Deep-learning based repurposing of FDA-approved drugs against dihydrofolate reductase and molecular dynamics study.

causes the fatal fungal bloodstream infection in humans called Candidiasis. Most of the species are resistant to the antifungals used to treat them. Drug-resistant poses very serious public health issues. To overcome this, the development of effective drugs with novel mechanism(s) of action is requisite. Drug repurposing is considered a viable alternative approach to overcome the above issue. In the present study, we have attempted to identify drugs that could target the essential enzyme, dihydrofolate reductase of (DHFR) to find out potent and selective antifungal antifolates. FDA-approved-drug-library from the Selleck database containing 1930 drugs was screened against DHFR using deep-learning, molecular docking, X-score and similarity search methods. The screened compounds showing better binding with DHFR were subjected to molecular dynamics simulation (MDS). The results of post-MDS analysis like RMSD, RMSF, RG, SASA, the number of hydrogen bonds and PCA suggest that Paritaprevir, Lumacaftor and Rifampin can make good interaction with DHFR. Furthermore, analysis of binding free energy corroborated the stability of interactions as they had binding energy of -114.91 kJ mol, -79.22 kJ mol and -78.52 kJ mol for Paritaprevir, Lumacaftor and Rifampin respectively as compared to the reference (-63.10 kJ mol). From the results, we conclude that these drugs have great potential to inhibit DHFR and would add to the drug discovery against candidiasis, and hence these drugs for repurposing should be explored further.