Quantitative Structure-Price Relationship (QS$R) Modeling and the Development of Economically Feasible Drug Discovery Projects
| Autor: | Fuqiang Ban, Valery V. Fokin, Michael Fernandez, Godwin Woo, Carl Perez, Alexander Tropsha, Oleksandr Isaev, Artem Cherkasov |
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| Rok vydání: | 2019 |
| Předmět: |
Quantitative structure–activity relationship
Models Statistical 010304 chemical physics Computer science Drug discovery General Chemical Engineering Bioactive molecules Cheminformatics Commerce Quantitative structure General Chemistry Library and Information Sciences Commercial Sources 01 natural sciences 0104 chemical sciences Computer Science Applications 010404 medicinal & biomolecular chemistry Pharmaceutical Preparations On demand 0103 physical sciences Drug Discovery Feasibility Studies Biochemical engineering |
| Zdroj: | Journal of chemical information and modeling. 59(4) |
| ISSN: | 1549-960X |
| Popis: | In recent years, the field of quantitative structure-activity/property relationship (QSAR/QSPR) modeling has developed into a stable technology capable of reliably predicting new bioactive molecules. With the availability of inexpensive commercial sources of both synthetic chemicals and bioactivity assays, a cheminformatics-savvy scientist can readily establish a virtual drug discovery enterprise. A skilled computational chemist can not only develop a computer-aided drug discovery pipeline but also acquire or have the drug candidates made inexpensively for economical screening of desired on-target activity, critical off-target effects, and essential drug-likeness properties. As part of our drug discovery pipeline, a novel machine-learning model was built to relate chemical structures of synthetically accessible molecules to their prices. The model was trained from our "in stock" and "made on demand" diverse chemical entities, ranging in price from $20 to$10,000. This novel model is encoded here as the quantitative structure-price relationship (QS$R) model. |
| Databáze: | OpenAIRE |
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