Predictions of Chromatography Methods by Chemical Structure Similarity to Accelerate High-Throughput Medicinal Chemistry

Abstract

We introduce a new workflow that relies heavily on chemical quantitative structure-retention relationship (QSRR) models to accelerate method development for micro/mini-scale highthroughput purification (HTP). This provides faster access to new active pharmaceutical ingredients (APIs) through high-throughput experimentation (HTE). By comparing fingerprint structural similarity (e.g., Tanimoto index) with small training data sets containing a few hundred diverse small molecule antagonists of a lipid metabolizing enzyme, we can predict retention time (RT) of new compounds. Machine learning (ML) helps to identify optimal separation conditions for purification without performing the traditional crude QC step involving ultrahigh performance liquid chromatography (UHPLC) analyses of each compound. This greenchemistry approach with the use of predictive tools reduces cost and significantly shortens the design-make-test (DMT) cycle of new drugs by way of HTE.