The broad prospects of artificial intelligence (AI) have led many pharmaceutical companies to incorporate it into their strategic focus. Especially considering the current high R&D investment and long R&D cycle, even a slight improvement to the status quo is sufficient to prove that AI is actually helpful in drug development. Similarly, the increasingly important post-marketing patient support and rational drug use tracking will further promote the application of AI in the medical industry. At the same time, as the technology itself continues to evolve, AI companies are also continuing to improve their products to meet the different needs of pharmaceutical customers.
Driven by the above factors, it can be foreseen that in the next ten years, AI technology will flourish in the field of biological sciences (especially drug discovery). The development of AI technology will significantly change the way pharmaceutical companies operate and replace some traditional time-consuming technologies (such as high-throughput screening), and these traditional technologies will only be used in some specific scenarios or fields.
As the AI market is currently highly fragmented and strictly regulated, it will be a complicated process for pharmaceutical companies to develop effective AI strategies. Therefore, in this process, pharmaceutical companies should consider four key issues:
1. Cooperation with artificial intelligence companies
Considering that talents with both AI and biological knowledge are extremely limited, it will be more efficient to establish cooperative relationships with leading AI companies than to establish internal AI teams. Based on such a win-win partnership, pharmaceutical companies can obtain customized AI solutions for enterprise internal data, and AI companies can further improve the accuracy of algorithms through a large amount of data analysis. AI & Medicine is such a company that providing a broad and integrated portfolio of medical and scientific solutions that arise in the process of drug R&D.
2. Data sharing
The fierce competition in the pharmaceutical industry has resulted in very little possibility of sharing information among companies, and increasingly stringent regulations and compliance standards have further exacerbated this phenomenon. Therefore, some AI projects have been criticized for lack of sufficient data. For example, IBM Watson’s oncology drug discovery platform has received very negative reviews. The negative reviews claimed that the system’s non-radiotherapy patient data was insufficient, which greatly affected the system’s performance to learn and predict. From this perspective, data sharing with other pharmaceutical companies can maximize the potential of artificial intelligence. In the program “Accelerating Therapeutics for Opportunities in Medicine (ATOM)”, this momentum will become more apparent.
3. Transparency of algorithms for regulatory agencies
Regulators need to clearly know the algorithms used in drug development to understand the logic behind AI-led decision-making. If the algorithm is not transparent to supervision, AI will be a “black box” that cannot be rigorously evaluated and verified by science. This may cause various unforeseen problems in the drug approval process, such as the unclear acceptance of biomarkers “discovered” by AI. In order to avoid such problems, pharmaceutical companies should actively discuss with regulators to figure out a regulatory approach that can be accepted and benefited by both parties. AI & Medicine provides solutions and suggestions on the design and optimization of clinical trials.
4. Data privacy
Since the drug discovery phase does not involve patient data, the application of AI in the drug discovery phase is broader than the clinical phase. It must be cautious enough to use patient data. With the development of AI technology, companies must take reasonable legal and compliance measures to protect the increasing amount of patient data. In Europe, the General Data Protection Regulation (GDPR) will become especially important. Failure to comply strictly with it will damage the company’s reputation and cause huge financial losses.
The future of artificial intelligence
The rapid development of AI has brought a more efficient, faster and cheaper drug development model to the pharmaceutical industry, leading the global pharmaceutical industry into an exciting new era. However, opportunities and challenges coexist. Under the tide of AI, pharmaceutical companies need to break the tradition and cooperate unprecedentedly close to seek new opportunities.

Author's Bio: 

AI & Medicine aspires to be a leader in the field of deep learning for drug discovery, personalized healthcare, and medical translation. We have successfully accomplished many projects.