These figures exclude the amounts that pharma companies are investing in their internal capabilities and investments by tech giants, which have also been active in expanding their AI investments into biology and drug research. Third-party investment in AI-enabled drug discovery has more than doubled annually for the last five years, topping $2.4 billion in 2020 and reaching more than $5.2 billion at the end of 2021.
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The move from traditional service and software models to asset development partnerships and pipeline development has led to soaring investment. For example, Atomwise and Schrödinger formed a joint venture with a shared portfolio, and Roivant Sciences acquired Silicon Therapeutics to combine distinct platform technologies. In addition, many of these players are also exploring innovative business models.
The last few years have seen several AI-native drug discovery companies build their own end-to-end drug discovery capabilities and internal pipelines, launching a new breed of biotech firm. Large pharma companies have been able to gain access to these capabilities through partnerships or software licensing deals and then apply them in their own pipelines. Examples include target discovery and validation using knowledge graphs and small-molecule design using generative neural networks. These firms use data and analytics to improve one or more specific use cases at various points in the value chain. Much of the historical progress has been led by AI-native drug discovery companies that offer software or a service to pharma players. They can look to the AI-first drug discovery startups that are leading the way for lessons and a roadmap for the journey ahead. To take advantage, companies must make investments in data, technology, and new skills and behaviors throughout the R&D organization.
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Instead, achieving full value from AI requires a transformation of the discovery process. From our experience with many companies, this is rarely the case. It may be tempting to think that AI can be delivered through a new tool or a single team. In practice, this means spending the time needed to understand the full impact that AI is having on R&D, which includes separating hype from actual achievement and recognizing the difference between individual software solutions and end-to-end AI-enabled drug discovery. New players are scaling up fast and creating significant value, but the applications are diverse and pharma companies need to determine where and how AI can most add value for them. Given the transformative potential of AI, pharma companies need to plan for a future in which AI is routinely used in drug discovery. This AI-fueled pipeline has been expanding at an annual rate of almost 40%. 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.