Identification of Dual Inhibitors Targeting Main Protease (Mpro) and Cathepsin L as Potential Anti-SARS-CoV-2 Agents.

In this structure-activity relationship (SAR) study, we report the development of dual inhibitors with antiviral properties targeting the SARS-CoV-2 main protease (Mpro) and human cathepsin L (hCatL). The novel molecules differ in the aliphatic amino acids at the P2 site and the fluorine position on the phenyl ring at the P3 site. The identified dual inhibitors showed Ki values within 1.61 and 10.72 µM against SARS-CoV-2 Mpro; meanwhile, Ki values ranging from 0.004 to 0.701 µM toward hCatL were observed. A great interdependency between the nature of the side chain at the P2 site and the position of the fluorine atom was found. Three dual-targeting inhibitors exhibited antiviral activity in the low micromolar range with CC50 values >100 µM. Docking simulations were executed to gain a deeper understanding of the SAR profile. The findings herein collected should be taken into consideration for the future development of dual SARS-CoV-2 Mpro/hCatL inhibitors.

Development of Novel Peptidyl Nitriles Targeting Rhodesain and Falcipain-2 for the Treatment of Sleeping Sickness and Malaria.

In recent decades, neglected tropical diseases and poverty-related diseases have become a serious health problem worldwide. Among these pathologies, human African trypanosomiasis, and malaria present therapeutic problems due to the onset of resistance, toxicity problems and the limited spectrum of action. In this drug discovery process, rhodesain and falcipain-2, of Trypanosoma brucei rhodesiense and Plasmodium falciparum, are currently considered the most promising targets for the development of novel antitrypanosomal and antiplasmodial agents, respectively. Therefore, in our study we identified a novel lead-like compound, i.e., inhibitor 2b, which we proved to be active against both targets, with a Ki = 5.06 µM towards rhodesain and an IC50 = 40.43 µM against falcipain-2.

Streamlining Large Chemical Library Docking with Artificial Intelligence: the PyRMD2Dock Approach.

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.