Abstract
BackgroundDespite continued efforts using chemical similarity methods in virtual screening, currently developed approaches suffer from time-consuming multistep procedures and low success rates. We recently developed a machine learning-based chemical binding similarity model considering common structural features from molecules binding to the same, or evolutionarily related targets. The chemical binding similarity measures the resemblance of chemical compounds in terms of binding site similarity to better describe functional similarities that arise from target binding. In this study, we have shown how the chemical binding similarity could be used in virtual screening together with the conventional structure-based methods.ResultsThe chemical binding similarity, receptor-based pharmacophore, chemical structure similarity, and molecular docking methods were evaluated to identify an effective virtual screening procedure for desired target proteins. When we tested the chemical binding similarity method with test sets of 51 kinases, it outperformed the traditional structural similarity-based methods as well as structure-based methods, such as molecular docking and receptor-based pharmacophore modeling, in terms of finding active compounds. We further validated the results by performing virtual screening (using the chemical binding similarity and receptor-based pharmacophore methods) against a completely blind dataset for mitogen-activated protein kinase kinase 1 (MEK1), ephrin type-B receptor 4 (EPHB4) and wee1-like protein kinase (WEE1). The in vitro kinase binding assay confirmed that 6 out of 13 (46.2%) for MEK1 and 2 out of 12 (16.7%) for EPHB4 were newly identified only by the chemical binding similarity model.ConclusionsWe report that the virtual screening results could further be improved by combining the chemical binding similarity model with 3D-QSAR pharmacophore and molecular docking models. Not only the new inhibitors are identified in this study, but also many of the identified molecules have low structural similarity scores against already reported inhibitors and that show the revelation of novel scaffolds.
Highlights
Despite continued efforts using chemical similarity methods in virtual screening, currently developed approaches suffer from time-consuming multistep procedures and low success rates
Even though several tools are available for target prediction and drug identification, most of the methods are based on the structural similarity of chemical compounds that often fail to represent the functional biological activity derived from a specific combination of local spatial features [11]
We recently developed a machine learning-based chemical similarity model referred to as a target-specific ensemble evolutionary chemical binding similarity (TS-ensECBS) model that was designed to measure the probability that chemical compounds bind to identical targets [17, 18]
Summary
Despite continued efforts using chemical similarity methods in virtual screening, currently developed approaches suffer from time-consuming multistep procedures and low success rates. As an alternative to the tedious, expensive, and time-consuming experimental screening procedure, computational methods that can screen the vast amount of chemical compounds (virtual chemical library) have become an indispensable tool in the early stage of drug discovery [2]. Similarity Ensemble Approach [6] and SuperPred [7] are web based target prediction tools that use the 2D fingerprint similarity principle to compare the input molecules to available ligands (with target information) in the database. Even though several tools are available for target prediction and drug identification, most of the methods are based on the structural similarity of chemical compounds that often fail to represent the functional biological activity derived from a specific combination of local spatial features [11]. Consideration of only structural similarities based on single target binding molecules may not be suitable for VS as the term activity cliffs are referred to compounds with high structural similarity but high activity differences [12]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
