Abstract
Experimental screening of large sets of compounds against macromolecular targets is a key strategy to identify novel bioactivities. However, large-scale screening requires substantial experimental resources and is time-consuming and challenging. Therefore, small to medium-sized compound libraries with a high chance of producing genuine hits on an arbitrary protein of interest would be of great value to fields related to early drug discovery, in particular biochemical and cell research. Here, we present a computational approach that incorporates drug-likeness, predicted bioactivities, biological space coverage, and target novelty, to generate optimized compound libraries with maximized chances of producing genuine hits for a wide range of proteins. The computational approach evaluates drug-likeness with a set of established rules, predicts bioactivities with a validated, similarity-based approach, and optimizes the composition of small sets of compounds towards maximum target coverage and novelty. We found that, in comparison to the random selection of compounds for a library, our approach generates substantially improved compound sets. Quantified as the “fitness” of compound libraries, the calculated improvements ranged from +60% (for a library of 15,000 compounds) to +184% (for a library of 1000 compounds). The best of the optimized compound libraries prepared in this work are available for download as a dataset bundle (“BonMOLière”).
Highlights
We show the capacity of the new computational approach by generating a set of optimized compound libraries (“BonMOLière”) of different sizes from a subset of the
We present a multi-step, computational approach for the design of small to medium-sized compound libraries that have a maximized likelihood of producing genuine hits in biological assays for an arbitrary target of interest
The hits identified by screening these compound libraries could serve as valuable tool compounds in biochemical and cell research, and some of them may prove to be valid starting points for the development of drugs
Summary
A key strategy to identify bioactive compounds for biomacromolecules of interest is to screen large collections of compounds with biochemical or cell-based assays [1]. The success of such screening campaigns depends on many factors, above all, the quality and composition of the compound library: the much-cited “needle in the haystack” can only possibly be found if it is in the haystack. Focused design aims to compile a set of compounds that have an increased likelihood of being active on a particular target of interest [4,5,6,7].
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