Bridging Chemical and Biological Space:  “Target Fishing” Using 2D and 3D Molecular Descriptors

Abstract

Bridging chemical and biological space is the key to drug discovery and development. Typically, cheminformatics methods operate under the assumption that similar chemicals have similar biological activity. Ideally then, one could predict a drug's biological function(s) given only its chemical structure by similarity searching in libraries of compounds with known activities. In practice, effectively choosing a similarity metric is case dependent. This work compares both 2D and 3D chemical descriptors as tools for predicting the biological targets of ligand probes, on the basis of their similarity to reference molecules in a 46,000 compound, biologically annotated chemical database. Overall, we found that the 2D methods employed here outperform the 3D (88% vs 67% success) in correct target prediction. However, the 3D descriptors proved superior in cases of probes with low structural similarity to other compounds in the database (singletons). Additionally, the 3D method (FEPOPS) shows promise for providing pharmacophoric alignment of the small molecules' chemical features consistent with those seen in experimental ligand/ receptor complexes. These results suggest that querying annotated chemical databases with a systematic combination of both 2D and 3D descriptors will prove more effective than employing single methods.

Developing Best Practices for Descriptor‐Based Property Prediction: Appropriate Matching of Datasets, Descriptors, Methods, and Expectations

“…The practical use of such information is easily exemplified in the century-old idea of representing lock and key or induced fit enzyme-substrate binding via pharmacophores: Molecular frameworks that carry essential features responsible for a drugs biological activity. A 3D molecular fingerprint representation of pharmacophores would thus indicate the presence or absence of groups of atoms that define these features, for example, aromatic, lipophilic, hydrogen bond donor/acceptor, and so on [43]. Such groups can be defined by topological (in 2D) or geometric (in 3D) distance, based on whole molecules or molecule fragments, such as the popular FEPOPS descriptors [44].…”

Role of Natural Products in Drug Discovery

“…These include diversity, biological relevance, and compound quality [121][122][123][124][125]. Retrospective analyses with regard to druglikeness, lead-likeness, or biological relevance may help to evaluate and increase the quality of a compound library [126][127][128][129][130][131][132][133]. NPs can be seen as one particular group of compounds endowed with special properties and biological relevance, since NPs have been selected during evolution to bind to proteins, for example, during biosynthesis or while fulfilling their biological role as a second messenger, hormone, or venom [134][135][136].…”

Knowledge‐Based and Computational Approaches to In Vitro Safety Pharmacology

“…For example, Cleves and Jain demonstrated the predictive ability of 3D morphological descriptors [64]. While 2D descriptors are powerful for similarity searching in annotated databases, 3D descriptors may be more appropriate when the orphan compound has low 2D similarity to all database molecules [65]. In addition to chemical similarity searching for target fishing, target ontologies may be exploited to find new targets for compounds.…”