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MOESM5 of Methodology of aiQSAR: a group-specific approach to QSAR modelling

NIAID Data Ecosystem2026-03-11 收录

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https://figshare.com/articles/dataset/MOESM5_of_Methodology_of_aiQSAR_a_group-specific_approach_to_QSAR_modelling/7950839 数据链接链接失效反馈

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Additional file 5. EPA toxicity ranking evaluation set and predictions.

应用场景：

创建时间：

2019-04-04

药物重定位

毒力预测

Studies in Toxicity Mechanism Elucidation, Target Nomination, and Drug Repurposing Guided by Biomedical Knowledge Graphs and Chemical Bioactivity Databases.

Figshare2025-03-04 更新70

Predicting Organ Toxicity Using In Vitro Bioactivity Data and Chemical Structure: Data Associated with Publication

毒力预测

化学物质安全评估

Data associated with publication Predicting Organ Toxicity Using In Vitro Bioactivity Data and Chemical Structure. Publication DOI: http://doi.org/10.1021/acs.chemrestox.7b00084

NIAID Data Ecosystem40

Table_2_Comparison of Quantitative and Qualitative (Q)SAR Models Created for the Prediction of Ki and IC50 Values of Antitarget Inhibitors.DOCX

毒力预测

定量构效关系建模

Estimation of interaction of drug-like compounds with antitargets is important for the assessment of possible toxic effects during drug development. Publicly available online databases provide data on

NIAID Data Ecosystem20

Additional file 1 of 6D-QSAR for predicting biological activity of human aldose reductase inhibitors using quasar receptor surface modeling

激酶抑制剂活性预测

定量构效关系建模

Additional file 1. More details and information on 3D-QSAR and 6D-QSAR performance obtained for biological activities of human AR inhibitors.

Figshare2024-08-13 更新50

Biomedical Knowledge Graphs and their Applications to Scientific Discovery

药物重定位

毒力预测

Studies in Toxicity Mechanism Elucidation, Target Nomination, and Drug Repurposing Guided by Biomedical Knowledge Graphs and Chemical Bioactivity Databases.

Figshare2025-03-04 更新40

MOESM5 of Methodology of aiQSAR: a group-specific approach to QSAR modelling

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