Strategies for Identifying Molecules of Interest in Large Chemical Spaces

Abstract

In recent years make-on-demand compound libraries (so-called Chemical Spaces) have gained more and more interest in pharmaceutical industry. Compound vendors promise cheap compounds, fast delivery, high synthetical accessibility and a large pool of novel chemical matter, fulfilling the requirements of fast Design-Make-Test cycles. Searching in ultralarge Chemical Spaces with known 2D similarity metrics, like fingerprint-based Tanimoto, substructure, or pharmacophore similarity searches, contains pitfalls due to the representation of molecules as synthons with connectivity rules. Applied to a set of almost 3000 drug-relevant queries we analyzed the ability of similarity search methods to retrieve analog compounds from Chemical Spaces, and how to best approach typical use cases in early phase drug discovery. Distinct characteristics of each similarity metric suggest orthogonal complementarity, enabling a versatile framework to diverse challenges present in hit discovery and lead expansion campaigns. Our investigations resulted in formulating practical considerations and guidelines for interpreting the scores including recommendations for thresholds for each similarity metric.