HARNESSING PREDICTIVE ANALYTICS AND MACHINE LEARNING IN DRUG DISCOVERY, DISEASE SURVEILLANCE, AND FUNGAL RESEARCH
Keywords:
Predictive analytics, Drug discovery, Fungal biodiversity, Machine learning integrationAbstract
The integration of big data, machine learning (ML), and artificial intelligence (AI) is driving a transformative shift in biomedical and ecological sciences. This review examines the pivotal role of predictive analytics across three interconnected fields: drug discovery, disease surveillance, and mycology. In pharmaceutical research, predictive models have significantly accelerated the identification of drug candidates, streamlined lead optimization, and enhanced toxicity prediction ultimately reducing both time and cost. Advanced approaches, including deep learning and graph-based algorithms, are now standard tools for designing new therapeutics and efficiently screening compound libraries. While mycology has traditionally been underrepresented in computational research, it is now benefiting from predictive analytics through improved fungal classification, ecological modeling, and biosurveillance. Progress in image recognition and genomic trait prediction is opening new frontiers for studying fungal biodiversity and discovering bioactive compounds. Despite these advances, critical challenges remain, such as data heterogeneity, limited model interpretability, regulatory hurdles, and ethical concerns. This review identifies these barriers and proposes strategic solutions, including the integration of multimodal datasets, increased model transparency, and broader accessibility of analytical tools. By bridging innovations in drug development, public health, and fungal science, this review underscores the growing synergy between predictive analytics and life sciences offering a pathway to faster drug development, enhanced disease diagnostics, and more informed ecosystem management.References
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