Demystifying Computational Biology: A Scalable Drug Discovery Framework for Medical Curricula

Area of Science:

• Bioinformatics and Computational Biology
• Genomic Medicine
• Drug Discovery Pipeline

Background:

• Precision medicine necessitates bioinformatics and computational biology training for medical students.
• Current medical curricula often lack accessible training in these essential areas.
• Integrating computational projects can bridge this gap, preparing future clinicians for genomic medicine.

Purpose of the Study:

• To develop a scalable, stepwise pedagogical framework for teaching medical students the drug discovery pipeline.
• To introduce students to bioinformatics tools and advanced protein modeling techniques.
• To provide practical insights into identifying and translating molecular inhibitors into potential therapeutics.

Main Methods:

• Utilizing online genomic databases to identify and characterize pathogen-related genes (Plasmodium falciparum as a model).
• Employing bioinformatics tools for gene characterization and advanced protein modeling (homology modeling, structural assessment).
• Using platforms like AlphaFold, SWISS-MODEL, and SwissDock for structural evaluation and virtual screening of small molecules from PubChem and ChEMBL-NTD.

Main Results:

• Students gain practical experience in gene characterization and protein structure analysis.
• Virtual screening protocols demystify computational biology, offering insights into drug lead identification.
• The modular approach makes complex drug discovery concepts accessible and relevant to medical students.

Conclusions:

• Accessible computational projects can effectively train medical students in bioinformatics and drug discovery.
• This pedagogical framework cultivates essential data literacy for future clinical practice in genomic medicine and infectious disease management.
• The project successfully integrates sequence and structure-based analyses, enhancing understanding of molecular inhibitor development.