Daniel P. Russo

发表

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

Hao Zhu, Wenyi Wang, Sunil M. Shende, 2016, Bioinform..

Data mining and machine learning for lysosomal disease drug discovery and beyond

Alex M. Clark, Sean Ekins, Daniel P. Russo, 2019 .

Toward Good Read-Across Practice (GRAP)

Jie Shen, Hao Zhu, Mark T. D. Cronin, 2016 .

t4 report*: Toward Good Read-Across Practice (GRAP) Guidance

Hao Zhu, Mark T. D. Cronin, Alexandra Maertens, 2016, ALTEX.

Universal nanohydrophobicity predictions using virtual nanoparticle library

Hao Zhu, Wenyi Wang, Xiaoli Zhao, 2019, Journal of Cheminformatics.

Analysis of Draize Eye Irritation Testing and its Prediction by Mining Publicly Available 2008–2014 REACH Data

Hao Zhu, Alexandra Maertens, Thomas Hartung, 2016, ALTEX.

Global Analysis of Publicly Available Safety Data for 9,801 Substances Registered under REACH from 2008–2014

Daniel P. Russo, Hao Zhu, T. Luechtefeld, 2016, ALTEX.

t4 report: Toward Good Read-Across Practice (GRAP) Guidance

Alexandra Maertens, Nicole Kleinstreuer, Thomas Hartung, 2016, ALTEX.

Prediction of Nano–Bio Interactions through Convolutional Neural Network Analysis of Nanostructure Images

Daniel P. Russo, Bing Yan, Xiliang Yan, 2020, ACS Sustainable Chemistry & Engineering.

Predicting Prenatal Developmental Toxicity Based On the Combination of Chemical Structures and Biological Data.

Daniel P. Russo, Hao Zhu, Heng Huang, 2022, Environmental science & technology.

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.

Virtual molecular projections and convolutional neural networks for end-to-end modeling of nanoparticle activities and properties.

Heng Huang, Daniel P. Russo, Bing Yan, 2020, Analytical chemistry.

Analysis of Publically Available Skin Sensitization Data from REACH Registrations 2008–2014

Alexandra Maertens, Thomas Hartung, Thomas Luechtefeld, 2016, ALTEX.

Nonanimal Models for Acute Toxicity Evaluations: Applying Data-Driven Profiling and Read-Across

Thomas Hartung, Judy Strickland, Daniel P. Russo, 2019, Environmental health perspectives.

Analysis of Public Oral Toxicity Data from REACH Registrations 2008–2014

Alexandra Maertens, Thomas Hartung, Thomas Luechtefeld, 2016, ALTEX.

Comparison of Deep Learning With Multiple Machine Learning Methods and Metrics Using Diverse Drug Discovery Datasets

Daniel P. Russo, S. Ekins, A. Korotcov, 2017 .

A simple and efficient seed-based approach to induce callus production from B73 maize genotype

Daniel P. Russo, Pacôme A. Noumavo, S. Kotchoni, 2012 .

Integrating structure annotation and machine learning approaches to develop graphene toxicity models.

Daniel P. Russo, Heng Huang, P. Demokritou, 2022, Carbon.

Discovery of Novel Tricyclic Thiazepine Derivatives as Anti-Drug-Resistant Cancer Agents by Combining Diversity-Oriented Synthesis and Converging Screening Approach.

Daniel P. Russo, Hao Zhu, B. Yan, 2016, ACS combinatorial science.

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.

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

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

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

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

In silico approaches in organ toxicity hazard assessment: current status and future needs in predicting liver toxicity.

Daniel P. Russo, Marlene T. Kim, Vinicius M. Alves, 2021, Computational toxicology.

A simple and efficient seed-based approach to induce callus production from B 73 maize genotype

Daniel P. Russo, Pacôme A. Noumavo, S. Kotchoni, 2013 .

CATMoS: Collaborative Acute Toxicity Modeling Suite

Kimberley M. Zorn, Daniel P. Russo, Timothy E. H. Allen, 2021, Environmental health perspectives.

The Role of Blm Helicase in Homologous Recombination, Gene Conversion Tract Length, and Recombination Between Diverged Sequences in Drosophila melanogaster

Daniel P. Russo, Joseph T. Brooks, Jeannine R. LaRocque, 2017, Genetics.

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

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

Construction of a Virtual Opioid Bioprofile: A Data-Driven QSAR Modeling Study to Identify New Analgesic Opioids.

Daniel P. Russo, Hao Zhu, Linlin Zhao, 2021, ACS sustainable chemistry & engineering.

In silico approaches in organ toxicity hazard assessment: Current status and future needs for predicting heart, kidney and lung toxicities.

Daniel P. Russo, Vinicius M. Alves, G. Myatt, 2021, Computational toxicology.

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.

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

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

Aggregation and analysis of secondary pharmacology data from investigational new drug submissions at the US Food and Drug Administration.

Daniel P. Russo, Rebecca Racz, M. Saulnier, 2021, Journal of pharmacological and toxicological methods.

Data-Driven Quantitative Structure–Activity Relationship Modeling for Human Carcinogenicity by Chronic Oral Exposure

Daniel P. Russo, Hao Zhu, L. Aleksunes, 2023, Environmental science & technology.

Mechanism-driven modeling of chemical hepatotoxicity using structural alerts and an in vitro screening assay.

Daniel P. Russo, Hao Zhu, X. Wen, 2022, Journal of hazardous materials.

Revealing Adverse Outcome Pathways from Public High-Throughput Screening Data to Evaluate New Toxicants by a Knowledge-Based Deep Neural Network Approach.

Daniel P. Russo, Fabian A. Grimm, Hao Zhu, 2021, Environmental science & technology.

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.

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

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

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.

Erratum: CATMoS: Collaborative Acute Toxicity Modeling Suite

Stephen P. Boyd, Kimberley M. Zorn, Daniel P. Russo, 2021, Environmental health perspectives.

Predictive modeling of angiotensin I-converting enzyme (ACE) inhibitory peptides using various machine learning approaches.

Daniel P. Russo, Hao Zhu, Chang Liu, 2020, Journal of agricultural and food chemistry.

Good Read-Across Practice (GRAP) guidance

Daniel P. Russo, E. Kroese, T. Hartung, 2016 .

Predicting Chemical Immunotoxicity through Data-Driven QSAR Modeling of Aryl Hydrocarbon Receptor Agonism and Related Toxicity Mechanisms

Daniel P. Russo, Hao Zhu, Elena Chung, 2024, Environment & health.

Integrating Concentration-Dependent Toxicity Data and Toxicokinetics To Inform Hepatotoxicity Response Pathways

Katy O Goyak, Daniel P. Russo, L. Aleksunes, 2023, Environmental science & technology.