Improving Synthetic Efficiency Using the Computational Prediction of Biological Activity

Abstract

A process has been developed whereby libraries of compounds for lead optimization can be synthesized and screened with greater efficiency using computational tools. In this method, analogues of a lead chemical structure are considered in the form of a virtual library. Less than 1/3 of the library is selected as a training set by clustering the compounds and choosing the centroid of each cluster. This training set is then used to generate a model using PLS regression upon the experimental values from that assay using 1D/2D descriptors. The model is applied to the remaining compounds (the test set) for which assay values are predicted and a rank ordering established. An example of this was a set of 169 PDE4 inhibitors. A predictive model was achieved using a training set of 52 compounds. When applied to the remaining 117 compounds this model allowed a rank ordering of these compounds for synthesis and testing. Selecting the top 33 compounds of the test set gives 78% of the compounds with the desired activity (hits) by synthesizing only 50% of the library, including the training set. Selecting the top 59 of the test set gives 97% of the hits from only 67% of the library. This process succeeds by avoiding two principal weaknesses of 2D descriptors: lack of interpretation and lack of extrapolation. Two principal assumptions of QSAR are shown to be unnecessary; removing descriptor redundancy does not improve fit and a predictive r2 greater than 0.5 is not necessary if rank-ordering is desired.

A New Structure-Based QSAR Method Affords both Descriptive and Predictive Models for Phosphodiesterase-4 Inhibitors

“…Even though these models provided preliminary pharmacophoric insights into PDE-4 inhibitors, the absence of the 3D structure of the target enzyme (i.e., PDE-4) in these models poses a major limitation on the reliability and interpretability of these models. Other traditional 2D QSAR analyses often result in only statistical models with little to offer in terms of structural insights that the medicinal chemist can use to optimize the inhibitors [12, 17]. Thus, it is highly desirable to develop structure-based, both descriptive and predictive models.…”