Theoretical Investigation of Para‐Substituted Aniline Based Dendritic Architecture: In‐Silico ADMET, DFT, and Machine Learning Approach

Abstract

Dendrimers are polymeric compounds which exhibit notable advantages, particularly in medicinal chemistry. The therapeutic potential of these molecular architectures can be screened through in silico method which facilitates the identification of the ones with the highest potential for further investigation. Here, we have reported the in silico investigation of a series of dendritic architecture based on para‐substituted aniline. Density functional theory (DFT) method was employed to optimize the structures and to analyze the quantum chemical parameters. The absorption, distribution, metabolism, excretion, and toxicity (ADMET) profiling was additionally assessed, which unveiled favorable drug‐like characteristics and moderate to good bioactivity across all compounds. Utilizing the drug‐likeness property in machine learning techniques, the potential for predicting novel drug‐like dendrimers was explored.