Computational Profiling of Monoterpenoid Phytochemicals: Insights for Medicinal Chemistry and Drug Design Strategies.

Monoterpenoids are a structurally diverse class of natural products with a long-standing history of therapeutic use. Despite their promising bioactivities, their clinical development has been limited by dose-dependent toxicities, poor pharmacokinetics, and suboptimal drug-like properties. In this work, a comprehensive in silico pipeline was employed to evaluate 1175 monoterpenoid compounds retrieved from ChEBI, aiming to identify structurally diverse candidates that possess favorable drug-like characteristics. A total of 54 molecular parameters were calculated using thirteen computational tools, covering physicochemical parameters, ADMET profiles, and toxicological risk assessments. Stepwise filtering was employed to retain only compounds meeting stringent thresholds across multiple domains, followed by chemoinformatic analysis. Structure-activity relationship mapping and target prediction were subsequently conducted to explore mechanistic plausibility. This workflow led to the identification of seven top-performing monoterpenoids that exhibited ideal physicochemical profiles, high gastrointestinal absorption, low predicted toxicity, and full compliance with medicinal chemistry rules. Notably, target prediction revealed a convergence on GPCRs, enzymatic and nuclear receptors, highlighting potential anti-inflammatory and neuromodulatory effects. The identification of conserved pharmacophores across selected scaffolds further reinforces their translational potential. Our results highlight the value of multi-parameter computational triage in natural product drug discovery and reveal a subset of overlooked monoterpenoids with promising preclinical applications.