AI-Assisted Nucleic Acid Drug Development

In a remarkable coincidence, the 2024 Nobel Prize in Physics was awarded to two pioneers in the field of Artificial Intelligence (AI): John J. Hopfield of Princeton University, USA, and Geoffrey E. Hinton of the University of Toronto, Canada, in recognition of their foundational discoveries and inventions in utilizing artificial neural networks for machine learning. Similarly, half of the 2024 Nobel Prize in Chemistry was awarded to the "AI Engineers" Demis Hassabis and John Jumper, the scientists behind the development of AlphaFold2, an AI model capable of predicting protein structures based on amino acid sequences. This breakthrough solved a scientific puzzle that had persisted for 50 years. It signifies AI's growing impact and recognition in the biological sciences, which has profound implications for understanding the foundations of life and drug development. AI technology not only demonstrates its potential in unraveling complex biological systems but also provides novel avenues for future drug design and disease treatment.

The Impact of AI on Drug Development

Artificial Intelligence (AI) is fundamentally transforming the process of drug discovery and development. Through techniques such as machine learning and deep learning, AI can analyze and process vast amounts of complex biomedical data, accelerating the identification of drug targets, compound screening and optimization, and even the design and execution of clinical trials.AI in Drug Discovery and Development

• Target Identification: AI can analyze large-scale genomic and proteomic data through machine learning models, rapidly identifying potential drug targets associated with specific diseases and predicting which targets are more likely to succeed.

• Compound Screening: After identifying targets, AI can screen large compound databases to find those that may interact with the target and predict which compounds have the highest likelihood of success.

• Compound Optimization: AI can be used to optimize the chemical structure of compounds, predicting how changes in the chemical structure affect properties such as binding affinity to the target and potential toxicity, thereby improving their efficacy and safety. AI is also instrumental in enhancing research efficiency and advancing personalized medicine.

• Clinical Trial Design: AI can analyze historical trial data to predict which patients are most likely to benefit from a specific drug, allowing clinical trials to be conducted more effectively and reducing the risk of adverse events.

• Quality Control: In the field of quality assurance, AI enhances the efficiency and accuracy of testing through techniques such as automated testing, test data generation, and adaptive testing.

  
  

  

  
  

AI Empowering Yunhai Bio's Development of tFNA

Tetrahedral Framework Nucleic Acids (tFNAs) serve as carriers for delivering drugs, genes, or other bioactive substances. With high stability and controllability, tFNAs can be fine-tuned by modifying their nucleic acid sequences, allowing for precise drug delivery.

Yunhai Bio has made significant strides in combining AI with omics data to identify and locate suitable drug targets for tFNAs. For example, in ophthalmic treatments, Yunhai Bio used AI to rapidly identify a specific microRNA. By leveraging tFNA to deliver this microRNA, the treatment demonstrated remarkable effects in inhibiting neovascularization in the retina and protecting the optic nerve, surpassing the performance of traditional therapies such as Aflibercept. AI analyzed omics data, nucleic acid sequences, and protein-protein interaction networks, and machine learning techniques were applied to identify potential targets for the small nucleic acid drug. This not only improved the efficiency of target discovery but also provided a solid foundation for subsequent drug development.

In research focused on Amyotrophic Lateral Sclerosis (ALS), Yunhai Bio utilized deep learning models to successfully map the three-dimensional structure of the target protein. This breakthrough provided critical structural information for subsequent drug screening. By combining molecular docking and dynamics simulation techniques, Yunhai Bio identified peptide candidates with strong binding affinity to the target protein. In subsequent animal trials, tFNA-loaded peptides significantly extended the median survival of transgenic mice. This discovery highlights AI's potential in deciphering complex biological systems and opens new pathways for future drug design and disease treatment.

In the new era of drug discovery, the integration of Artificial Intelligence (AI) and Tetrahedral Framework Nucleic Acids (tFNAs) is opening up new possibilities. AI’s ability to process data and predict outcomes through machine learning models, combined with the stability and controllability of tFNAs as drug carriers, is revolutionizing precision drug delivery and disease treatment, paving the way for a brighter future in pharmaceutical research.