Background: During the current SARS-CoV-2 pandemic, the identification of effective antiviral drugs is crucial. Unfortunately, no specific treatment or vaccine is available to date. Objective: Here, we aimed to predict the interactions between SARS-CoV-2 proteins and protein targets from the human body for some flavone molecules (kaempferol, morin, pectolinarin, myricitrin, and herbacetin) in comparison to synthetic compounds (hydroxychloroquine, remdesivir, ribavirin, ritonavir, AMD-070, favipiravir). Methods: Using MOE software and advanced bioinformatics and cheminformatics portals, we conducted an extensive analysis based on various structural and functional features of compounds, such as their amphiphilic field, flexibility, and steric features. The structural similarity analysis of natural and synthetic compounds was performed using Tanimoto coefficients. The interactions of some compounds with SARS-CoV-2 3CLprotease or RNA-dependent RNA polymerase were described using 2D protein-ligand interaction diagrams based on known crystal structures. The potential targets of considered compounds were identified using the SwissTargetPrediction web tool. Results: Our results showed that remdesivir, pectolinarin, and ritonavir present a strong structural similarity which may be correlated to their similar biological activity. As common molecular targets of compounds in the human body, ritonavir, kaempferol, morin, and herbacetin can activate multidrug resistance-associated proteins, while remdesivir, ribavirin, and pectolinarin appear as ligands for adenosine receptors. Conclusion: Our evaluation recommends remdesivir, pectolinarin, and ritonavir as promising anti-SARS-CoV-2 agents.

Alzheimer’s disease is a neurodegenerative disorder incompatible with normal daily activity, affecting one in nine people. One of its potential targets is the apelin receptor (APJR), a G-protein coupled receptor, which presents considerably high expression levels in the central nervous system. In silico studies of APJR drug-like molecule binding are in small numbers while high throughput screenings (HTS) are already sufficiently many to devise efficient drug design strategies. This presents itself as an opportunity to optimize different steps in future large scale virtual screening endeavours. Here, we ran a first stage docking simulation against a library of 95 known binders and 3829 generated decoys in an effort to improve the rescoring stage. We then analyzed receptor binding site structure and ligands binding poses to describe their interactions. As a result, we devised a simple and straightforward virtual screening Stage II filtering score based on search space extension followed by a geometric estimation of the ligand—binding site fitness. Having this score, we used an ensemble of receptors generated by Hamiltonian Monte Carlo simulation and reported the results. The improvements shown herein prove that our ensemble docking protocol is suited for APJR and can be easily extrapolated to other GPCRs.