Microbial-Derived Anti-Cancer Compounds: Advances in Drug Discovery, Bioengineering, and Therapeutic Applications

Area of Science:

• Biochemistry
• Pharmacology
• Computational Chemistry

Background:

• Microbial metabolites offer a rich source of anticancer compounds.
• Traditional drug discovery is slow and costly.
• Advanced computational and nanotech tools can accelerate this process.

Purpose of the Study:

• To review the role of AI, ML, molecular docking, and QSAR in identifying and optimizing microbial anticancer agents.
• To examine the impact of nanocarrier systems on drug delivery and efficacy.
• To identify challenges and future directions in the field.

Main Methods:

• Review of recent developments in artificial intelligence (AI), machine learning (ML), molecular docking, and quantitative structure-activity relationship (QSAR) modeling.
• Analysis of nanocarrier-based drug delivery systems for microbial metabolites.
• Examination of clinical translation challenges.

Main Results:

• AI significantly enhances compound screening and efficacy prediction.
• Nanocarriers improve bioavailability, specificity, and stability while reducing toxicity.
• Clinical translation is hindered by a lack of in vivo validation and pharmacokinetic data.

Conclusions:

• Integration of computational tools and nanotechnology accelerates discovery and delivery of microbial anticancer agents.
• Future research should combine AI with synthetic biology and advanced nanotechnology for targeted delivery.
• Understanding resistance mechanisms and molecular pathways is crucial for combination therapies and precision oncology.