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Literature DB >> 24635517

The ChEMBL database: a taster for medicinal chemists.

George Papadatos¹, John P Overington.

Abstract

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Substances：

Year: 2014 PMID： 24635517 DOI： 10.4155/fmc.14.8

Source DB: PubMed Journal: Future Med Chem ISSN： 1756-8919 Impact factor: 3.808

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19 in total

1. Thermodynamic Proxies to Compensate for Biases in Drug Discovery Methods.

Authors: Sean Ekins; Nadia K Litterman; Christopher A Lipinski; Barry A Bunin
Journal: Pharm Res Date: 2015-08-27 Impact factor: 4.200

2. QSAR Modeling Using Large-Scale Databases: Case Study for HIV-1 Reverse Transcriptase Inhibitors.

Authors: Olga A Tarasova; Aleksandra F Urusova; Dmitry A Filimonov; Marc C Nicklaus; Alexey V Zakharov; Vladimir V Poroikov
Journal: J Chem Inf Model Date: 2015-06-29 Impact factor: 4.956

3. Making Transporter Models for Drug-Drug Interaction Prediction Mobile.

Authors: Sean Ekins; Alex M Clark; Stephen H Wright
Journal: Drug Metab Dispos Date: 2015-07-21 Impact factor: 3.922

4. Docking to multiple pockets or ligand fields for screening, activity prediction and scaffold hopping.

Authors: Yu-Chen Chen; Max Totrov; Ruben Abagyan
Journal: Future Med Chem Date: 2014 Impact factor: 3.808

5. Data Mining and Computational Modeling of High-Throughput Screening Datasets.

Authors: Sean Ekins; Alex M Clark; Krishna Dole; Kellan Gregory; Andrew M Mcnutt; Anna Coulon Spektor; Charlie Weatherall; Nadia K Litterman; Barry A Bunin
Journal: Methods Mol Biol Date: 2018

Review 6. Artificial intelligence and machine-learning approaches in structure and ligand-based discovery of drugs affecting central nervous system.

Authors: Vertika Gautam; Anand Gaurav; Neeraj Masand; Vannajan Sanghiran Lee; Vaishali M Patil
Journal: Mol Divers Date: 2022-07-11 Impact factor: 3.364

The ChEMBL database: a taster for medicinal chemists.

1. Thermodynamic Proxies to Compensate for Biases in Drug Discovery Methods.

2. QSAR Modeling Using Large-Scale Databases: Case Study for HIV-1 Reverse Transcriptase Inhibitors.

3. Making Transporter Models for Drug-Drug Interaction Prediction Mobile.

4. Docking to multiple pockets or ligand fields for screening, activity prediction and scaffold hopping.

5. Data Mining and Computational Modeling of High-Throughput Screening Datasets.

Review 6. Artificial intelligence and machine-learning approaches in structure and ligand-based discovery of drugs affecting central nervous system.

7. Predicting Mouse Liver Microsomal Stability with "Pruned" Machine Learning Models and Public Data.

8. A promising drug candidate for the treatment of glaucoma based on a P2Y6-receptor agonist.

9. Activity, assay and target data curation and quality in the ChEMBL database.

10. Open Source Bayesian Models. 1. Application to ADME/Tox and Drug Discovery Datasets.