Task-Specific Activity Cliff Prediction Method Based on Transfer Learning and a Hyper Connection Graph Model.

Area of Science:

• Medicinal Chemistry
• Computational Chemistry
• Drug Discovery

Background:

• Activity cliffs (ACs) represent significant biological activity changes from minor structural modifications in molecules.
• Accurate prediction of ACs is critical for efficient drug discovery and molecular optimization.
• Current methods often fail to capture complex structural relationships, limiting prediction accuracy and generalizability.

Purpose of the Study:

• To develop a novel framework, TS-AC, for accurate activity cliff prediction.
• To enhance model generalization by integrating transfer learning from a large-scale drug-drug interaction (DDI) prediction task.
• To improve the representation of structure-activity relationships using a hyper connection graph architecture.

Main Methods:

• Developed TS-AC, a task-specific framework combining transfer learning and a hyper connection graph architecture.
• Pretrained a model on a large-scale drug-drug interaction (DDI) prediction task to acquire general chemical knowledge.
• Designed a hyper connection graph module to model interactions between core and substituent fragments in matched molecular pairs.

Main Results:

• TS-AC demonstrated superior performance compared to state-of-the-art methods across three independent datasets.
• The hyper connection graph module effectively captured the impact of subtle structural modifications on biological activity.
• Visualization analyses confirmed the interpretability and logical design of the TS-AC framework.

Conclusions:

• TS-AC offers a significant advancement in activity cliff prediction for drug discovery.
• The integration of transfer learning and graph neural networks provides a robust approach to modeling structure-activity relationships.
• The proposed framework enhances accuracy and generalizability in predicting the effects of small chemical changes on biological activity.