Novel Thiosemicarbazone Quantum Dots in the Treatment of Alzheimer’s Disease Combining In Silico Models Using Fingerprints and Physicochemical Descriptors
Abstract
High Resolution Image Download MS PowerPoint Slide Searching for thiosemicarbazone derivatives with the potential to inhibit acetylcholinesterase for the treatment of Alzheimer’s disease (AD) is an important current goal. The QSAR KPLS, QSAR ANN, and QSAR SVR models were constructed using binary fingerprints and physicochemical (PC) descriptors of 129 thiosemicarbazone compounds screened from a database of 3791 derivatives. The R 2 and Q 2 values for the QSAR KPLS, QSAR ANN, and QSAR SVR models are greater than 0.925 and 0.713 using dendritic fingerprint (DF) and PC descriptors, respectively. The in vitro pIC 50 activities of four new design-oriented compounds N1, N2, N3, and N4, from the QSAR KPLS model using DFs, are consistent with the experimental results and those from the QSAR ANN and QSAR SVR models. The designed compounds N1, N2, N3, and N4 do not violate Lipinski-5 and Veber rules using the ADME and BoiLED-Egg methods. The binding energy, kcal mol –1, of the novel compounds to the 1ACJ -PDB protein receptor of the AChE enzyme was also obtained by molecular docking and dynamics simulations consistent with those predicted from the QSAR ANN and QSAR SVR models. New compounds N1, N2, N3, and N4 were synthesized, and the experimental in vitro pIC 50 activity was determined in agreement with those obtained from in silico models. The newly synthesized thiosemicarbazones N1, N2, N3, and N4 can inhibit 1ACJ -PDB, which is predicted to be able to cross the barrier. The DFT…
Read the paper
Related studies
- Chaos and quantum thermalization
- The Dalton quantum chemistry program system
- Cord Blood Leptin and Adiponectin as Predictors of Adiposity in Children at 3 Years of Age: A Prospective Cohort Study
- Quantitative metabolomics by H-NMR and LC-MS/MS confirms altered metabolic pathways in diabetes
- Stable adiabatic quantum batteries
- A novel self-lipid antigen targets human T cells against CD1c(+) leukemias
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.