Computational Aspects of Fungal Bioprospection for API/NCE Discovery

Le résumé fourni par la source

Fungi are among the richest sources of specialized metabolites, which have yielded active pharmaceutical ingredients (APIs) and new chemical entities (NCEs) with transformative clinical impact. However, their full potential remains constrained by cryptic biosynthetic gene clusters (BGCs), incomplete genomic resources and difficulties of linking genetic information to metabolite bioactivities. Recent advances in computational bioprospecting are reshaping this field, providing tools to decode fungal genomes, predict biosynthetic pathways and optimize bioactive compounds with unprecedented efficiency. Concurrently, improvement in genome sequencing, BGC mining and long-read assemblies are rapidly expanding the catalog of fungal metabolic potential thought platforms like antiSMASH, DeepBGC and BiG-SCAPE, while molecular docking, structural prediction and virtual screening bridge the genotype-to-phenotype gap. Besides, artificial intelligence (AI) and molecular language models facilitate scaffold generation, bioactivity prediction and retrosynthetic feasibility. Despite these advances, challenges, including incomplete genome assemblies, limited fungal bioactivity datasets and the complexity of polypharmacological interactions, remain. Thus, this chapter explores the current computational landscape, their key challenges and emerging prospects of computational fungal bioprospecting and highlighting how AI, multi-omics integration and synthetic biology promise to drive the next generation of precision therapeutics.

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