Pros and cons of virtual screening based on public 'Big Data': In silico mining for new bromodomain inhibitors
| Autor: | Yurii S. Moroz, O. V. Vasylchenko, Alexandre Varnek, Anastasiia Gryniukova, Dragos Horvath, Petro Borysko, Jürgen Bajorath, Kateryna A. Tolmachova, Iuri Casciuc |
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| Přispěvatelé: | Unité de Glycobiologie Structurale et Fonctionnelle UMR 8576 (UGSF), Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Recherche Agronomique (INRA), Institut de Chimie de Strasbourg, Université Louis Pasteur - Strasbourg I-Centre National de la Recherche Scientifique (CNRS), Unité de Glycobiologie Structurale et Fonctionnelle - UMR 8576 (UGSF), Chimie de la matière complexe (CMC), Université de Strasbourg (UNISTRA)-Institut de Chimie du CNRS (INC)-Centre National de la Recherche Scientifique (CNRS), Centre National de la Recherche Scientifique (CNRS)-Université Louis Pasteur - Strasbourg I-Institut de Chimie du CNRS (INC) |
| Rok vydání: | 2019 |
| Předmět: |
In silico
Big data Drug Evaluation Preclinical Cell Cycle Proteins [CHIM.THER]Chemical Sciences/Medicinal Chemistry Ligands Machine learning computer.software_genre 01 natural sciences Machine Learning Structure-Activity Relationship 03 medical and health sciences Reaxys Drug Discovery Data Mining Humans Computer Simulation Hit selection ComputingMilieux_MISCELLANEOUS 030304 developmental biology Pharmacology 0303 health sciences Virtual screening 010405 organic chemistry Chemistry business.industry Drug discovery Organic Chemistry Nuclear Proteins General Medicine chEMBL 0104 chemical sciences Hit rate Artificial intelligence business computer [CHIM.CHEM]Chemical Sciences/Cheminformatics Transcription Factors |
| Zdroj: | European Journal of Medicinal Chemistry European Journal of Medicinal Chemistry, Elsevier, 2019, 165, pp.258-272. ⟨10.1016/j.ejmech.2019.01.010⟩ |
| ISSN: | 0223-5234 1768-3254 |
| DOI: | 10.1016/j.ejmech.2019.01.010 |
| Popis: | International audience; The Virtual Screening (VS) study described herein aimed at detecting novel Bromodomain BRD4 binders and relied on knowledge from public databases (ChEMBL, REAXYS) to establish a battery of predictive models of BRD activity for in silico selection of putative ligands. Beyond the actual discovery of new BRD ligands, this represented an opportunity to practically estimate the actual usefulness of public domain "Big Data" for robust predictive model building. Obtained models were used to virtually screen a collection of 2 million compounds from the Enamine company collection. This industrial partner then experimentally screened a subset of 2992 molecules selected by the VS procedure for their high likelihood to be active. Twenty nine confirmed hits were detected after experimental testing, representing 1% of the selected candidates. As a general conclusion, this study emphasizes once more that public structure-activity databases are nowadays key assets in drug discovery. Their usefulness is however limited by the state-of-the-art knowledge harvested so far by published studies. Target-specific structure activity information is rarely rich enough, and its heterogeneity makes it extremely difficult to exploit in rational drug design. Furthermore, published affinity measures serving to build models selecting compounds to be experimentally screened may not be well correlated with the experimental hit selection criterion (in practice, often imposed by equipment constraints). Nevertheless, a robust 2.6-fold increase in hit rate with respect to an equivalent, random screening campaign showed that machine learning is able to extract some real knowledge in spite of all the noise in structure-activity data. |
| Databáze: | OpenAIRE |
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