Biased Complement Diversity Selection for Effective Exploration of Chemical Space in Hit-Finding Campaigns
| Autor: | Mika Lindvall, Susan Fong, Charles Wartchow, Gianfranco De Pascale, Johanna M. Jansen, Keith B. Pfister, Heinz E. Moser, Bob Warne |
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| Rok vydání: | 2019 |
| Předmět: |
Computer science
General Chemical Engineering media_common.quotation_subject Plasmodium falciparum Drug Evaluation Preclinical Library and Information Sciences 01 natural sciences Workflow Set (abstract data type) Small Molecule Libraries Antimalarials 0103 physical sciences Drug Discovery Gram-Negative Bacteria Selection (linguistics) Animals Humans Quality (business) Protein Interaction Maps Malaria Falciparum Function (engineering) media_common Complement (set theory) 010304 chemical physics General Chemistry Data science Chemical space 0104 chemical sciences Computer Science Applications Anti-Bacterial Agents High-Throughput Screening Assays 010404 medicinal & biomolecular chemistry Gram-Negative Bacterial Infections Diversity (business) |
| Zdroj: | Journal of chemical information and modeling. 59(5) |
| ISSN: | 1549-960X |
| Popis: | The success of hit-finding campaigns relies on many factors, including the quality and diversity of the set of compounds that is selected for screening. This paper presents a generalized workflow that guides compound selections from large compound archives with opportunities to bias the selections with available knowledge in order to improve hit quality while still effectively sampling the accessible chemical space. An optional flag in the workflow supports an explicit complement design function where diversity selections complement a given core set of compounds. Results from three project applications as well as a literature case study exemplify the effectiveness of the approach, which is available as a KNIME workflow named Biased Complement Diversity (BCD). |
| Databáze: | OpenAIRE |
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