Abstract

The challenges of drug discovery from hit identification to clinical development sometimes involve addressing scaffold hopping issues, in order to optimize biological activity or ADME properties, improve selectivity or mitigate toxicology concerns of a drug candidate, not to mention intellectual property reasons. Docking is usually viewed as the method of choice for identification of isofunctional molecules, i.e. highly dissimilar molecules that share common binding modes with a protein target. However, in cases where the protein structure has low resolution or is unknown, docking may not be suitable. In such cases, ligand-based approaches offer promise but are often inadequate to handle large-step scaffold hopping, because they usually rely on the molecular structure. Therefore, we propose the Interaction Fingerprints Profile (IFPP), a molecular representation that captures molecules binding modes based on docking experiments against a panel of diverse high-quality protein structures. Evaluation on the Large-Hops (LH) benchmark demonstrates the utility of IFPP for identification of isofunctional molecules. Nevertheless, computation of IFPPs is expensive, which limits the scalability for screening very large molecular libraries. We propose to overcome this limitation by leveraging Metric Learning approaches, allowing fast estimation of molecules IFPP similarities, thus providing an efficient pre-screening strategy applicable to very large molecular libraries. Overall, our results suggest that IFPP provides an interesting and complementary tool alongside existing methods, in order to address challenging scaffold hopping problems effectively in drug discovery.