An overview of molecular fingerprint similarity search in virtual screening

Abstract

Fingerprint similarity search methods are especially useful in VS if only a few unrelated ligands are known for a given target and therefore more complex and information rich methods such as pharmacophore searches or structure-based design are not applicable. In addition, fingerprint methods are used in characterizing properties of compound collections such as chemical diversity, density in chemical space, and content of biologically active molecules (biodiversity). Such assessments are important for deciding what compounds to experimentally screen, to purchase, or to assemble in a virtual compound deck for in silico screening or de novo design.

The Symbiotic Relationship Between Drug Discovery and Organic Chemistry

“…Connectivity-or substructure-based descriptors are typically molecular fingerprints obtainedb yanumerical representation of certain connectivity paths or substructural features of the molecule. [35] In particular,topological fingerprints (e.g.,Daylight or Chemaxonf ingerprints) capturet he pathso fm olecular features (usually atoms)l inearly up to ag iven number of connecting bonds. Each path identified is then transformed into ab it string pattern by using the hash function (i.e.,am athematical function used to map any data set of arbitrary size into an umerical data set of fixed size), and the resulting stringsa re then combinedb yl ogicalc onjunction (i.e.,A ND operation) to give the targetf ingerprint ( Figure 6).…”

“…[39] Descriptors based on the concept of the pharmacophore (i.e.,t he pattern of features of am olecule responsible for its biological effect [40] )a ssess molecular similarity,i nt erms of the presenceo ra bsence of pharmacophoric features, such as positive/negativec harges,H Don/HAcc, and aromatic or hydrophobic moieties. Typically,t heir topological or spatiala rrangement is also taken into account; [34,35,41,42] hence, most pharmacophore-based descriptors are hybrids. Pharmacophore fingerprints are also common (both 2D and 3D);t ypically, they focus on three-or four-point pharmacophoric feature combinations and record all patterns of these features and topological or Euclidean distances between them.…”

“…Other types of molecular descriptors used to accessm olecular diversity can be mentioned here,i np articular,p hysicochemicalp roperty or bioactivity-based descriptors, [34,35] for example,B ayes affinity fingerprints.T he latter are based on circular fingerprints,b ut also include results obtained from ab iological target prediction model based on activity data extracted from the ChEMBL database. [34] Along with the molecular descriptor,a nother important component of the diversity measurement is distance metrics.…”

Targeting against HIV/HCV Co-infection using Machine Learning-based multitarget-quantitative structure-activity relationships (mt-QSAR) Methods

“…MACCS is a common 2D molecular fingerprint descriptor, composed of 166 structural keys. And most of the important chemical features are covered in spite of small length[65]. Additionally, Extended-connectivity fingerprints (ECFPs) are a new kind of topological fingerprints, which were clearly developed to capture molecular features associated with molecular activity[23].…”

Enabling the hypothesis-driven prioritization of ligand candidates in big databases: Screenlamp and its application to GPCR inhibitor discovery for invasive species control

“…1), were kept ( Fig. 3); specifically, the distance between the 3-keto group and the sulfur atom in the sulfate group in these conformers was [13][14][15][16][17][18][19][20] Å. Up to 200 favorable (low) energy conformations, following clustering by OMEGA to identify distinct conformations, were retained for each of the database molecules selected by the Screenlamp filtering steps.…”