Daniel P. Russo
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View article: Comparing and Validating Machine Learning Models for Mycobacterium tuberculosis Drug Discovery
Comparing and Validating Machine Learning Models for Mycobacterium tuberculosis Drug Discovery Open
Tuberculosis is a global health dilemma. In 2016, the WHO reported 10.4 million incidences and 1.7 million deaths. The need to develop new treatments for those infected with Mycobacterium tuberculosis ( Mtb) has led to many large-scale phe…
View article: An Online Nanoinformatics Platform Empowering Computational Modeling of Nanomaterials by Nanostructure Annotations and Machine Learning Toolkits
An Online Nanoinformatics Platform Empowering Computational Modeling of Nanomaterials by Nanostructure Annotations and Machine Learning Toolkits Open
Modern nanotechnology has generated numerous datasets from in vitro and in vivo studies on nanomaterials, with some available on nanoinformatics portals. However, these existing databases lack the digital data and tools suitable for machin…
View article: Predicting Chemical Immunotoxicity through Data-Driven QSAR Modeling of Aryl Hydrocarbon Receptor Agonism and Related Toxicity Mechanisms
Predicting Chemical Immunotoxicity through Data-Driven QSAR Modeling of Aryl Hydrocarbon Receptor Agonism and Related Toxicity Mechanisms Open
Computational modeling has emerged as a time-saving and cost-effective alternative to traditional animal testing for assessing chemicals for their potential hazards. However, few computational modeling studies for immunotoxicity were repor…
View article: Integrating Concentration-Dependent Toxicity Data and Toxicokinetics To Inform Hepatotoxicity Response Pathways
Integrating Concentration-Dependent Toxicity Data and Toxicokinetics To Inform Hepatotoxicity Response Pathways Open
Failure of animal models to predict hepatotoxicity in humans has created a push to develop biological pathway-based alternatives, such as those that use in vitro assays. Public screening programs (e.g., ToxCast/Tox21 programs) have tested …
View article: Data-Driven Quantitative Structure–Activity Relationship Modeling for Human Carcinogenicity by Chronic Oral Exposure
Data-Driven Quantitative Structure–Activity Relationship Modeling for Human Carcinogenicity by Chronic Oral Exposure Open
Traditional methodologies for assessing chemical toxicity are expensive and time-consuming. Computational modeling approaches have emerged as low-cost alternatives, especially those used to develop quantitative structure-activity relations…
View article: Predicting Prenatal Developmental Toxicity Based On the Combination of Chemical Structures and Biological Data
Predicting Prenatal Developmental Toxicity Based On the Combination of Chemical Structures and Biological Data Open
For hazard identification, classification, and labeling purposes, animal testing guidelines are required by law to evaluate the developmental toxicity potential of new and existing chemical products. However, guideline developmental toxici…
View article: Erratum: CATMoS: Collaborative Acute Toxicity Modeling Suite
Erratum: CATMoS: Collaborative Acute Toxicity Modeling Suite Open
In this article, the “ Acknowledgments ” section was missing the text below: H.C., D.P.R., and H.Z. at Rutgers University at Camden were partially supported by the NIEHS (grants R01ES031080 and R15ES023148).The authors regret the error.
View article: Revealing Adverse Outcome Pathways from Public High-Throughput Screening Data to Evaluate New Toxicants by a Knowledge-Based Deep Neural Network Approach
Revealing Adverse Outcome Pathways from Public High-Throughput Screening Data to Evaluate New Toxicants by a Knowledge-Based Deep Neural Network Approach Open
Traditional experimental testing to identify endocrine disruptors that enhance estrogenic signaling relies on expensive and labor-intensive experiments. We sought to design a knowledge-based deep neural network (k-DNN) approach to reveal a…
View article: CATMoS: Collaborative Acute Toxicity Modeling Suite
CATMoS: Collaborative Acute Toxicity Modeling Suite Open
BACKGROUND: Humans are exposed to tens of thousands of chemical substances that need to be assessed for their potential toxicity. Acute systemic toxicity testing serves as the basis for regulatory hazard classification, labeling, and risk …
View article: Construction of a Virtual Opioid Bioprofile: A Data-Driven QSAR Modeling Study to Identify New Analgesic Opioids
Construction of a Virtual Opioid Bioprofile: A Data-Driven QSAR Modeling Study to Identify New Analgesic Opioids Open
Compared to traditional experimental approaches, computational modeling is a promising strategy to efficiently prioritize new candidates with low cost. In this study, we developed a novel data mining and computational modeling workflow pro…
View article: Leveraging the Value of CDISC SEND Data Sets for Cross-Study Analysis: Incidence of Microscopic Findings in Control Animals
Leveraging the Value of CDISC SEND Data Sets for Cross-Study Analysis: Incidence of Microscopic Findings in Control Animals Open
Implementation of the Clinical Data Interchange Standards Consortium (CDISC)'s Standard for Exchange of Nonclinical Data (SEND) by the United States Food and Drug Administration Center for Drug Evaluation and Research (US FDA CDER) has cre…
View article: Machine Learning Models for Estrogen Receptor Bioactivity and Endocrine Disruption Prediction
Machine Learning Models for Estrogen Receptor Bioactivity and Endocrine Disruption Prediction Open
The U.S. Environmental Protection Agency (EPA) periodically releases in vitro data across a variety of targets, including the estrogen receptor (ER). In 2015, the EPA used these data to construct mathematical models of ER agonist and antag…
View article: Mechanism-Driven Read-Across of Chemical Hepatotoxicants Based on Chemical Structures and Biological Data
Mechanism-Driven Read-Across of Chemical Hepatotoxicants Based on Chemical Structures and Biological Data Open
Hepatotoxicity is a leading cause of attrition in the drug development process. Traditional preclinical and clinical studies to evaluate hepatotoxicity liabilities are expensive and time consuming. With the advent of critical advancements …
View article: Nonanimal Models for Acute Toxicity Evaluations: Applying Data-Driven Profiling and Read-Across
Nonanimal Models for Acute Toxicity Evaluations: Applying Data-Driven Profiling and Read-Across Open
The in vitro bioassay data-driven profiling strategy developed in this study meets the urgent needs of computational toxicology in the current big data era and can be extended to develop predictive models for other complex toxicity end poi…
View article: Multiple Machine Learning Comparisons of HIV Cell-based and Reverse Transcriptase Data Sets
Multiple Machine Learning Comparisons of HIV Cell-based and Reverse Transcriptase Data Sets Open
The human immunodeficiency virus (HIV) causes over a million deaths every year and has a huge economic impact in many countries. The first class of drugs approved were nucleoside reverse transcriptase inhibitors. A newer generation of reve…
View article: MOESM4 of Universal nanohydrophobicity predictions using virtual nanoparticle library
MOESM4 of Universal nanohydrophobicity predictions using virtual nanoparticle library Open
Additional file 5. The original GNP library input file for the demo.
View article: MOESM3 of Universal nanohydrophobicity predictions using virtual nanoparticle library
MOESM3 of Universal nanohydrophobicity predictions using virtual nanoparticle library Open
Additional file 4. Summary table of the GNP library in the training and validation sets.
View article: Comparing Multiple Machine Learning Algorithms and Metrics for Estrogen Receptor Binding Prediction
Comparing Multiple Machine Learning Algorithms and Metrics for Estrogen Receptor Binding Prediction Open
Many chemicals that disrupt endocrine function have been linked to a variety of adverse biological outcomes. However, screening for endocrine disruption using in vitro or in vivo approaches is costly and time-consuming. Computational metho…
View article: Comparing and Validating Machine Learning Models for <i>Mycobacterium tuberculosis</i> Drug Discovery
Comparing and Validating Machine Learning Models for <i>Mycobacterium tuberculosis</i> Drug Discovery Open
Tuberculosis is a global health dilemma. In 2016, the WHO reported 10.4 million incidences and 1.7 million deaths. The need to develop new treatments for those infected with Mycobacterium tuberculosis ( Mtb) has led to many large-scale phe…
View article: Predicting Nano–Bio Interactions by Integrating Nanoparticle Libraries and Quantitative Nanostructure Activity Relationship Modeling
Predicting Nano–Bio Interactions by Integrating Nanoparticle Libraries and Quantitative Nanostructure Activity Relationship Modeling Open
The discovery of biocompatible or bioactive nanoparticles for medicinal applications is an expensive and time-consuming process that may be significantly facilitated by incorporating more rational approaches combining both experimental and…