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

As precision medicine becomes an integral part of clinical practice, medical curricula would benefit from the inclusion of accessible training in bioinformatics and computational biology. This project provides a scalable, stepwise pedagogical framework for introducing medical students to the drug discovery pipeline using high-impact, small-scale computational projects. Each research project involves using online genomic databases to identify and characterize genes of importance to pathogenesis using Plasmodium falciparum as a model organism. The methodology changes from sequence to structure and teaches students to use bioinformatics tools to characterize genes and then applies advanced protein modeling techniques such as homology modeling and structural assessment. Students use platforms such as AlphaFold (developed through a collaboration of Google DeepMind, London, UK, and the European Molecular Biology Laboratory, Wellcome Genome Campus, Hinxton, Cambridgeshire, UK), SWISS-MODEL (Computational Structural Biology Group, Swiss Institute of Bioinformatics, Biozentrum, University of Basel, Basel, Switzerland), and SwissDock (developed through a collaboration between the Molecular Modeling Group of the University of Lausanne and the SIB Swiss Institute of Bioinformatics in Lausanne, Switzerland) to evaluate protein structures and model binding interactions with small molecules obtained from the PubChem and ChEMBL-NTD databases. The inclusion of these virtual screening protocols in these projects serves to demystify the complexities of computational biology, giving medical students concrete insights into the identification and translation of molecular inhibitors into potential therapeutic leads. The modular nature of this approach shows that complex concepts in drug discovery can be made accessible and relevant for future clinicians, thus cultivating the data literacy necessary to navigate the future of genomic medicine and infectious disease management.