Environmental DNA and AI-Driven Drug Discovery: Unlocking Nature’s Genomic Reservoirs for Novel Therapeutics

Abstract

The combination of the environmental DNA (eDNA) technology and the artificial intelligence (AI) offers a radical direction to quickening the discovery of natural products used in drugs. The low culturing efficiency, sampling bias, and limited access to genomic data, nearly all constrain conventional bioprospecting approaches, causing the underrepresentation of microbial diversity. eDNA overcomes such barriers by allowing direct genetic harvesting of diverse ecosystems, making unculturing microbial genomes accessible. Parallel AI-based bioinformatics, machine learning, and predictive modeling can provide potent analysis tools to analyze and understand intricate metagenomic data, reveal biosynthetic gene clusters (BGCs) and predict the structures and functions of encoded metabolites. This paper is an overview of the last developments in eDNA-based metagenomics and AI-based compound discovery pipelines. It suggests a methodological framework, which combines eDNA sampling, next-generation sequencing, AI-assisted BGC annotation, predictive metabolites, and virtual screening. Case studies demonstrate how effective such approach has been in revealing new antibiotic candidates that are active in diverse applications in multidrug-resistant bacteria, polyketide-derived anticancer leads, and antiviral secondary metabolites. Data indicate that discovery times are greatly shortened, the ratio of hits to leads is increased, and predictive accuracy is greater than in conventional methods. The results demonstrate the possible change in the eDNA-AI combination that will transform the world of drug discovery and the need to pursue sustainable bio-prospecting methods and biodiversity exploitation. Future studies must include explainable AI models, development of world repositories of eDNA, and the implementation of fair systems on how to share genomic resources.