Mutational Effect Prediction

Predicting the effects of mutations on molecules and proteins is crucial for drug discovery, protein engineering, and understanding biological processes. Current research focuses on developing sophisticated machine learning models, including graph neural networks and ensemble methods, that leverage multiple data modalities (e.g., molecular structure, sequence information) to improve prediction accuracy. These models often incorporate pre-training strategies and hierarchical representations to capture complex interactions and microenvironmental effects. Accurate mutational effect prediction accelerates the design of therapeutic proteins and drugs, streamlining the development process and reducing costs.