Leveraging artificial intelligence and koch snowflake fuzzy sets to optimize antibiotic development pathways

Abstract

The rapid escalation of antibiotic resistance is diminishing the effectiveness of current treatments and poses a severe threat to global health security. Addressing this challenge requires identifying the most critical criteria in the antibiotic development process and determining which approaches yield the most effective results. However, the literature reveals a significant gap: few studies systematically analyze the factors that shape the effectiveness of antibiotic development, and even fewer comparatively evaluate the most efficient development strategies. This study aims to fill this gap by providing a scientific roadmap for decision-makers through the integration of artificial intelligence (AI) methods into a fuzzy multi-criteria decision-making (MCDM) framework. A total of 15 evaluation criteria and eight antibiotic development approaches were identified through a comprehensive literature review. Expert opinions were collected from five specialists in the field, and their relative importance was objectively quantified using a dimensionality reduction technique, a machine learning–based AI approach. Subsequently, criteria weights were calculated via the LOPCOW method, while antibiotic development strategies were ranked using the CODAS method. To further enhance the robustness of decision-making under uncertainty, the newly introduced Koch Snowflake fuzzy sets were integrated into the AI-driven framework, marking an additional innovation in fuzzy set theory. This hybrid model contributes to the literature by (i) enabling a holistic analysis of critical factors and effective strategies in antibiotic development, (ii) demonstrating how AI-based dimensionality reduction can be combined with fuzzy decision-making tools for more objective and precise outcomes, and (iii) offering a more comprehensive evaluation than previous studies by incorporating an extended set of criteria. The study’s findings reveal that the most important factor in the antibiotic development process is smart biosafety and computerized control systems (0.0904), while the optimal development strategy is artificial intelligence-assisted molecule discovery (0.504). Additionally, antibiotic repositioning was found to play a significant supporting role. By highlighting the value of integrating machine learning techniques and fuzzy AI frameworks into drug discovery processes, this research not only addresses a pressing issue in global health but also demonstrates the transformative potential of artificial intelligence in advancing life sciences and accelerating antibiotic innovation.