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

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.