Virtual screening (VS) is an outstanding cornerstone in the drug discovery pipeline. A variety of computational approaches, which are generally classified as ligand-based (LB) and structure-based (SB) techniques, exploit key structural and physicochemical properties of ligands and targets to enable the screening of virtual libraries in the search of active compounds. Though LB and SB methods have found widespread application in the discovery of novel drug-like candidates, their complementary natures have stimulated continued efforts toward the development of hybrid strategies that combine LB and SB techniques, integrating them in a holistic computational framework that exploits the available information of both ligand and target to enhance the success of drug discovery projects. In this review, we analyze the main strategies and concepts that have emerged in the last years for defining hybrid LB + SB computational schemes in VS studies. Particularly, attention is focused on the combination of molecular similarity and docking, illustrating them with selected applications taken from the literature.

The pharmacophore concept is a fundamental cornerstone in drug discovery, playing a critical role in determining the success of in silico techniques, such as virtual screening and 3D-QSAR studies. The reliability of these approaches is influenced by the quality of the physicochemical descriptors used to characterize the chemical entities. In this context, a pivotal role is exerted by lipophilicity, which is a major contribution to host–guest interaction and ligand binding affinity. Several approaches have been undertaken to account for the descriptive and predictive capabilities of lipophilicity in 3D-QSAR modeling. Recent efforts encode the use of quantum mechanical-based descriptors derived from continuum solvation models, which open novel avenues for gaining insight into structure–activity relationships studies.

Different azides and alkynes have been coupled via Cu-catalyzed 1,3-dipolar Huisgen cycloaddition to afford a novel family of N1- and C5-substituted 1,2,3-triazole derivatives that feature the propargylamine group typical of irreversible MAO-B inhibitors at the C4-side chain of the triazole ring. All the synthesized compounds were evaluated against human MAO-A and MAO-B. Structure-activity relationships and molecular modeling were utilized to gain insight into the structural and chemical features that enhance the binding affinity and selectivity between the two enzyme isoforms. Several lead compounds, in terms of potency (submicromolar to low micromolar range), MAO-B selective recognition, and brain permeability, were identified. One of these leads (MAO-B IC50 of 3.54μM, selectivity MAO-A/MAO-B index of 27.7) was further subjected to reversibility and time-dependence inhibition studies, which disclosed a slow and irreversible inhibition of human MAO-B. Overall, the results support the suitability of the 4-triazolylalkyl propargylamine scaffold for exploring the design of multipotent anti-Alzheimer compounds endowed with irreversible MAO-B inhibitory activity.

The human soluble epoxide hydrolase (sEH) is a bifunctional enzyme that modulates the levels of regulatory epoxy lipids. The hydrolase activity is carried out by a catalytic triad located at the center of a wide L-shaped binding site, which contains two hydrophobic subpockets at both sides. On the basis of these structural features, it can be assumed that desolvation is a major factor in determining the maximal achievable affinity that can be attained for this pocket. Accordingly, hydrophobic descriptors may be better suited to the search of novel hits targeting this enzyme. This study examines the suitability of quantum mechanically derived hydrophobic descriptors in the discovery of novel sEH inhibitors. To this end, three-dimensional quantitative structure-activity relationship (3D-QSAR) pharmacophores were generated by combining electrostatic and steric or alternatively hydrophobic and hydrogen-bond parameters in conjunction with a tailored list of 76 known sEH inhibitors. The pharmacophore models were then validated by using two external sets chosen (i) to rank the potency of four distinct series of compounds and (ii) to discriminate actives from decoys, using in both cases datasets taken from the literature. Finally, a prospective study was performed including a virtual screening of two chemical libraries to identify new potential hits, which were subsequently experimentally tested for their inhibitory activity on human, rat, and mouse sEH. The use of hydrophobic-based descriptors led to the identification of six compounds as inhibitors of the human enzyme with IC₅₀ < 20 nM, including two with IC₅₀ values of 0.4 and 0.7 nM. The results support the use of hydrophobic descriptors as a valuable tool in the search of novel scaffolds that encode a proper hydrophilic/hydrophobic distribution complementary to the target's binding site.