Peptides are promising drug candidates due to high specificity and standout safety. Identification of bioactive peptides de novo using molecular docking is a widely used approach. However, current scoring functions are poorly optimized for peptide ligands. In this work, we present a novel algorithm PeptoGrid that rescores poses predicted by AutoDock Vina according to frequency information of ligand atoms with particular properties appearing at different positions in the target protein’s ligand binding site. We explored the relevance of PeptoGrid ranking with a virtual screening of peptide libraries using angiotensin-converting enzyme and GABAB receptor as targets. A reasonable agreement between the computational and experimental data suggests that PeptoGrid is suitable for discovering functional leads.

The aim of the study was to develop better anxiolytics and antidepressants. We focused on GABAA receptors and the α2δ auxiliary subunit of V-gated Ca2+ channels as putative targets because they are established as mediators of efficacious anxiolytics, antidepressants, and anticonvulsants. We further focused on short peptides as candidate ligands because of their high safety and tolerability profiles. We employed a structural bioinformatics approach to develop novel tetrapeptides with predicted affinity to GABAA receptors and α2δ. In silico docking studies of one of these peptides, LCGA-17, showed a high binding score for both GABAA receptors and α2δ, combined with anxiolytic-like properties in a Danio rerio behavioral screen. LCGA-17 showed anxiolytic-like effects in the novel tank test, the light–dark box, and the social preference test, with efficacy comparable to fluvoxamine and diazepam. In binding assays using rat brain membranes, [3H]-LCGA-17 was competed more effectively by gabapentinoid ligands of α2δ than ligands of GABAA receptors, suggesting that α2δ represents a likely target for LCGA-17. [3H]-LCGA-17 binding to brain lysates was unaffected by competition with ligands for GABAB, glutamate, dopamine, serotonin, and other receptors, suggesting specific interaction with α2δ. Dose-finding studies in mice using acute administration of LCGA-17 (i.p.) demonstrated anxiolytic-like effects in the open field test, elevated plus maze, and marble burying tests, as well as antidepressant-like properties in the forced swim test. The anxiolytic effects were effectively blocked by bicuculline. Therefore, LCGA-17 is a novel candidate anxiolytic and antidepressant that may act through α2δ, with possible synergism by GABAA receptors.

This microperspective covers the most recent research outcomes of artificial intelligence (AI) generated molecular structures from the point of view of the medicinal chemist. The main focus is on studies that include synthesis and experimental in vitro validation in biochemical assays of the generated molecular structures, where we analyze the reported structures’ relevance in modern medicinal chemistry and their novelty. The authors believe that this review would be appreciated by medicinal chemistry and AI-driven drug design (AIDD) communities and can be adopted as a comprehensive approach for qualifying different research outcomes in AIDD.

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The study examined the effect of an analog to N-terminal nociceptin fragment AcOH×Phe-Gly-Gly-Phe-NH(2) on the behavior of albino rats. This tetrapeptide (5 μg/kg intraperitoneally) significantly enhanced motor and exploratory activity in mature rats and in 42-day pups and produced opposite effects in 21-day rat pups, which attests to the complex dynamics of maturation of nervous structures involved in the realization of nociceptin action.