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Methods in Molecular Biology (Clifton, N.J.)
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August 13, 2016
CurveP Method for Rendering High-Throughput Screening Dose-Response Data into Digital Fingerprints
Alexander Sedykh
Chemistry (Weinheim an Der Bergstrasse, Germany)
|
April 3, 2026
[Ti(py)<sub>4</sub>I<sub>2</sub>] as an Entry Point to Titanium(II) Chemistry via Titanium Metal Oxidation
Alexander Sedykh
Methods in Molecular Biology (Clifton, N.J.)
|
March 16, 2022
CurveP Method for Rendering High-Throughput Screening Dose-Response Data into Digital Fingerprints
Alexander Sedykh
Journal of Applied Toxicology : JAT
|
May 4, 2013
Hybrid in silico models for drug-induced liver injury using chemical descriptors and in vitro cell-imaging information
Xiang-Wei Zhu, Alexander Sedykh, Shu-Shen Liu
ACS Omega
|
July 11, 2017
Experimental Errors in QSAR Modeling Sets: What We Can Do and What We Cannot Do
Linlin Zhao, Wenyi Wang, Alexander Sedykh, et al.
Pharmaceutical Research
|
April 12, 2015
Developing Enhanced Blood-Brain Barrier Permeability Models: Integrating External Bio-Assay Data in QSAR Modeling
Wenyi Wang, Marlene T Kim, Alexander Sedykh, et al.
Methods in Molecular Biology (Clifton, N.J.)
|
August 13, 2016
Curating and Preparing High-Throughput Screening Data for Quantitative Structure-Activity Relationship Modeling
Marlene T Kim, Wenyi Wang, Alexander Sedykh, et al.
Nature Communications
|
May 21, 2020
Construction of a web-based nanomaterial database by big data curation and modeling friendly nanostructure annotations
Xiliang Yan, Alexander Sedykh, Wenyi Wang, et al.
Chemical Research in Toxicology
|
November 15, 2012
Predicting chemical ocular toxicity using a combinatorial QSAR approach
Renee Solimeo, Jun Zhang, Marlene Kim, et al.
Drug Safety
|
June 8, 2018
Predicting Adverse Drug Effects from Literature- and Database-Mined Assertions
Mary K La, Alexander Sedykh, Denis Fourches, et al.
Page
of 5
Search research articles
Search
Showing results (1-10 of 50) with videos related to
Sort By:
Page
of 5
Methods in Molecular Biology (Clifton, N.J.)
|
August 13, 2016
CurveP Method for Rendering High-Throughput Screening Dose-Response Data into Digital Fingerprints
Alexander Sedykh
Chemistry (Weinheim an Der Bergstrasse, Germany)
|
April 3, 2026
[Ti(py)<sub>4</sub>I<sub>2</sub>] as an Entry Point to Titanium(II) Chemistry via Titanium Metal Oxidation
Alexander Sedykh
Methods in Molecular Biology (Clifton, N.J.)
|
March 16, 2022
CurveP Method for Rendering High-Throughput Screening Dose-Response Data into Digital Fingerprints
Alexander Sedykh
Journal of Applied Toxicology : JAT
|
May 4, 2013
Hybrid in silico models for drug-induced liver injury using chemical descriptors and in vitro cell-imaging information
Xiang-Wei Zhu, Alexander Sedykh, Shu-Shen Liu
ACS Omega
|
July 11, 2017
Experimental Errors in QSAR Modeling Sets: What We Can Do and What We Cannot Do
Linlin Zhao, Wenyi Wang, Alexander Sedykh, et al.
Pharmaceutical Research
|
April 12, 2015
Developing Enhanced Blood-Brain Barrier Permeability Models: Integrating External Bio-Assay Data in QSAR Modeling
Wenyi Wang, Marlene T Kim, Alexander Sedykh, et al.
Methods in Molecular Biology (Clifton, N.J.)
|
August 13, 2016
Curating and Preparing High-Throughput Screening Data for Quantitative Structure-Activity Relationship Modeling
Marlene T Kim, Wenyi Wang, Alexander Sedykh, et al.
Nature Communications
|
May 21, 2020
Construction of a web-based nanomaterial database by big data curation and modeling friendly nanostructure annotations
Xiliang Yan, Alexander Sedykh, Wenyi Wang, et al.
Chemical Research in Toxicology
|
November 15, 2012
Predicting chemical ocular toxicity using a combinatorial QSAR approach
Renee Solimeo, Jun Zhang, Marlene Kim, et al.
Drug Safety
|
June 8, 2018
Predicting Adverse Drug Effects from Literature- and Database-Mined Assertions
Mary K La, Alexander Sedykh, Denis Fourches, et al.
Page
of 5