This research evaluated the biocontrol potential of the bacterial flora from cured sugarcane bagasse (SCB) against Fusarium oxysporum f. sp. lycopersici (Fol), the causal agent of tomato Fusarium wilt. Screenings of twenty SCB-derived isolates revealed consistent antagonistic activity, inhibiting mycelial growth from 32.08% to 55.00%. The most effective isolate, 100MTN1, was identified via 16S rRNA sequencing (GenBank: PX506225) as Bacillus atrophaeus. Interaction between B. atrophaeus 100MTN1 and Fol FOLViF has revealed a distinct profile of bioactive metabolites produced specifically during co-cultivation. Transcriptomic profiling of Fol FOLViF exposure to 100MTN1 identified 189 differentially expressed genes, with downregulation of genes involved in DNA replication, translation, and membrane transport, and upregulation of those linked to secondary metabolism and oxidative stress. KEGG pathway mapping further supported the possible causes of disruptions within the pathogen. Molecular docking suggested that the B. atrophaeus 100MTN1 derived metabolite, 6-Hydroxy-3′-methoxyflavone and exhibits binding affinity for key Fol proteins that compares favorably with the commercial fungicides. Greenhouse trials using tomato cv. Kalyan confirmed that treatment with strain 100MTN1 was associated with reduced disease severity and enhanced plant growth. These findings suggest that B. atrophaeus 100MTN1 suppresses Fol FOLViF through a combination of metabolite-driven inhibition and transcriptional interference, signifying its potential as a biological control agent for managing Fusarium wilt.

The discovery of environmentally compatible antipathogenic molecules is increasingly important for sustainable crop protection. Computational chemistry and artificial intelligence (AI) provide powerful frameworks for accelerating the identification and mechanistic evaluation of bioactive molecules derived from plants and beneficial microorganisms. This review critically examines computational strategies for discovering antifungal, antibacterial, anti‐oomycete, antiviral, and nematicidal molecules, encompassing molecular‐target selection, protein structure prediction, binding‐site analysis, ligand preparation, virtual screening, molecular docking, molecular dynamics simulations, free‐energy calculations, and AI/machine‐learning‐assisted approaches. Particular attention is given to the molecular interactions of natural products and microbial metabolites with virulence‐associated proteins, effectors, signaling proteins, and essential metabolic enzymes of phytopathogens. Representative molecules, including trioxsalen, dihydroartemisinin, triamcinolone acetonide, 2,4‐di‐tert‐butylphenol, glafenine, hippuric acid, surfactin, and nicotinamide mononucleotide, illustrate the chemical diversity of emerging candidates. Current limitations associated with docking scores, protein flexibility, target validation, simulation protocols, and insufficient experimental validation are critically assessed. Integrating AI‐enabled molecular modeling with rigorous thermodynamic analyses and biological validation offers a promising route from computational screening toward chemically defined, target‐specific, and sustainable crop‐protection agents.