Abstract:

In the dynamic realm of drug discovery, computational techniques have revolutionized the pace at which we identify potential therapeutic agents. However, the membrane permeability of these compounds, a crucial factor in their efficacy, often remains underrepresented in computational predictions. In this talk, I look at two anti-cancer drugs, Withaferin-A and Withanone.¹ Despite their structural similarities, the marked difference in their activities is believed to arise from their varying permeabilities. I’ll investigate the simulations used and explain the physical reasons for the differences in permeability along with the disadvantages of this method. A glimpse into the future of drug discovery will be offered, by way of my anticipated research: leveraging machine learning to predict the permeability of potential drug candidates, paving the way for more effective and efficient drug discovery.

1. Wadhwa, R., Yadav, N.S., Katiyar, S.P. et al. Nat Sci Rep,11, 2352 (2021)