Transformer-Based Model for Drug Design and Therapy Response Prediction.

Area of Science:

• Artificial Intelligence in Pharmacology
• Computational Chemistry
• Drug Discovery and Development

Background:

• Transformer models are increasingly utilized in drug design.
• Molecules are commonly represented using textual sequences or graphs.
• Applications span chemical structure prediction and target discovery.

Purpose of the Study:

• To review the application and advantages of transformer models in drug discovery.
• To elaborate on transformer applications across diverse prediction tasks.
• To compare transformer-based approaches with traditional drug discovery methods.

Main Methods:

• Literature review of transformer model applications in drug discovery.
• Analysis of transformer utility in tasks like SMILES principle, spectrum prediction, and drug response prediction.
• Examination of transformer roles in chemical structure, drug-drug interaction, and target interaction prediction.

Main Results:

• Transformers demonstrate significant advantages in various prediction tasks within drug discovery.
• The review covers applications from molecular representation to protein prediction.
• Comparison highlights the potential of transformers over traditional methods.

Conclusions:

• Transformer models offer a powerful and versatile approach to drug discovery.
• Future advancements will focus on more efficient models and multimodal/multitask predictions.
• Integrating transformers with other models enhances operational performance for complex tasks.