New coumaryl-thiazole derivatives with the acetamide moiety as a linker between the alkyl chains and/or the heterocycle nucleus were synthesized and in vitro tested as acetylcholinesterase (AChE) inhibitors. 2-(diethylamino)-N-(4-(2-oxo-2H-chromen-3-yl)thiazol-2-yl)acetamide (6c, IC₅₀ value of 43 nM) was the best AChE inhibitor with a selectivity index of 4151.16 over BuChE. Kinetic study of AChE inhibition revealed that 6c was a mixed-type inhibitor. Moreover, the result of H4IIE hepatoma cell toxicity assay for 6c showed negligible cell death. Molecular docking studies were also carried out to clarify the inhibition mode of the more active compounds. Best pose of compound 6c is positioned into the active site with the coumarin ring wedged between the residues of the CAS and catalytic triad of AChE. In addition, the coumarin ring is anchored into the gorge of the enzyme by H-bond with Tyr130.

The expansion of the chemical space to tangible libraries containing billions of synthesizable molecules opens exciting opportunities for drug discovery, but also challenges the power of computer-aided drug design to prioritize the best candidates. This directly hits quantum mechanics (QM) methods, which provide chemically accurate properties, but subject to small-sized systems. Preserving accuracy while optimizing the computational cost is at the heart of many efforts to develop high-quality, efficient QM-based strategies, reflected in refined algorithms and computational approaches. The design of QM-tailored physics-based force fields and the coupling of QM with machine learning, in conjunction with the computing performance of supercomputing resources, will enhance the ability to use these methods in drug discovery. The challenge is formidable, but we will undoubtedly see impressive advances that will define a new era.

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.