| Advancing computational toxicology in the big data era by artificial intelligence: data-driven and mechanism-driven modeling for chemical toxicity HL Ciallella, H Zhu Chemical research in toxicology 32 (4), 536-547, 2019 | 156 | 2019 |
| Advancing computer-aided drug discovery (CADD) by big data and data-driven machine learning modeling L Zhao, HL Ciallella, LM Aleksunes, H Zhu Drug discovery today 25 (9), 1624-1638, 2020 | 143 | 2020 |
| CATMoS: collaborative acute toxicity modeling suite K Mansouri, AL Karmaus, J Fitzpatrick, G Patlewicz, P Pradeep, D Alberga, ... Environmental health perspectives 129 (4), 047013, 2021 | 82 | 2021 |
| Revealing adverse outcome pathways from public high-throughput screening data to evaluate new toxicants by a knowledge-based deep neural network approach HL Ciallella, DP Russo, LM Aleksunes, FA Grimm, H Zhu Environmental science & technology 55 (15), 10875-10887, 2021 | 33 | 2021 |
| Predictive modeling of estrogen receptor agonism, antagonism, and binding activities using machine-and deep-learning approaches HL Ciallella, DP Russo, LM Aleksunes, FA Grimm, H Zhu Laboratory investigation 101 (4), 490-502, 2021 | 32 | 2021 |
| Construction of a virtual opioid bioprofile: a data-driven QSAR modeling study to identify new analgesic opioids X Jia, HL Ciallella, DP Russo, L Zhao, MH James, H Zhu ACS sustainable chemistry & engineering 9 (10), 3909-3919, 2021 | 22 | 2021 |
| Extended stability evaluation of selected cathinones HL Ciallella, LR Rutter, LA Nisbet, KS Scott Frontiers in chemistry 8, 597726, 2020 | 20 | 2020 |
| Predicting prenatal developmental toxicity based on the combination of chemical structures and biological data HL Ciallella, DP Russo, S Sharma, Y Li, E Sloter, L Sweet, H Huang, ... Environmental science & technology 56 (9), 5984-5998, 2022 | 17 | 2022 |
| Data-Driven Quantitative Structure–Activity Relationship Modeling for Human Carcinogenicity by Chronic Oral Exposure E Chung, DP Russo, HL Ciallella, YT Wang, M Wu, LM Aleksunes, H Zhu Environmental Science & Technology 57 (16), 6573-6588, 2023 | 5 | 2023 |
| Automatic Quantitative Structure–Activity Relationship Modeling to Fill Data Gaps in High-Throughput Screening HL Ciallella, E Chung, DP Russo, H Zhu High-Throughput Screening Assays in Toxicology, 169-187, 2022 | 2 | 2022 |
| Hybrid non-animal modeling: A mechanistic approach to predict chemical hepatotoxicity E Chung, X Wen, X Jia, HL Ciallella, LM Aleksunes, H Zhu Journal of Hazardous Materials, 134297, 2024 | | 2024 |
| Predicting Developmental and Reproductive Toxicity Using Artificial Intelligence and High-Throughput Screening Data HL Ciallella Rutgers, The State University of New Jersey-Camden, 2022 | | 2022 |
| Stability of AH-7921 in polar organic solvents M Murphy, H Ciallella, K Scott ABSTRACTS OF PAPERS OF THE AMERICAN CHEMICAL SOCIETY 253, 2017 | | 2017 |
| Stability of selected cathinones in methanol and acetonitrile H Ciallella, K Scott ABSTRACTS OF PAPERS OF THE AMERICAN CHEMICAL SOCIETY 252, 2016 | | 2016 |
Hao ZhuProfessor of Biomedical Informatics and Genomics, Tulane University在 tulane.edu 的电子邮件经过验证
