Sankranarayanan, M. | bioRxiv Science

bioRxiv2026

Experimental validation of computationally prioritized bisphosphonates reveals no direct in vitro antiviral activity against SARS-CoV-2

It is essential to experimentally evaluate antiviral efficacy predicted by molecular docking and related in silico approaches, which this study achieves for three bisphosphonates (alendronate, minodronate, zoledronate) and treprostinil, identified as potential antiviral therapies in our previous work. These investigational compounds had no detectable cytotoxicity at concentrations up to 25M in Vero E6 cells but also failed to show any antiviral effect against the PQ.8.1 isolate of SARS-CoV-2, when compared to control drugs (ensitrelvir, nirmatrelvir, and remdesivir). It appears that for bisphosphonates, any protective effect is likely due to other virus- or host-mediated mechanisms of action. This study demonstrates the importance of iterating between theoretical hypotheses and experiments to elucidate underlying mechanisms at play.

molecular biology↗

bioRxiv2025

Machine Learning-Based Bioactivity Prediction of Potential PPAR-γ Agonists for the Management of Diabetes

This research paper presents a machine learning approach to predict bioactivity of compounds that can act as PPAR-gamma agonist, a critical target for diabetes treatment. Using data from the ChEMBL database, molecular descriptors were calculated and a Random Forest model was developed, achieving an R2 score of 0.83. Key molecular features influencing bioactivity were identified, and a web application was created for real-time predictions. This approach demonstrates how computational methods can accelerate drug discovery for diabetes treatment.

bioinformatics↗