In Silico Drug-Likeness, ADME, Pharmacokinetic and Toxicity Profiling of Novel Indole–Piperazine Hybrid Derivatives: An Index Computational Study

Abstract

The present study investigated the in silico drug-likeness, physicochemical, pharmacokinetic, ADME and toxicity profiles of ten novel indole–piperazine hybrid derivatives (C1–C10) to identify promising candidates for further drug-development research. Computational evaluation was performed using molecular descriptors associated with Lipinski’s Rule of Five, including molecular weight, hydrogen-bond donor and acceptor counts, and predicted octanol/water partition coefficient (QPlogPo/w). Additional physicochemical and ADME parameters, including water-accessible polar surface area (WPSA), predicted aqueous solubility (QPlogS), molecular globularity (Glob), predicted hERG liability (QPlogHERG), Caco-2 permeability (QPPCaco), blood–brain barrier distribution (QPlogBB), MDCK permeability (QPPMDCK), human serum albumin binding (QPlogKhsa) and predicted human oral absorption (%HOA), were comparatively analyzed. Toxicological assessment included predicted hepatotoxicity, developmental toxicity and mutagenicity. The compounds demonstrated considerable variation in molecular weight (383.49–482.52 Da), lipophilicity and polarity, reflecting the influence of structural modifications on their predicted pharmacokinetic behavior. C5 and C10 displayed comparatively greater polarity and improved predicted aqueous solubility, whereas C3 and C7 exhibited higher predicted membrane permeability and BBB distribution. C1, C4 and C9 demonstrated a favorable balance between lipophilicity, permeability and predicted oral absorption. Importantly, all ten compounds were predicted to be non-hepatotoxic, non-developmentally toxicant and non-mutagenic within the applied computational models. Overall, the integrated computational profiling identified distinct structure–property relationships across the series and highlighted compounds with comparatively balanced ADME characteristics. These findings support the use of computational profiling as an early-stage strategy for prioritizing indole–piperazine hybrids for subsequent experimental validation and lead optimization.