A new article from Nature, titled “AI’s potential to accelerate drug discovery needs a reality check,” discusses the recent claims made by companies about the potential of Artificial Intelligence (AI) in expediting drug discovery.
The article emphasizes the need for independent verification of these claims, as they have primarily been made by the companies themselves. It suggests that the findings should be published in peer-reviewed literature and authenticated by researchers who are not affiliated with the companies involved. This would ensure a more objective evaluation of the role of AI in drug discovery.
Read the article on Nature’s website by clicking here.
Dive In
Artificial Intelligence (AI) is revolutionizing the drug discovery process, offering improved efficiency, accuracy, and speed. However, the successful application of AI is dependent on the availability of high-quality data, the addressing of ethical concerns, and the recognition of the limitations of AI-based approaches.
The traditional drug discovery process is a long, complex, and expensive process, with many potential points of failure. It generally takes 12–15 years from the initiation of a discovery program to the point at which national drug regulatory agencies grant marketing approval. Some nine in ten drugs that enter clinical trials don’t get approved. Estimates suggest it costs about US$2.5 billion to bring a drug to market.
AI-based techniques can assist in selecting potential patients for pre-clinical trials by identifying relevant human-disease bio-markers. AI approaches can speed up and prevent failures in the drug discovery pipeline. Applications of AI at various stages of drug discovery pipelines include the inherently computational approaches of de novo design and prediction of a drug’s likely properties.
AI is also being used to develop experimental drugs. For instance, Insilico Medicine, which has headquarters in Hong Kong and New York, used AI to develop an experimental drug for the incurable lung disease idiopathic pulmonary fibrosis. The treatment is in mid-stage trials in the US and China with some results expected in early 2025.
AI can assist in structure-based drug discovery by predicting the 3D protein structure because the design is in accordance with the chemical. AI is also attractive for drug discovery because it applies the rapid and massive number-crunching capabilities of 21st-century computing technologies, such as machine learning, to data comparison and analysis in the way the human brain would, but in a fraction of the time.
However, it is still early days for AI drug discovery. There are a lot of AI companies making claims they can’t back up. The ultimate validation needs to be done in the lab. Despite the challenges, AI is already changing how drugs are being made. It could be a few years before the first drugs designed with the help of AI hit the market, but the technology is set to shake up the pharma industry, from the earliest stages of drug design to the final approval process.
In conclusion, AI has the potential to revolutionize the drug discovery process, but it is not without its challenges. The successful application of AI in this field will require the availability of high-quality data, addressing ethical concerns, and recognizing the limitations of AI-based approaches. Despite these challenges, the potential benefits of AI in drug discovery are significant and could lead to faster, more efficient, and cost-effective drug development processes.
