Revealing the Macromolecular Targets of Fragment‐Like Natural Products

Abstract

Fragment-like natural products were identified as ligand-efficient chemical matter for hit-to-lead development and chemical-probe discovery.R elying on ac omputational method using at opological pharmacophore descriptor and ad rug database,s everal macromolecular targets from distinct protein families were expeditiously retrieved for structurally unrelated chemotypes.T he selected fragments feature structural dissimilarity to the reference compounds and suitable target affinity,a nd they offer opportunities for chemical optimization. Experimental confirmation of hitherto unknown macromolecular targets for the selected molecules corroborate the usefulness of the computational approach and suggests broad applicability to chemical biology and molecular medicine.

Artificial intelligence and machine learning‐aided drug discovery in central nervous system diseases: State‐of‐the‐arts and future directions

“…Several studies implemented AI/ML algorithms into computational target deconvolution tools for higher predictive power. For example, Schneider and colleagues have widely applied self‐organizing maps (SOMs) to predict the macromolecular targets of compounds 94–97 . They preferred to use “fuzzy” molecular representations, such as pharmacophoric feature descriptors, since such fuzzy molecular representations demonstrated greater scaffold‐hopping potential than atomistic approaches in similarity searches.…”

“…Hence, the trained SOM was able to transfer the knowledge of annotated drug targets to query molecules that are the nearest neighbors to known drugs 94 . They have applied this SOM approach to identify the macromolecular targets of de novo‐designed molecules, 95 complex natural products, 94 fragment‐like natural products, 96 and a natural anticancer compound 97 . Besides the SOM models, a multiple‐category Naïve Bayesian model was developed for the rapid identification of potential targets for compounds based on only chemical structure information, which is the connectivity fingerprints of compounds from 964 target classes in the WOMBAT (World Of Molecular BioAcTivity) chemogenomics database 98 .…”

Current advances in ligand‐based target prediction

“…Other representative drug repositioning examples include Memantine, 16 Buprenorphine, 17 Requip, 18,19 Colesevelam, 20 and so on. Moreover, Chinese traditional medicines have been widely used for health care in many Asian countries for thousands of years, but the ambiguous targets and mechanisms have constrained their wide applications worldwide 21,22 . Due to the precision treatment initiative, it is necessary to uncover the therapeutic targets of these traditional medicines and thus breaking the caused uncertainty.…”

Allosteric Antagonist Modulation of TRPV2 by Piperlongumine Impairs Glioblastoma Progression

“…Considering the large volume of publicly available bioactivity data, statistical learning of ligand-target relationships has become tractable in recent years and amenable to providing probabilistically motivated target binding hypotheses 7, 11 . One such ligand-based target-identification program (SPiDER: self-organizing mapbased prediction of drug equivalence relationships) 11 has been validated for the deorphanization of natural products and discovery of new biology 7,12 , drug repurposing [13][14] and the unravelling of primary and secondary pharmacology 2 . Indeed, in silico technologies are a growing concept in drug discovery that may offer complementary/alternative solutions not only for target identification programs 7,14 but also discovery chemistry in general 15 .…”