Open Data Resources

This data set contains the PubChem CIDs (Compound ID), InChi (International Chemical Identifier), and the logBB experimental values of 438 chemicals. The logBB is the steady-state logarithmic ratio of brain-plasma concentration for a chemical.

Details regarding the aggregation and curation of this data set can be found in the following paper: 

• Wang, W.; Kim, M. T.; Sedykh, A.; Zhu, H. Developing Enhanced Blood–Brain Barrier Permeability Models: Integrating External Bio-Assay Data in QSAR Modeling. Pharm Res 2015, 32 (9), 3055–3065. DOI: 10.1007/s11095-015-1687-1.

This data set contains the names, InChi (International Chemical Identifier), and binary values for evidence of inhibition of BCRP at a concentration of 10 uM for 395 chemicals. BCRP is an ABC (ATP-Binding Cassette) transporter responsible for the efflux of chemicals out of cells in organs like the intestines, brain, and placenta.

Details regarding the aggregation and curation of this data set can be found in the following papers:

• Sedykh, A.; Fourches, D.; Duan, J.; Hucke, O.; Garneau, M.; Zhu, H.; Bonneau, P.; Tropsha, A. Human Intestinal Transporter Database: QSAR Modeling and Virtual Profiling of Drug Uptake, Efflux and Interactions. Pharm Res 2013, 30 (4), 996–1007. DOI: 10.1007/s11095-012-0935-x.

• Zhao, L.; Wang, W.; Sedykh, A.; Zhu, H. Experimental Errors in QSAR Modeling Sets: What We Can Do and What We Cannot Do. ACS Omega 2017, 2 (6), 2805–2812. DOI: 10.1021/acsomega.7b00274.

This data set contains the names, InChi (International Chemical Identifier), and oral bioavailability (%F) values for 1070 chemicals.

Details regarding the aggregation and curation of this data set can be found in the following paper:

• Kim, M. T.; Sedykh, A.; Chakravarti, S. K.; Saiakhov, R. D.; Zhu, H. Critical Evaluation of Human Oral Bioavailability for Pharmaceutical Drugs by Using Various Cheminformatics Approaches. Pharm Res 2014, 31 (4), 1002–1014. DOI: 10.1007/s11095-013-1222-1.

This data set contains the names, InChi (International Chemical Identifier), and binary values for evidence of inhibition of BSEP at a concentration of 100 uM for 725 chemicals. BSEP is an ABC (ATP-Binding Cassette) transporter responsible for secreting bile salts from liver hepatocytes into bile.

Details regarding the aggregation and curation of this data set can be found in the following paper:

• Zhao, L.; Wang, W.; Sedykh, A.; Zhu, H. Experimental Errors in QSAR Modeling Sets: What We Can Do and What We Cannot Do. ACS Omega 2017, 2 (6), 2805–2812. DOI: 10.1021/acsomega.7b00274. 

This data set contains the CAS (Chemical Abstracts Service) numbers, InChi (International Chemical Identifier), and experimental data for developmental and reproductive toxicity for a collection of 696 chemicals.

Details regarding the aggregation and curation of this data set can be found in the following paper:

• Ciallella, H. L.; Russo, D. P.; Sharma, S.; Li, Y.; Sloter, E.; Sweet, L.; Huang, H.; Zhu, H. Predicting Prenatal Developmental Toxicity Based On the Combination of Chemical Structures and Biological Data. Environ. Sci. Technol. 2022, 56 (9), 5984–5998. DOI: 10.1021/acs.est.2c01040. 

This data set contains the InChi (International Chemical Identifier) and experimental data for estrogen receptor activity for a collection of 2036 chemicals. This data set was merged from 3 data sets that were aggregated and curated for predicting estrogen receptor activity in the following papers:

• Ciallella, H. L.; Russo, D. P.; Aleksunes, L. M.; Grimm, F. A.; Zhu, H. Predictive Modeling of Estrogen Receptor Agonism, Antagonism, and Binding Activities Using Machine- and Deep-Learning Approaches. Lab Invest 2021, 101 (4), 490–502. DOI: 10.1038/s41374-020-00477-2.

• Ciallella, H. L.; Russo, D. P.; Aleksunes, L. M.; Grimm, F. A.; Zhu, H. Revealing Adverse Outcome Pathways from Public High-Throughput Screening Data to Evaluate New Toxicants by a Knowledge-Based Deep Neural Network Approach. Environ. Sci. Technol. 2021, 55 (15), 10875–10887. DOI: 10.1021/acs.est.1c02656. 

This data set contains the names, InChi (International Chemical Identifier), and the experimental -log of concentration (uM) values representative of toxicity to fathead minnows for a collection of 674 chemicals.

Details regarding the aggregation and curation of this data set can be found in the following paper:

• Klopman, G.; Stuart, S. E. Multiple Computer-Automated Structure Evaluation Study of Aquatic Toxicity. III. Vibrio Fischeri. Environmental Toxicology and Chemistry 2003, 22 (3), 466–472. DOI: 10.1002/etc.5620220302.

• Zhao, L.; Wang, W.; Sedykh, A.; Zhu, H. Experimental Errors in QSAR Modeling Sets: What We Can Do and What We Cannot Do. ACS Omega 2017, 2 (6), 2805–2812. DOI: 10.1021/acsomega.7b00274.

This data set contains the PubChem CIDs (Compound ID), InChi (International Chemical Identifier), and experimental data for hepatotoxicity for 3709 chemicals. Details regarding the aggregation and curation of this data set can be found in the following paper:

• Mulliner, D.; Schmidt, F.; Stolte, M.; Spirkl, H.-P.; Czich, A.; Amberg, A. Computational Models for Human and Animal Hepatotoxicity with a Global Application Scope. Chem. Res. Toxicol. 2016, 29 (5), 757–767. https://doi.org/10.1021/acs.chemrestox.5b00465.

This data set contains the names, InChi (International Chemical Identifier), and binary values for evidence of inhibition of MDR1 at a concentration of 10 uM for 1585 chemicals. MDR1, also known as P-glycoprotein (P-gp), is an ABC (ATP-Binding Cassette) transporter responsible for the efflux of foreign chemicals out of cells.

Details regarding the aggregation and curation of this data set can be found in the following papers:

• Sedykh, A.; Fourches, D.; Duan, J.; Hucke, O.; Garneau, M.; Zhu, H.; Bonneau, P.; Tropsha, A. Human Intestinal Transporter Database: QSAR Modeling and Virtual Profiling of Drug Uptake, Efflux and Interactions. Pharm Res 2013, 30 (4), 996–1007. DOI: 10.1007/s11095-012-0935-x.

• Zhao, L.; Wang, W.; Sedykh, A.; Zhu, H. Experimental Errors in QSAR Modeling Sets: What We Can Do and What We Cannot Do. ACS Omega 2017, 2 (6), 2805–2812. DOI: 10.1021/acsomega.7b00274.