Design, synthesis, and in vitro and in silico study of 1-benzyl-indole hybrid thiosemicarbazones as competitive tyrosinase inhibitors.

Developing new anti-tyrosinase drugs seems crucial for the medical and industrial fields since irregular melanin synthesis is linked to the resurgence of several skin conditions, including melanoma, and the browning of fruits and vegetables. A novel series of N-1 and C-3 substituted indole-based thiosemicarbazones 5(a-r) are synthesized and further analyzed for their inhibition potential against tyrosinase enzyme through in vitro assays. The synthesized compounds displayed very good to moderate inhibition with half maximal inhibitory concentration in the range of 12.40 ± 0.26 µM to 47.24 ± 1.27 µM. Among all the derivatives 5k displayed the highest inhibitory activity. According to SAR analysis, the derivatives with 4-substitution at the benzyl or phenyl ring of the thiosemicarbazones exhibited better inhibitory potential against tyrosinase. In silico analysis (including ADMET prediction and molecular docking) was conducted and compared with the standard drug (kojic acid). These findings may help the hunt for new melanogenesis inhibitors that the food and cosmetics industries may find valuable.

Revisiting methotrexate and phototrexate Zinc15 library-based derivatives using deep learning in-silico drug design approach.

Introduction: Cancer is the second most prevalent cause of mortality in the world, despite the availability of several medications for cancer treatment. Therefore, the cancer research community emphasized on computational techniques to speed up the discovery of novel anticancer drugs. Methods: In the current study, QSAR-based virtual screening was performed on the Zinc15 compound library (271 derivatives of methotrexate (MTX) and phototrexate (PTX)) to predict their inhibitory activity against dihydrofolate reductase (DHFR), a potential anticancer drug target. The deep learning-based ADMET parameters were employed to generate a 2D QSAR model using the multiple linear regression (MPL) methods with Leave-one-out cross-validated (LOO-CV) Q2 and correlation coefficient R2 values as high as 0.77 and 0.81, respectively. Results: From the QSAR model and virtual screening analysis, the top hits (09, 27, 41, 68, 74, 85, 99, 180) exhibited pIC50 ranging from 5.85 to 7.20 with a minimum binding score of -11.6 to -11.0 kcal/mol and were subjected to further investigation. The ADMET attributes using the message-passing neural network (MPNN) model demonstrated the potential of selected hits as an oral medication based on lipophilic profile Log P (0.19-2.69) and bioavailability (76.30% to 78.46%). The clinical toxicity score was 31.24% to 35.30%, with the least toxicity score (8.30%) observed with compound 180. The DFT calculations were carried out to determine the stability, physicochemical parameters and chemical reactivity of selected compounds. The docking results were further validated by 100 ns molecular dynamic simulation analysis. Conclusion: The promising lead compounds found endorsed compared to standard reference drugs MTX and PTX that are best for anticancer activity and can lead to novel therapies after experimental validations. Furthermore, it is suggested to unveil the inhibitory potential of identified hits via in-vitro and in-vivo approaches.