Structure-property modeling of pharmacokinetic characteristics of anticancer drugs via topological indices, multigraph modeling and multi-criteria decision making

This study presents an in-depth inquiry into estimating ADME properties for promising anticancer drugs, particularly amino acid-based alkylating agents, through ev-ve degree topological indices and QSPR analysis. The aim of the study is to compare multigraph modeling to simple graph modeling in estimating six ADME properties. Results demonstrate that multigraph modeling's superior performance, with notable high correlations such as r=0.926$$ r=0.926 $$ for maximum passive absorption (MPA) using the M-ev index, compared to simple graph modeling's r=0.68$$ r=0.68 $$ with the M2$$ {M}_2 $$-ev index. This emphasizes the need for sophisticated modeling techniques in drug development. The primary objective is to compare multigraph and simple graph modeling using topological structure descriptors, followed by QSPR analysis to determine the better approach in estimating ADME properties. MCDM weight allocation techniques validate correlation values, enhancing understanding of estimators and identifying potential drugs. This underscores the importance of considering various MCDM methods and weight allocation approaches for reliable decision-making in healthcare contexts. In the realm of graph theory, the comparison between multigraphs and simple graphs revealed that in estimating physicochemical properties of drug structures using topological indices, multigraphs demonstrated a marginal but noteworthy superiority. Here one of the ADME property maximum passive absorption (MPA) of multigraph model shows a stronger positive correlation relationship, r=0.926$$ r=0.926 $$ with the amino acid based anticancer drugs compared to the simple graph model with r=0.68$$ r=0.68 $$. This perspective shows that using multigraphs in graph theory has small but important benefits for understanding and estimating properties in complex structures like drugs. image