Since the pandemic, the pharmaceutical industry has been redefining and reinventing its operations and implementing more digital innovations. Generative artificial intelligence is one of the digital technologies promising to accelerate and improve the drug discovery process. The drug discovery process is extensively complicated and requires significant investments of time and money. Realizing new drugs take, on average, between 12 to 18 years, costing approximately $2.6 billion. Eventually, only 10% of drugs make it to clinical trials. Several companies are extensively researching the possibilities of integrating generative AI to improve the efficiency of their drug discovery processes (GlobalData Healthcare, 2023). My interest in (holistic) health, medical innovations, and breakthroughs stems from my own medical journey, searching for remedies for my chronic condition. The possibilities shown by the technique of generative AI to support the development of new drug discovery genuinely intrigue me. Seeing through the years how innovations widen the possibilities in the medical landscape is exciting to me.
Companies train their artificial intelligence to inspect vast and complex chemical and biological data sets. Subsequently, generative models process all this data to locate new targets for treating diseases and create new molecular structures with suitable properties. The input of scientists is to look for specific molecules with particular characteristics to transform these into new drugs (Nouri, 2023).
One of the companies to use generative AI to discover new drugs is Insilico Medicine. Insilico Medicine uses its generative AI platform, Pharma.AI, in each step during the drug discovery process. Traditional discovery of a drug for idiopathic pulmonary fibrosis would have cost over $400 million over six years. However, with the use of their generative models, the cost was $40 million, and the first phase of clinical trials began after 2.5 years. The generative approach to designing drugs thus increases time and cost efficiency (Yao, 2023).
Insilico Medicine even received IND approval from the U.S. Food and Drug Administration (FDA) to start their drug in the clinical validation stage(Insilico Medicine receives IND approval for novel AI-designed USP1 inhibitor for cancer, 2023). Besides, they have several other AI-designed drugs in their pipeline. Their lead drug for progressive idiopathic pulmonary disease is a breakthrough for entirely generated AI drugs since it is in phase II patient trials (Nouri, 2023).
Even though generative-designed drugs have a promising outlook, they also have barriers. Regulatory and ethical considerations may limit the design of drugs with the use of generative AI. Additionally, datasets must be high quality and large enough for machine learning (GlobalData Healthcare, 2023).
I believe using generative AI to design drugs will mature since it can shorten the discovery process of drugs and increase cost-efficiency. What do you think the future of healthcare will look like now that drugs can be created more efficiently with generative AI? Do you regard generative AI as a prescription for success in the future of healthcare?
References
GlobalData Healthcare. (2023, 3 augustus). Generative AI has the potential to revolutionise drug discovery. Pharmaceutical Technology. https://www.pharmaceutical-technology.com/comment/generative-ai-revolutionise-drug-discovery/?cf-view&cf-closed
Insilico Medicine receives IND approval for novel AI-designed USP1 inhibitor for cancer. (2023, 25 mei). EurekAlert! https://www.eurekalert.org/news-releases/990417
Nouri, S. (2023, 5 september). Generative AI drugs are coming. Forbes. https://www.forbes.com/sites/forbestechcouncil/2023/09/05/generative-ai-drugs-are-coming/
Yao, R. (2023, 27 juni). Insilico Medicine uses generative AI to accelerate drug discovery | NVIDIA blog. NVIDIA Blog. https://blogs.nvidia.com/blog/2023/06/27/insilico-medicine-uses-generative-ai-to-accelerate-drug-discovery/
A very interesting topic that is original and not covered by a lot of writers. Using AI to transform the competitive landscape of drugs is a disrupting innovation. The structure of the essay is very good and it was clear to follow. However one missing piece is that it lacks a part of your personal experience with the AI usage on drugs or a personal connection to the subject. This is important for a blog post.
Hi Tommy, Thank you for your comment. That is sharp of you. Let me explain briefly my personal interest in the topic. My interest in (holistic) health, medical innovations, and breakthroughs stems from my own medical journey, searching for remedies for my chronic condition. The possibilities shown in the blog by the technique of generative AI to support in the development of new drug discovery truly intrigue me and form hope for future solutions for my condition. Seeing through the years how innovations widen the possibilities in the medical landscape is exciting.