in environmental science 4, 12, 2016 | 43 | 2016 |
| Design, synthesis and experimental validation of novel potential chemopreventive agents using random forest and support vector machine binary classifiers B Sprague, Q Shi, MT Kim, L Zhang, A Sedykh, E Ichiishi, H Tokuda, ... Journal of computer-aided molecular design 28, 631-646, 2014 | 34 | 2014 |
| CIIPro: a new read-across portal to fill data gaps using public large-scale chemical and biological data DP Russo, MT Kim, W Wang, D Pinolini, S Shende, J Strickland, ... Bioinformatics 33 (3), 464-466, 2017 | 33 | 2017 |
| Assessing the structural and pharmacological similarity of newly identified drugs of abuse to controlled substances using public health assessment via structural evaluation CR Ellis, R Racz, NL Kruhlak, MT Kim, EG Hawkins, DG Strauss, ... Clinical Pharmacology & Therapeutics 106 (1), 116-122, 2019 | 25 | 2019 |
| Transitioning to composite bacterial mutagenicity models in ICH M7 (Q) SAR analyses C Landry, MT Kim, NL Kruhlak, KP Cross, R Saiakhov, S Chakravarti, ... Regulatory Toxicology and Pharmacology 109, 104488, 2019 | 24 | 2019 |
| In silico approaches in organ toxicity hazard assessment: current status and future needs in predicting liver toxicity A Bassan, VM Alves, A Amberg, LT Anger, S Auerbach, L Beilke, ... Computational Toxicology 20, 100187, 2021 | 22 | 2021 |
| Assessing the impact of expert knowledge on ICH M7 (Q) SAR predictions. Is expert review still needed? PS Jayasekara, SK Skanchy, MT Kim, G Kumaran, BE Mugabe, ... Regulatory Toxicology and Pharmacology 125, 105006, 2021 | 13 | 2021 |
| Curating and preparing high-throughput screening data for quantitative structure-activity relationship modeling MT Kim, W Wang, A Sedykh, H Zhu High-Throughput Screening Assays in Toxicology, 161-172, 2016 | 13 | 2016 |
| Development of QSAR models to predict blood-brain barrier permeability S Faramarzi, MT Kim, DA Volpe, KP Cross, S Chakravarti, L Stavitskaya Frontiers in Pharmacology 13, 1040838, 2022 | 10 | 2022 |
| Computers instead of cells: computational modeling of chemical toxicity H Zhu, M Kim, L Zhang, A Sedykh | 6 | 2013 |
| New QSAR models for predicting drug-induced liver injury with enhanced sensitivity RD Saiakhov, NL Kruhlak, L Stavitskaya, MT Kim, S Chakravarti INTERNATIONAL JOURNAL OF TOXICOLOGY 38 (1), 68-69, 2019 | 1 | 2019 |
| Developing advanced rules and tools to improve in vivo QSAR models MT Kim Rutgers University-Camden Graduate School, 2016 | 1 | 2016 |
