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Pharma Tech Outlook | Monday, May 30, 2022
For the past decade, artificial intelligence has made significant progress in drug discovery.
Fremont, CA: Small-molecule drug discovery can benefit from AI in four ways: new biology, improved or original chemistry, higher success rates, and faster and cheaper discovery processes. Many issues and limits in traditional R&D can be addressed with this technique. Each tool provides drug research teams with new insights and, in some circumstances, can completely transform long-standing operations. Understanding and distinguishing between use cases is crucial because these technologies are applicable to a number of discovery scenarios and biological targets.
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AI-native drug discovery firms that sell software or services to pharma companies have spearheaded much of the historical advances. At various points throughout the value chain, these companies employ data and analytics to better one or more distinct use cases. Target discovery and validation with knowledge graphs and small-molecule design with generative neural networks are two examples. Large pharmaceutical corporations have been able to obtain these capabilities through collaborations or software licensing agreements, and then implement them in their own pipelines.
Several AI-native drug discovery businesses have built their own end-to-end drug development capabilities and internal pipelines in recent years, ushering in a new breed of biotech company. Many of these players are also experimenting with new business strategies. Atomwise and Schrödinger, for example, formed a joint venture with a shared portfolio, and Roivant Sciences purchased Silicon Therapeutics to bring together different platform technologies.
The shift away from traditional service and software models and toward asset development partnerships and pipeline development has resulted in record levels of investment. For the past five years, third-party investment in AI-enabled drug discovery has more than doubled annually, topping $2.4 billion in 2020 and exceeding $5.2 billion by the end of 2021. These estimates do not include amounts invested by pharma businesses in internal capabilities or by IT behemoths, which have been aggressive in expanding their AI investments into biology and drug research.
Although the impact of AI on traditional drug development is still in its early stages, we have shown that when AI-enabled capabilities are integrated into a traditional process, they can significantly speed up or improve specific phases while also lowering the costs of doing expensive studies. Indeed, AI algorithms have the ability to automate most discovery procedures (such as molecular design and testing) so that physical studies are only needed to confirm results.
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