Computational multi-criteria evaluation of fungicides against anthracnose disease using QSPR, ADMET and molecular docking

Anthracnose disease causes significant yield losses in a wide range of crops, necessitating the development of effective and environmentally responsible fungicides. In this study, a computational multi-criteria framework integrating quantitative structure-property relationship (QSPR)-based descriptor analysis, ADMET profiling, agrochemical-likeness assessment, and molecular docking was employed to comparatively evaluate selected fungicides against anthracnose disease. Key physicochemical descriptors governing absorption, distribution, metabolism, excretion, and toxicity were computed to examine molecular behavior relevant to agricultural application. Agrochemical suitability was further assessed using Tice's rules, which are associated with plant uptake and systemic behavior. Molecular docking was performed to explore fungicide-target binding interactions, providing mechanistic insight at the molecular level. The integrated analysis enabled a comparative in silico assessment of fungicides by jointly considering binding interactions, ADMET properties, and agrochemical-likeness. The proposed framework offers a conceptually consistent and exploratory strategy for in silico evaluation and prioritization of fungicides in crop protection research.