Entropy estimation in acetylcholinesterase-donepezil interaction through topological indices: a graph theoretical perspective

In this work, we investigate the molecular interaction between acetylcholinesterase (AChE) and the Alzheimer's medication donepezil using a graph-theoretical approach. We have presented the molecular graphs of AChE, donepezil, and their docked complex and calculated a set of degree-based topological indices, along with their respective Shannon entropy values. The entropy values measure the structural complexity and information content integral to each molecular graph. The study indicated a uniform growth of entropy for all indices in the docked complex over the unbound protein and ligand, signifying an enhancement in its topological disorder and edge-type variability with binding. It is interesting to see that indices such as the First Zagreb Index, Atom-Bond Connectivity Index, and Randić Index were successful in capturing this trend. This entropy-based assessment offers a solid and computationally efficient method for defining biomolecular interactions and providing insights into binding-induced structural reorganization. The results emphasize the value of topological entropy as a predictive tool for drug-target interaction profiling and structure-based drug design. Thus, the present study offers a computational approach to comprehend and forecast the energetics of a particular protein-ligand binding, which is a crucial objective in drug development and biophysical chemistry.