Marlene T. Kim

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..

Design, synthesis and experimental validation of novel potential chemopreventive agents using random forest and support vector machine binary classifiers

Hao Zhu, Qian Shi, Liying Zhang, 2014, Journal of Computer-Aided Molecular Design.

Predicting chemical ocular toxicity using a combinatorial QSAR approach.

Marlene T. Kim, Alexander Sedykh, Hao Zhu, 2012, Chemical research in toxicology.

Developing advanced rules and tools to improve in vivo QSAR models

Marlene T. Kim, 2016 .

Predictive Modeling of Estrogen Receptor Binding Agents Using Advanced Cheminformatics Tools and Massive Public Data

Marlene T. Kim, Hao Zhu, Kathryn Ribay, 2016, Front. Environ. Sci..

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.

Critical Evaluation of Human Oral Bioavailability for Pharmaceutical Drugs by Using Various Cheminformatics Approaches

Marlene T. Kim, Suman K. Chakravarti, Roustem D. Saiakhov, 2013, Pharmaceutical Research.

Developing Enhanced Blood–Brain Barrier Permeability Models: Integrating External Bio-Assay Data in QSAR Modeling

Marlene T. Kim, Hao Zhu, Alexander Sedykh, 2015, Pharmaceutical Research.

Mechanism Profiling of Hepatotoxicity Caused by Oxidative Stress Using Antioxidant Response Element Reporter Gene Assay Models and Big Data

Ruili Huang, Menghang Xia, Marlene T. Kim, 2015, Environmental health perspectives.

CHAPTER 5:Computers Instead of Cells: Computational Modeling of Chemical Toxicity

Marlene T. Kim, Hao Zhu, Alexander Sedykh, 2013 .

Predicting opioid receptor binding affinity of pharmacologically unclassified designer substances using molecular docking

Marlene T. Kim, Christopher R. Ellis, N. Kruhlak, 2018, PloS one.

Evaluating kratom alkaloids using PHASE

Marlene T. Kim, Christopher R. Ellis, D. Strauss, 2020, PloS one.

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.

Development of QSAR models to predict blood-brain barrier permeability

Marlene T. Kim, K. Cross, Sadegh Faramarzi, 2022, Frontiers in Pharmacology.

Developing Enhanced Blood–Brain Barrier Permeability Models: Integrating External Bio-Assay Data in QSAR Modeling

Marlene T. Kim, Alexander Sedykh, Hao Zhu, 2015, Pharmaceutical Research.

In silico toxicology protocols.

Tudor I. Oprea, Naomi L Kruhlak, Lidiya Stavitskaya, 2018, Regulatory toxicology and pharmacology : RTP.

Curating and Preparing High-Throughput Screening Data for Quantitative Structure-Activity Relationship Modeling.

Hao Zhu, Wenyi Wang, Marlene T Kim, 2016, Methods in molecular biology.

Critical Evaluation of Human Oral Bioavailability for Pharmaceutical Drugs by Using Various Cheminformatics Approaches

Marlene T. Kim, Alexander Sedykh, Hao Zhu, 2014, Pharmaceutical Research.

Big Data in Chemical Toxicity Research: The Use of High-Throughput Screening Assays To Identify Potential Toxicants

Hao Zhu, Jun Zhang, Marlene T. Kim, 2014, Chemical research in toxicology.

Assessing the Structural and Pharmacological Similarity of Newly Identified Drugs of Abuse to Controlled Substances Using Public Health Assessment via Structural Evaluation

Christopher R Ellis, Rebecca Racz, Naomi L Kruhlak, 2019, Clinical pharmacology and therapeutics.

Transitioning to composite bacterial mutagenicity models in ICH M7 (Q)SAR analyses.

Naomi L Kruhlak, Lidiya Stavitskaya, Curran Landry, 2019, Regulatory toxicology and pharmacology : RTP.

Assessing the impact of expert knowledge on ICH M7 (Q)SAR predictions. Is expert review still needed?

Marlene T. Kim, N. Kruhlak, L. Woodard, 2021, Regulatory toxicology and pharmacology : RTP.