Hole filling and library optimization: Application to commercially available fragment libraries

Abstract

Compound libraries comprise an integral component of drug discovery in the pharmaceutical and biotechnology industries. While in-house libraries often contain millions of molecules, this number pales in comparison to the accessible space of drug-like molecules. Therefore, care must be taken when adding new compounds to an existing library in order to ensure that unexplored regions in the chemical space are filled efficiently while not needlessly increasing the library size. In this work, we present an automated method to fill holes in an existing library using compounds from an external source and apply it to commercially available fragment libraries. The method, called Canvas HF, uses distances computed from 2D chemical fingerprints and selects compounds that fill vacuous regions while not suffering from the problem of selecting only compounds at the edge of the chemical space. We show that the method is robust with respect to different databases and the number of requested compounds to retrieve. We also present an extension of the method where chemical properties can be considered simultaneously with the selection process to bias the compounds toward a desired property space without imposing hard property cutoffs. We compare the results of Canvas HF to those obtained with a standard sphere exclusion method and with random compound selection and find that Canvas HF performs favorably. Overall, the method presented here offers an efficient and effective hole-filling strategy to augment compound libraries with compounds from external sources. The method does not have any fit parameters and therefore it should be applicable in most hole-filling applications.