A key component of a new drug’s development is clinical trial research, which takes approximately six years and a significant amount of money. Currently, only one out of every ten molecules entering a clinical trial achieves clearance. Reasons for these failures range from inadequate patient selection to poor infrastructure. Implementing AI into drug discovery processes will help reduce the number of failed trials and reduce the burden on biopharma companies.
For example, the entire process of developing a new drug can take several decades, and billions of dollars. Starting with the chemical space of billions of compounds and progressing to candidate selection in clinical trials can take years or even decades. With AI-based solutions, researchers can rapidly design novel drugs, analyzing biomedical literature. The resulting new drugs are then tested in human clinical trials. In a recent report from GAO, we explored the future of drug development with AI.
AI-based solutions are being tested on large datasets. One company, Cyclica, uses its cloud-based proteome-screening AI platform, Ligand Express, to find receptors that interact with small molecules. As a result, the algorithm can identify both on-target and off-target interactions in a drug’s target. The AI-based solution can also be used to determine the potential adverse effects of new drugs.
AI-powered drug discovery technology can help pharma companies unlock untapped potential in their R&D. Innovative biopharma organizations are exploring new models of harnessing AI-powered drug discovery technology. They are striking deals with startups to gain access to disruptive innovation. While some large players are acquiring start-ups for intellectual property, they are also forming partnerships with silicon valley tech startups and dedicated internal teams to adopt AI-enabled drug discovery methods. The common pattern of AI-powered drug discovery solutions is that the large players introduce several solutions at different levels.
The use of AI in drug discovery can help develop a product’s dosage form and optimize the drug’s dosage. AI can also make quick decisions, leading to faster manufacturing, better-quality products, and batch-to-batch consistency. AI can also help with regulatory submissions and ensure a product’s position in the market and costs. But so far, no AI-based drug has been developed as a result of this technology. The potential is great, but specific challenges remain.
Despite the numerous potential applications of AI in drug discovery, the most popular of these technologies are machine learning and artificial intelligence. AI has already been applied to drug discovery in other fields, including predictive modeling. With this technology, pharma companies can speed up clinical development by predicting drug efficacy and side effects and handle vast amounts of data. In the end, artificial intelligence has the potential to revolutionize the pharma industry and speed up the development process.