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Although the development of modern molecular biology such as genomics, proteomics, and bioinformatics has brought great progress to the theory of drug research and development, due to the complexity of the biochemical reactions of drug molecules in the human body, the research and development of new drugs has not escaped the empirical color
Traditional drug research and development are dominated by pharmacological experts.
The advantages of AI technology in natural language processing, image recognition, deep learning, and cognitive computing can be applied to all aspects of new drug research and development
Therefore, AI+ new drug R&D has become a hot spot in current pharmaceutical research and cutting-edge medical entrepreneurship
Figure 1: The application of AI in all aspects of drug development
Source: According to public information
01 The application of AI in clinical trials is seriously insufficient
01 The application of AI in clinical trials is seriously insufficientThere are more than ten links in drug development, but due to factors such as data availability, AI can only be applied to a few links at present
Figure 2: Distribution of the number of cases of AI application in the segmentation of new drug R&D in recent years
Source: Speedstone Technology
02 The greatest value of AI-assisted new drug development: improving the success rate of clinical trials
02 The greatest value of AI-assisted new drug development: improving the success rate of clinical trialsIn fact, the efficiency improvement or cost reduction in the clinical trial phase has a far greater impact on the investment in new drug research and development than the drug discovery phase
Figure 3: Improve speed, improve quality, and reduce costs
Comparison of the degree of impact on drug R&D revenue
Source: Drug Discovery Today
Andreas Bender simulated three situations of accelerating drug discovery speed, reducing costs, and improving the success rate (improving quality) of each link of the whole process through AI technology (assuming that AI improves speed, cost and stage success rate by 20%), right The cost impact of a new drug successfully introduced to the market
It can be seen from Figure 3 that improving the success rate (especially in all clinical stages) has the greatest impact on the value of the entire R&D project, which far exceeds the benefits of increasing the R&D speed and reducing costs at each stage
03 Biological complexity limits the computational processing power of AI in clinical trials
03 Biological complexity limits the computational processing power of AI in clinical trialsAI applications are concentrated in the drug discovery link because this link is mainly based on chemical processes.
However, the clinical trial phase is dominated by biological processes, and its complexity poses huge challenges in both data and AI modeling
The current AI drug development model usually starts from simplicity and rarely considers the complexity of biology at the beginning, so it often fails in clinical testing
In addition, AI systems tend to simplify the model while ignoring other issues, such as whether the compound reaches its intended target, whether it can treat a certain phenotype of the disease, and whether its side effects are within an acceptable range, and so on
For the AI system to succeed, a clear "compound-target-phenotype" link needs to be established
04 Summary
04 SummaryIn a nutshell, limited by the complexity of biology and the lack of clinical databases, the application of AI in the field of drug research and development is mainly concentrated in the front-end drug discovery link, but the benefits that it brings to drug research and development are relatively limited
Reference materials:
1.
2.
3.
Bender A.
et al.
Artificial intelligence in drug discovery: what is realistic, what are illusions? ——Drug Discovery Today 2020.
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