Polypharmacology as the Future of Drug Design

What is polypharmacology, and why is it important? 

V.G.: Polypharmacology involves designing drugs that target multiple biological pathways instead of focusing on a single molecule or receptor. Unlike the traditional “one drug, one target” approach, this method is particularly vital for complex diseases like cancer and neurological disorders, where multiple pathways contribute to the disease. By targeting several pathways, we can create more effective and durable treatments.  

Benefits over single-target drug design

V.G.: Polypharmacology reduces the risk of side effects and drug resistance. Single-target drugs can lead to resistance as diseases adapt, especially in cancer or infections. Multi-target approaches address this by disrupting several points in a network. For complex conditions like Alzheimer’s, targeting multiple pathways significantly improves the chance of therapeutic success.  

Role of biocomputational science?

V.G.: Biocomputational tools are indispensable for polypharmacology. They enable us to simulate complex biological networks, identify effective target combinations, and screen ultra-large molecular libraries with billions of compounds. Computational methods also aid in finding molecules that interact with multiple targets, a task nearly impossible with traditional techniques.  

Machine learning applications?

V.G.: Machine learning helps predict target similarities and identify unexpected pairings. Deep learning models facilitate screening vast molecular libraries, and generative methods are emerging as promising alternatives. Supercomputing plays a pivotal role in these advancements.  

Challenges in multi-target drug design?

V.G.: Designing multi-target drugs is complex, with higher risks of side effects and unpredictable interactions. We need more data and better models to simulate biological systems accurately.  

Future outlook? 

V.G.: With advances in computation and biology, polypharmacology will progress rapidly. Improved in silico models and AI will streamline drug development, reducing time and costs. Regulatory frameworks will likely adapt, enabling faster approvals. At Nostrum, we’re committed to advancing these technologies.  

Advice for young researchers?

V.G.: Build expertise in computational methods, machine learning, and traditional biology. Polypharmacology demands a multidisciplinary approach, patience, and persistence. The rewards, however, can revolutionize medicine.