How Artificial Intelligence will Transform Drug Discovery How Artificial Intelligence will Transform Drug Discovery
Pharma Tech Outlook

How Artificial Intelligence will Transform Drug Discovery

Pharma Tech Outlook | Friday, July 15, 2022

Artificial Intelligence has been making inroads in drug discovery for a good part of the last decade. We recently published an analysis that showed that biotech companies using an AI-first approach have more than 150 small-molecule drugs in discovery and more than 15 already in clinical trials.

FREMONT, CA: Given artificial intelligence’s transformative potential, pharmaceutical companies must prepare for a future in which AI is routinely used in drug discovery. New market players are rapidly expanding and adding significant value, but the applications are diverse, and pharma companies must determine where and how AI can most benefit them. In practice, this means devoting the time required to comprehend the full impact of AI on R&D, which includes distinguishing between hype and actual achievement and distinguishing between individual software solutions and end-to-end AI-enabled drug discovery. It's tempting to believe that AI can be delivered by a new tool or a single team. This is rarely the case in our experience with many businesses. Instead, maximising the value of artificial intelligence necessitates a transformation of the discovery process. Companies must invest in data, technology, and new skills and behaviours across the R&D organisation. For lessons and a roadmap for the journey ahead, they can look to the AI-first drug discovery startups that are leading the way.

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In small-molecule drug discovery, artificial intelligence can add value in four ways, viz access to new biology, improved or novel chemistry, higher success rates, and faster and cheaper discovery processes. Many challenges and constraints in traditional R&D can be addressed by technology. Each application adds new insights to drug discovery teams and, in some cases, can completely reimagine long-standing workflows. Understanding and differentiating between use cases are critical because these technologies apply to a wide range of discovery contexts and biological targets. Much of the historical progress has been led by AI-native drug discovery companies that provide pharma players with software or service. These companies use data and analytics to improve one or more specific use cases at various points along the value chain. These are two examples of target discovery and validation using knowledge graphs and small-molecule design using generative neural networks. Large pharmaceutical companies have gained access to these capabilities through partnerships or software licencing deals, which they have then applied to their pipelines. Several AI-native drug discovery companies have built their end-to-end drug discovery capabilities and internal pipelines in recent years, ushering in a new breed of biotech firm. Furthermore, many of these players are experimenting with new business models.

AI-native biotech firms provide a glimpse of this AI-first model. Many have stacked capabilities end to end, reshaping the drug discovery and development process and capitalising on the operational advantages of a reimagined value chain. These artificial intelligence biotechs frequently use an ecosystem of partners to accelerate discovery and ensure that they focus on competitively differentiating capabilities, including academic researchers to provide target expertise, contract research organisations (CROs) to do wet-lab experimentation, and other industry partners to codevelop and commercialise assets. Using the ecosystem approach, one emerging biotech has developed artificial intelligence capabilities such as precision targeting, clinical candidate generation and optimization, and clinical-trial optimization based on predictions of the best therapy using patient samples. The company has its experimental capabilities, but they are focused on generating data to support its artificial intelligence models and developing expertise in specific therapeutic areas for internal assets. Through partnerships, it gains access to broader scientific expertise and clinical experience in other areas.

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