In the field of drug development, artificial intelligence has played a significant role, particularly in the research and development of cancer drugs. The application of AI has not only improved R&D efficiency but also reduced costs and accelerated the process of bringing new drugs to market.
創薬分野において、人工知能は重要な役割を果たしており、特にがん治療薬の研究開発に大きく貢献しています。研究開発の効率が向上しただけでなく、コスト削減や新薬を市場に投入するまでのプロセスの加速にもつながっています。
- Drug discovery: AI can expedite the process of drug discovery by screening a large number of candidate compounds using data mining and machine learning techniques. It can optimize drug molecule design and predict drug activity, toxicity, and metabolic pathways, thereby accelerating the drug discovery process.
- Genomics and proteomics research: AI can analyze data at the genomic and proteomic levels to identify new therapeutic targets and drug mechanisms, providing new ideas for drug development.
- Clinical trial design: AI can simulate the clinical trial process to improve its efficiency and accuracy, reducing the cost and risk of drug development.
- Drug safety assessment: AI can predict and assess drug toxicity and adverse reactions, reducing the number and risks of animal and human trials.
- Drug repurposing: AI can re-evaluate existing drugs and identify their potential uses, providing new possibilities for drug repurposing.
In the molecular generation stage, the current main technologies include variational autoencoder (VAE), generative confrontation network (GAN), and other natural language processing (NLP)-based RNN, LSTM, GRU, Transformer, etc. Among them, although VAE is not the latest technology compared to GAN and Transformer, it has a high degree of fit for the scene of drug molecule generation, and has excellent generation performance.