A physicochemical descriptor-based scoring scheme for effective and rapid filtering of kinase-like chemical space

Abstract

BackgroundThe current chemical space of known small molecules is estimated to exceed 1060 structures. Though the largest physical compound repositories contain only a few tens of millions of unique compounds, virtual screening of databases of this size is still difficult. In recent years, the application of physicochemical descriptor-based profiling, such as Lipinski's rule-of-five for drug-likeness and Oprea's criteria of lead-likeness, as early stage filters in drug discovery has gained widespread acceptance. In the current study, we outline a kinase-likeness scoring function based on known kinase inhibitors.ResultsThe method employs a collection of 22,615 known kinase inhibitors from the ChEMBL database. A kinase-likeness score is computed using statistical analysis of nine key physicochemical descriptors for these inhibitors. Based on this score, the kinase-likeness of four publicly and commercially available databases, i.e., National Cancer Institute database (NCI), the Natural Products database (NPD), the National Institute of Health's Molecular Libraries Small Molecule Repository (MLSMR), and the World Drug Index (WDI) database, is analyzed. Three of these databases, i.e., NCI, NPD, and MLSMR are frequently used in the virtual screening of kinase inhibitors, while the fourth WDI database is for comparison since it covers a wide range of known chemical space. Based on the kinase-likeness score, a kinase-focused library is also developed and tested against three different kinase targets selected from three different branches of the human kinome tree.ConclusionsOur proposed methodology is one of the first that explores how the narrow chemical space of kinase inhibitors and its relevant physicochemical information can be utilized to build kinase-focused libraries and prioritize pre-existing compound databases for screening. We have shown that focused libraries generated by filtering compounds using the kinase-likeness score have, on average, better docking scores than an equivalent number of randomly selected compounds. Beyond library design, our findings also impact the broader efforts to identify kinase inhibitors by screening pre-existing compound libraries. Currently, the NCI library is the most commonly used database for screening kinase inhibitors. Our research suggests that other libraries, such as MLSMR, are more kinase-like and should be given priority in kinase screenings.

Identification of Novel Compounds for Human Bitter Taste Receptors

“…In light of the elusive properties of bitter chemicals, there is a need to identify additional characteristics involved in the development of bitter compounds to better understand the relationship between ligands and receptors. A propertybased approach has been widely used in the ligand design of a certain target, especially in the field of drug discovery (35)(36)(37)(38). Lipinski et al (39) analyzed 2245 compounds based on molecular descriptors from the WDI and proposed a rule of five to limit the ranges for successful drug screening.…”

Computational View toward the Inhibition of SARS-CoV-2 Spike Glycoprotein and the 3CL Protease

“…All calculations were operated on Dell PowerEdge C6220 servers. The chemical structures were prepared by AutoDockTools-1.5.6 [ 8 ], Chimera 1.14 [ 9 ], and Avogadro [ 10 ]. The docking studies were performed with Autodock 4.2.6, Autodock4, AutoDockTools4 [ 11 ], and Autodock Vina 1.1.2 [ 12 ].…”

HerbiPAD: a free web platform to comprehensively analyze constitutive property and herbicide‐likeness to estimate chemical bioavailability

“…Evaluating herbicide-likeness with strict restrictive rules may lead to many potential candidate molecules being excluded. We therefore used three quantitative evaluation methods: QEH, 23 GAU, 34 and RDL. 22 We adopted a scoring function based on that of Avram et al 23 QEH provides a simple yet effective approach for integrating multiple numerical parameters measured on different scales into a single dimensionless and continuous score.…”