Protein structures and artificial intelligence join forces to transform drug combination therapy

Predicting whether drugs will work better together—or cancel each other out—is one of the toughest challenges in modern medicine. Now, scientists from China, Austria, Italy, and Hong Kong have synthesized the latest breakthroughs in combining protein 3D structure data with artificial intelligence to tackle this problem head-on.

Published in Advanced Science, the review describes how the spatial structure of proteins—the shape, size, and flexibility of their binding sites—determines how drugs interact with their targets. Changes in protein conformation can dramatically alter therapeutic effects, leading to either enhanced synergy or harmful antagonism when multiple drugs are used together.

Artificial intelligence, especially machine learning and deep learning, can efficiently process massive data from structural biology, genomics, and pharmacology, help researchers accurately simulate drug target interactions, more accurately predict clinical reactions, and optimize drug combination schemes. The article points out that AI analysis based on protein structure is helpful to solve drug resistance, reduce side effects, and guide the rational design of multi-target drugs.

The author emphasizes that this interdisciplinary strategy has practical application potential. Combining high-throughput screening, computational modeling, and clinical data, researchers can accelerate the discovery of new drug combinations and improve the safety and effectiveness of existing therapies. The fusion of protein structure and AI is expected to promote the development of precision medicine and realize the real personalized treatment.