The present contribution introduces a novel computational protocol called PyRMD2Dock, which combines the Ligand-Based Virtual Screening (LBVS) tool PyRMD with the popular docking software AutoDock-GPU (AD4-GPU) to enhance the throughput of virtual screening campaigns for drug discovery. By implementing PyRMD2Dock, we demonstrate that it is possible to rapidly screen massive chemical databases and identify those with the highest predicted binding affinity to a target protein. Our benchmarking and screening experiments illustrate the predictive power and speed of PyRMD2Dock and highlight its potential to accelerate the discovery of novel drug candidates. Overall, this study showcases the value of combining AI-powered LBVS tools with docking software to enable effective and high-throughput virtual screening of ultralarge molecular databases in drug discovery. PyRMD and the PyRMD2Dock protocol are freely available on GitHub (https://github.com/cosconatilab/PyRMD) as an open-source tool.

Neurodegenerative disorders, such as Parkinson's disease and Alzheimer's disease, constitute pathological conditions of great relevance on health span and quality of life. The identification of novel therapeutic options, able to modulate the processes involved in the insurgence and progression of neurodegenerative disorders, represents an intriguing challenge of current research. Herein, a library of 36-membered 2-(phenylamino)-7,8-dihydroquinazolinone derivatives was synthesized and biologically evaluated as human MAO inhibitors. Some compounds able to inhibit MAO-B potently and selectively (K_i in the nanomolar range) were identified, and robust structure-activity relationships were drawn, supported by computational studies. Further biological assays revealed a safe profile for all derivatives and, for compounds selected as the best MAO-B inhibitors (4, 5, 13, 14) the following properties also emerged: (i) the ability to inhibit MAO-B activity in whole cells, with an effectiveness comparable or slight lower with respect to the reference safinamide; (ii) physicochemical parameters suggesting drug-likeness properties; (iii) the ability to inhibit, albeit weakly, GSK3β kinase (for compound 4). Within the whole series, compound 4 stood out as a promising lead for future optimization campaigns aimed to obtain useful drugs for the treatment of Alzheimer's and Parkinson's diseases.

Background/Objectives: Rhodesain is a cysteine protease crucial for the life cycle of Trypanosoma brucei rhodesiense, a parasite that causes the lethal form of human African trypanosomiasis. For these reasons, rhodesain is considered an important target for the drug discovery process of novel antitrypanosomal agents. Methods: In the present work, we carried out a combination study of two novel synthetic nitriles, Nitrile 1 and Nitrile 2, with curcumin, the golden multitarget nutraceutical obtained from Curcuma longa L., which we demonstrated to inhibit rhodesain in a non-competitive manner. We calculated the combination index (CI) in both the combination studies by using the Chou and Talalay method. Results: Comparing the CI values of the combinations Nitrile 1 + curcumin and Nitrile 2 + curcumin, we assessed that the inhibitory effect of the combination Nitrile 2 + curcumin against rhodesain was much more potent than that of the other combination. At the IC₅₀ value, in the case of the combination Nitrile 1 + curcumin an additive effect occurred, while in the case of Nitrile 2 + curcumin, we observed a moderate synergism: at 99% of the effect, the synergism induced by the combination Nitrile 2 + curcumin was much stronger than the synergism promoted by the combination Nitrile 1 + curcumin (CI = 0.3843 vs 0.6622, respectively). Conclusions: The co-administration of dipeptide nitriles with curcumin enhances rhodesain inhibition through synergistic effects. Notably, Nitrile 2 + curcumin exhibits a stronger synergy at higher inhibition levels, indicating a greater therapeutic potential.