Scaffold hopping from natural products to synthetic mimetics by holistic molecular similarity

Natural products offer unexplored molecular frameworks for the development of chemical leads and innovative drugs. However, the structural complexity of natural products compared with synthetic drug-like molecules often limits the scaffold hopping potential of natural-product-inspired molecular design. Here we introduce a holistic molecular representation incorporating pharmacophore and shape patterns, which facilitates scaffold hopping from natural products to isofunctional synthetic compounds. This computational approach captures simultaneously the partial charge, atom distributions and molecular shape. In a prospective application, we use four natural cannabinoids as queries in a chemical database search for novel synthetic modulators of human cannabinoid receptors. Of the synthetic compounds selected by the new method, 35% are experimentally confirmed as active. These cannabinoid receptor modulators are structurally less complex than their respective natural product templates. The results of this study validate this holistic molecular representation for hit and lead finding in drug discovery. The successful prediction of drug-like structures by scaffold hopping can be limited by the structural complexity of natural products. Here, a molecular descriptor which captures partial charge, atom density distributions, and molecular shape is used to predict novel active compounds which are simpler than the original natural products.