Revolutionising Drug Discovery and Development with Artificial Intelligence

Author: Aarti Chitale, Senior Industry Analyst, Healthcare and Life Sciences, Frost & Sullivan.

As the industry continues its innovation spree across small and large molecules, tech-enabled drug discovery vendors are re-establishing themselves as digital biotechnology companies with in-house therapeutic pipelines along with platform-based and project-based partnerships with pharma players. Also, incorporating integrated AI-driven solutions in clinical trial design will aid in reducing costs, timelines, and increasing efficiency through DCT models.

As the global pharma industry continues its shift towards precision medicine in the quest for targeting undruggable targets, research and development (R&D) activities have experienced a tremendous surge, requiring cutting-edge technologies that not only streamline drug development processes but most importantly manage costs. As a result, adoption of newer technologies such as artificial intelligence (AI)/machine learning (ML), large language models (LLMs), and generative AI (a subset of the AI metaverse) is on the rise, providing time- and cost-efficient alternatives to manual processes that took years to complete.

As partnerships between pharmaceutical companies and technology providers grow, newer technologies like generative AI for de novo drug design and disease modelling, synthetic control arms and digital twins for clinical trials’ control groups and patient data, and many more are gaining traction, supporting all aspects of drug discovery and development.

Before venturing in detail about the many benefits of technology in drug development, it is important to understand the R&D landscape. In terms of the R&D expenditure, industry giants including Roche, Pfizer, JNJ, Merck, and Novartis take the lead with the highest expenditure in value. These companies are also among the top 5 with a number of R&D projects, with a significant focus on oncology. Apart from oncology, neurology (CNS disorders), infectious diseases, and immunology are the next key focus areas across the industry. Infectious diseases, especially, gained attention during the pandemic, with several trials initiated to support the development of COVID-19 therapies. Antibiotics are also gaining traction, with a focus on developing therapies for antimicrobial-resistant bacteria.

Interestingly, large pharma companies are now reporting comparatively smaller pipelines than previous years, except for a few larger conglomerates such as Novartis, BMS, Pfizer, Eli Lilly, and Boehringer Ingelheim. This suggests that the contribution of top pharma companies to the growing pipeline is declining. Instead, smaller and emerging biopharma companies with less than 10 products in the pipeline account for more than 60% of the pipeline. This shift is what is driving a greater reliance on technology in the form of AI/ML and generative AI.

AI-enabled Drug Discovery: A step ahead in Pharma innovation

One of the most promising applications of AI in drug discovery is in the early stages of identifying potential drug candidates, which is simply called ‘target identification’. Traditional drug discovery involves screening millions of compounds manually in the lab to find ones that are effective against a particular target, such as a protein associated with a disease progression. This process is not only time-consuming and costly but also results in a large number of potential compounds failing to make it through the screening process.

AI offers a more efficient approach by using machine learning algorithms to analyze vast amounts of data and predict the likelihood that a given compound will be effective against a specific target. These algorithms consider a wide range of factors, including the chemical structure of the compound, its biological composition, its similarity to known drugs, and its predicted interactions with the protein of interest.

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