Scoring of de novo Designed Chemical Entities by Macromolecular Target Prediction

Computational de novo molecular design and macromolecular target prediction have become routine in applied cheminformatics. In this study, we have generated populations of drug template-derived designs using ligand-based building block assembly, and predicted their potential targets. The results of our analysis show that the reaction-based de novo design generated new chemical entities with similar properties and pharmacophores as that of the template drugs as well as up to 44 % of the de novo compounds receiving the correct target predictions. Keeping in mind the probabilistic nature of the methods, such a combination of fast and meaningful computational structure generation by reaction-based design and product scoring by target class prediction may be appropriate for prospective application in medicinal chemistry.