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BonMOLière: Small-Sized Libraries of Readily Purchasable Compounds, Optimized to Produce Genuine Hits in Biological Screens across the Protein Space.
Mathai, Neann; Stork, Conrad; Kirchmair, Johannes.
Afiliación
  • Mathai N; Computational Biology Unit (CBU) and Department of Chemistry, University of Bergen, N-5020 Bergen, Norway.
  • Stork C; Center for Bioinformatics (ZBH), Department of Informatics, Universität Hamburg, 20146 Hamburg, Germany.
  • Kirchmair J; Computational Biology Unit (CBU) and Department of Chemistry, University of Bergen, N-5020 Bergen, Norway.
Int J Mol Sci ; 22(15)2021 Jul 21.
Article en En | MEDLINE | ID: mdl-34360558
Experimental screening of large sets of compounds against macromolecular targets is a key strategy to identify novel bioactivities. However, large-scale screening requires substantial experimental resources and is time-consuming and challenging. Therefore, small to medium-sized compound libraries with a high chance of producing genuine hits on an arbitrary protein of interest would be of great value to fields related to early drug discovery, in particular biochemical and cell research. Here, we present a computational approach that incorporates drug-likeness, predicted bioactivities, biological space coverage, and target novelty, to generate optimized compound libraries with maximized chances of producing genuine hits for a wide range of proteins. The computational approach evaluates drug-likeness with a set of established rules, predicts bioactivities with a validated, similarity-based approach, and optimizes the composition of small sets of compounds towards maximum target coverage and novelty. We found that, in comparison to the random selection of compounds for a library, our approach generates substantially improved compound sets. Quantified as the "fitness" of compound libraries, the calculated improvements ranged from +60% (for a library of 15,000 compounds) to +184% (for a library of 1000 compounds). The best of the optimized compound libraries prepared in this work are available for download as a dataset bundle ("BonMOLière").
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Texto completo: 1 Bases de datos: MEDLINE Asunto principal: Algoritmos / Proteínas / Bibliotecas de Moléculas Pequeñas / Descubrimiento de Drogas / Ensayos Analíticos de Alto Rendimiento Tipo de estudio: Prognostic_studies Límite: Humans Idioma: En Revista: Int J Mol Sci Año: 2021 Tipo del documento: Article País de afiliación: Noruega

Texto completo: 1 Bases de datos: MEDLINE Asunto principal: Algoritmos / Proteínas / Bibliotecas de Moléculas Pequeñas / Descubrimiento de Drogas / Ensayos Analíticos de Alto Rendimiento Tipo de estudio: Prognostic_studies Límite: Humans Idioma: En Revista: Int J Mol Sci Año: 2021 Tipo del documento: Article País de afiliación: Noruega