Daniel P Russo

发表

Exploiting machine learning for end-to-end drug discovery and development

Sean Ekins, Ana C Puhl, Kimberley M Zorn, 2019, Nature Materials.

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

Hao Zhu, Sean Ekins, Kimberley M Zorn, 2018, Molecular pharmaceutics.

Multiple Machine Learning Comparisons of HIV Cell-based and Reverse Transcriptase Data Sets.

Sean Ekins, Kimberley M Zorn, Thomas R Lane, 2019, Molecular pharmaceutics.

Machine Learning Models for Estrogen Receptor Bioactivity and Endocrine Disruption Prediction.

Sean Ekins, Kimberley M Zorn, Thomas R Lane, 2020, Environmental science & technology.

Comparison of Deep Learning With Multiple Machine Learning Methods and Metrics Using Diverse Drug Discovery Data Sets.

Sean Ekins, Daniel P Russo, Alexandru Korotcov, 2017, Molecular pharmaceutics.

Comparing and Validating Machine Learning Models for Mycobacterium tuberculosis Drug Discovery.

Sean Ekins, Kimberley M Zorn, Daniel P Russo, 2018, Molecular pharmaceutics.

Accessing the High-Throughput Screening Data Landscape.

Hao Zhu, Daniel P Russo, 2016, Methods in molecular biology.

Predictive Modeling of Estrogen Receptor Agonism, Antagonism, and Binding Activities Using Machine and Deep Learning Approaches

Daniel P Russo, Daniel P. Russo, Heather L Ciallella, 2020, Laboratory Investigation.

Predicting Nano-Bio Interactions by Integrating Nanoparticle Libraries and Quantitative Nanostructure Activity Relationship Modeling.

Daniel P Russo, Daniel P. Russo, Hainan Sun, 2017, ACS nano.

Mechanism-driven Read-Across of Chemical Hepatotoxicants Based on Chemical Structures and Biological Data.

Daniel P Russo, Daniel P. Russo, Linlin Zhao, 2020, Toxicological sciences : an official journal of the Society of Toxicology.

CIIPro: a new read-across portal to fill data gaps using public large-scale chemical and biological data.

Daniel P Russo, Thomas Hartung, Judy Strickland, 2016, Bioinformatics.