Arr, matey! Wit' AI's help, we might discover new, purse-friendly potions for our ailing scallywags!
2023-09-22
Avast ye mateys! 3 things to savvy how AI might aid in the creation of new, purse-friendly remedies. Methinks the success of this artificial intelligence won't be swift, but our industry be well-poised for victory! Arrr!
The life sciences industry is optimistic about the potential of generative AI in drug discovery. Biotech startups are already testing AI-generated drugs in clinical trials with human patients, and researchers estimate that AI-powered drug discovery could generate $50 billion in economic value over the next decade. The CEO of Dotmatics, a software company for pharmaceutical scientists, is excited about the potential of AI to reduce the time and cost of bringing new drugs to market and decreasing therapy costs for patients.However, the journey towards an AI-supported future of drug discovery will be deliberate and marked with ups and downs. Setbacks have already been seen, such as a schizophrenia drug discovered with AI failing clinical trials. It may take years for the costs and timelines of drug discovery to decrease significantly, especially since clinical trials contribute a significant portion of the costs and are currently manual.
The concern is that once the initial excitement about AI wears off, interest from outside the lab will dissipate. Investors, governments, and journalists play crucial roles in funding, regulating, and publicizing how AI is transforming drug discovery.
Despite the challenges, the life sciences industry is catching up in the race to digitally transform. Pharma companies have access to scalable and cost-effective infrastructure for managing large amounts of data, and there is now a greater alignment on the importance of digitization within the industry.
However, pharma still faces challenges in making AI useful, including the tsunami of data and complex new treatment modalities. Harnessing this data for AI requires proper data governance practices, and organizations must design data collection protocols with future reuse in mind.
Overall, progress in AI application will be slow and arduous, but investing in platforms and processes that enable the practical use of AI in the lab lays the foundation for a future where treatments can be developed quickly and cost-effectively. Each new drug candidate represents a step towards better health and quality of life for patients.