Problem analysis
Drug discovery is characterized by data scarcity and high experimental costs, while QLLM requires sufficient data to take advantage of quantum advantages.
solution strategy
- transfer learning: Fine-tuning with pre-trained biomedical QLLMs
- data enhancement: Application of Quantum Generative Adversarial Networks (QGAN) to synthesize molecular structure data
- multimodal learning: Integration of external knowledge sources such as AlphaFold, a protein structure prediction model
- Active Learning: Guiding experimental design through quantum Bayesian optimization for efficient data collection
Implementation pathway
A "small-data-driven" approach is suggested: 1) establish a quantum embedding space for molecular characterization; 2) use quantum similarity metrics to guide compound screening; and 3) iteratively optimize the model step by step.
This answer comes from the articleWorld's First Quantum AI Model! SECQAI Releases QLLM for Beta Testing!The
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