TRIZ-guided Computational Innovation Framework for the Automated Generation and Prioritization of Novel Azole-Like Antifungal Candidates Targeting Candida Auris CYP51

The rising incidence of antifungal resistance has underscored the need for novel strategies that can expedite the discovery of structurally diverse and optimized antifungal candidates. Conventional approaches to modify azoles are limited by narrow chemical space and problems related to resistance with the fungal CYP51 (Eeg11). The objective of this study was to develop a TRIZ-guided computational framework for unbiased generation, prioritization and evaluation of novel azole-like antifungal candidates targeting fungal CYP51. A set of TRIZ inventive principles was translated into medicinal chemistry-guided structural modification rules, to build a Python-based molecular design workflow. The workflow produced a virtual library of seventy azole-like candidates that were computationally filtered and ranked without manual candidate selection. The top-ranked candidates were subjected to a combined in silico pipeline involving SwissDock-based molecular docking against a heme-containing holo-Candida auris CYP51 model, ligand efficiency analysis, SwissADME pharmacokinetic prediction, RDKit-based quantitative estimate of drug-likeness (QED), ProTox-3.0 toxicity profiling, molecular interaction analysis, target-space profiling, and ASKCOS retrosynthetic assessment. The automated TRIZ-guided approach could generate azole-like scaffolds with chemical relevance for ready downstream evaluation. The most promising compound identified from the screening of the candidates was AZ-TRIZ-02, which showed better predicted binding affinity to CYP51 than fluconazole. Ligand efficiency analysis showed greater binding contribution per heavy atom, implying that the improved interaction was not due to increased molecular size. AZ-TRIZ-02 also showed good drug-like attributes, the pharmacokinetic properties were acceptable, and the preliminary toxicity profile was free of the hepatotoxicity, mutagenicity, carcinogenicity, and cytotoxicity alerts. Interaction analysis confirmed stable placement within the CYP51 catalytic pocket. Retrosynthetic assessment supported theoretical synthetic feasibility. The work shows the potential of TRIZ-guided innovation, cheminformatics and AI-assisted molecular design combined as a reproducible framework for antifungal lead discovery. Future studies will be necessary to confirm the translational potential of the identified candidate, including experimental synthesis and antifungal testing, CYP51 inhibition assays and overall safety assessment.