Venture Bytes #108: Drug Discovery Process Major Beneficiary of AI Integration
Lead article in VentureBytes Edition #108 - June 2024
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Drug Discovery Process Major Beneficiary of AI Integration
The drug discovery process is characterized by lengthy timelines, substantial expenses, and limited success rates. For instance, according to the NIH, the journey from conceptualizing a new drug to its market launch typically spans 12–15 years and incurs costs exceeding $1 billion. Contract research organizations (CROs) specialize in providing drug discovery services and are being
increasingly commissioned by pharmaceutical companies. While some pharma firms conduct in-house drug discovery, the proportion of outsourced drug discovery has risen significantly from 36.5% in 2017 to 49.3% in 2023, per Statista. The CRO market is highly consolidated, with the top 5 players experiencing stagnant revenues in 2023. In response, AI-powered drug discovery startups are emerging as attractive acquisition targets for CRO industry leaders. These acquisitions promise enhanced revenue growth and seamless integration of AI capabilities into their service portfolios.
Incorporating AI technologies presents a promising avenue for enhancing drug discovery processes. AI can expedite drug discovery by forecasting molecule properties and pinpointing potential drug candidates. Machine learning algorithms sift through extensive chemical and biological datasets, uncovering patterns and correlations that facilitate the identification of new drug targets and compounds.
The rise of telehealth, huge databases in Electronic Health Records, accessibility of high computer power, and the proven tangible benefits through AI integration in various industries, are driving the adoption of AI in drug discovery. Moreover, regulatory bodies are also accepting AI integration. For instance, FDA’s Center for Drug Evaluation and Research, in partnership with the Center for Biologics Evaluation and Research and the Center for Devices and Radiological Health, has issued an initial discussion paper to engage stakeholders and explore the use of artificial intelligence and machine learning (AI/ML) in drug and biological product development. Continuous feedback is sought to advance regulatory science. Highlighting AI/ML’s significance, the FDA intends to establish a flexible, risk-based regulatory framework that encourages innovation while safeguarding patient safety.
The AI in drug discovery market is projected to grow from $0.9 billion in 2023 to $4.9 billion in 2028, at a CAGR of 40.2%, per MarketsandMarkets. Startups such as Generate Biomedicines, BigHat Biosciences, Seismic Therapeutics, and Benchsci, among others, are well-equipped to capitalize on this rapidly growing market.
In addition to drug discovery, AI can significantly contribute to drug repurposing strategies, a method focused on identifying existing drugs or those with established safety profiles in humans, which can be applied to treat conditions different from their original indications. This approach expedites the delivery of effective treatments to patients awaiting medical interventions. Moreover, it offers substantial financial advantages for pharmaceutical enterprises seeking to introduce new medications to the market, in contrast to conventional drug discovery methodologies. Implementing drug repurposing can slash costs to below half a billion dollars, thereby exerting a profound influence on a company's research and development budget.
A number of promising startups are working on integrating AI in the drug discovery process. Significant among them are: Generate Biomedicines, BogHat BioSciences, Seismic Therapeutics and BenchSci.
•Massachusetts-based Generate Biomedicines integrates machine learning, medicine, and biological engineering to develop proteins with specific biophysical, biological, and therapeutic qualities. Ranked 25th on the 2024 CNBC Disruptor 50 list, the company has attracted significant investments from renowned firms including ARCH Venture Partners, Fidelity Investments, Morningside Venture Partners, NVentures, and T. Rowe Price, among others. A 29% surge in valuation in the recent funding round underscores investor faith in the startup's potential.
•California-based BigHat Biosciences stands at the intersection of drug development, machine learning, and biology. With over 10 therapeutics under development, the startup is emerging as a strong contender in AI-powered drug discovery. Strategic collaborations with pharmaceutical giants such as Merck and Abbvie affirm credibility. Marquee investors such as 8VC, Amgen Ventures, Andreessen Horowitz, and Bristol-Myers Squibb, among others, further cement the credibility.
•Massachusetts-based Seismic Therapeutics is a biotech firm revolutionizing the discovery and development of immunology therapies through machine learning. Leveraging its integrated IMPACT platform, an AI Model, the company has cultivated a robust pipeline of preclinical stage best-in-class and first-in-class biologics aimed at modulating dysregulated adaptive immunity and tackling various autoimmune diseases.
•Benches, headquartered in Canada, provides ASCEND, a GenAI platform in disease biology. ASCEND leverages comprehensive biomedical experiment data to develop a scalable AI assistant tailored for preclinical organizations. With over 4,300 prestigious academic research institutions and 16 of the top 20 pharmaceutical companies adopting BenchSci's AI solutions, it has garnered the trust of over 50,000 scientists who depend on it to inform their experiment decisions.
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