Quantitative Structure–Price Relationship (QS$R) Modeling and the Development of Economically Feasible Drug Discovery Projects

• Published in: Journal of chemical information and modeling Vol. 59; no. 4; pp. 1306 - 1313
• Main Authors: Fernandez, Michael, Ban, Fuqiang, Woo, Godwin, Isaev, Oleksandr, Perez, Carl, Fokin, Valery, Tropsha, Alexander, Cherkasov, Artem
• Format: Journal Article
• Language: English
• Published: United States American Chemical Society 22-04-2019
• Subjects: Biological activity; Coding; Economic models; Machine learning; Organic chemistry; Project feasibility
• ISSN: 1549-9596; 1549-960X
• DOI: 10.1021/acs.jcim.8b00747

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.