Zhu, Hao

From machine learning to deep learning: progress in machine intelligence for rational drug discovery

Zhang, L., Tan, J., Han, D., & Zhu, H. (2017) · Drug Discovery Today

Computational Drug Discovery MethodsMachine Learning in Materials Science

Cited by 760

Big data and artificial intelligence modeling for drug discovery

Zhu, H. (2020) · Annual Review of Pharmacology and Toxicology

Computational Drug Discovery MethodsMachine Learning in Materials Science

Cited by 455

Quantitative Structure−Activity Relationship Modeling of Rat Acute Toxicity by Oral Exposure

Zhu, H., Martin, T. M., Ye, L., Sedykh, A., Young, D. M., & Tropsha, A. (2009) · Chemical Research in Toxicology

Computational Drug Discovery MethodsAnimal testing and alternatives

Cited by 287

Advancing Computational Toxicology in the Big Data Era by Artificial Intelligence: Data-Driven and Mechanism-Driven Modeling for Chemical Toxicity

Ciallella, H. L., & Zhu, H. (2019) · Chemical Research in Toxicology

Computational Drug Discovery MethodsAnimal testing and alternatives

Cited by 227

Big data in chemical toxicity research: The use of high-throughput screening assays to identify potential toxicants

Zhu, H., Zhang, J., Kim, M. T., Boison, A., Sedykh, A., & Moran, K. (2014) · Chemical Research in Toxicology

Computational Drug Discovery MethodsEnvironmental Toxicology and Ecotoxicology

Cited by 154

Comparing Multiple Machine Learning Algorithms and Metrics for Estrogen Receptor Binding Prediction

Russo, D. P., Zorn, K. M., Clark, A. M., Zhu, H., & Ekins, S. (2018) · Molecular Pharmaceutics

Computational Drug Discovery MethodsMachine Learning in Materials Science

Cited by 152

Use of in Vitro HTS-Derived Concentration–Response Data as Biological Descriptors Improves the Accuracy of QSAR Models of in Vivo Toxicity

Sedykh, A., Zhu, H., Tang, H., Zhang, L., Richard, A., Rusyn, I., & Tropsha, A. (2011) · Environmental Health Perspectives

Computational Drug Discovery MethodsAnimal testing and alternatives

Cited by 119

A Deep Learning Pipeline Using Prior Knowledge for Automatic Evaluation of Placenta Accreta Spectrum Disorders With MRI

Wang, H., Wang, Y., Zhang, H., Yin, X., Wang, C., Lu, Y., Song, Y., Zhu, H., & Yang, G. (2024) · Journal of Magnetic Resonance Imaging

Maternal and fetal healthcareMedical Image Segmentation Techniques

Cited by 19