Artificial intelligence (AI) is revolutionizing drug discovery by providing novel and efficient ways to identify potential therapeutic compounds. The process of discovering new drugs is expensive, time-consuming, and often plagued by high failure rates. Also, the return on investment in the industry has been declining and the industry is starving for new technologies to enhance the process.
AI can help accelerate the drug discovery process by predicting the effectiveness and toxicity of compounds, reducing the number of costly experiments needed, and ultimately leading to the development of safer and more effective drugs. In recent years, the pharmaceutical industry has seen a surge in investment in AI-based drug discovery technologies.
One of the most significant advantages of AI in drug discovery is the ability to identify new targets for drugs. For example, researchers can use AI to analyze genomic data and identify genetic mutations that are associated with specific diseases. They can then use this information to develop drugs that target those mutations.
Another advantage of AI in drug discovery is the ability to predict the efficacy of potential drugs. By using machine learning algorithms to analyze data from preclinical studies, scientists can identify the characteristics of drugs that are most likely to be effective in humans. This can help reduce the number of failed drug trials and accelerate the development of new drugs.
Investors have recognized the potential of AI in drug discovery and have been pouring funds into this space. According to a report by CB Insights, investment in AI drug discovery increased from $100 million in 2014 to over $15 billion in 2022 and is expected to grow to $4 billion in 2027 (CAGR of 45,7%). The largest regions for the industry are North America and Western Europe including the UK, while such regions as the Middle East and Asia, including Japan, keep increasing their activities.
What are the growth drivers for AI in Drug Discovery?
- AI technology keeps solving costly and time-consuming issues of the rapidly growing Drug Discovery industry.
- Emerging countries and regions like India, China, Middle East show a significant increase in demand for pharmaceuticals while showing the growth of population income and improving healthcare infrastructure.
- AI and ML allows to shorten the number of mistakes and human factor during the process of Drug Discovery which stimulates the industry to grow faster
- Big Tech corporations, such as Microsoft, NVIDIA, and IBM entered the market, seeking new partners and M&A targets.
In this article, we will explore the latest examples of AI-based drug discovery and the investments that are driving their development.
Insitro, a company focused on using machine learning to develop new therapies for diseases, raised $400 million in a Series C funding round in February 2022. The round was led by Viking Global Investors and joined by several other investors, including SoftBank Vision Fund 2, BlackRock, and Canada Pension Plan Investment Board. Insitro uses AI to analyze large datasets of genetic and other patient data to identify new drug targets and predict how potential therapies will perform in clinical trials.
AbCellera, a Vancouver-based biotech company that uses AI to discover new antibody therapies, raised $483 million in an initial public offering (IPO) in December 2021. AbCellera’s platform uses machine learning to analyze immune system data from patients and develop new treatments for cancer and other diseases. The company’s IPO was one of the largest ever for a Canadian biotech firm.
Atomwise, a San Francisco-based startup that uses AI to develop small-molecule therapies, raised $123 million in a Series B funding round in November 2021. The round was led by B Capital Group and joined by several other investors, including DCVC and Sanabil Investments. Atomwise uses deep learning algorithms to analyze large databases of molecular structures and predict which compounds are most likely to be effective against specific diseases.
Insilico Medicine, a Hong Kong-based company that uses AI to develop new drugs and personalized therapies, raised $255 million (mega-round) in a Series C in September 2021 and $95 million in a Series D funding round in Summer 2022. The C round was led by Warburg Pincus and joined by several other investors, including Qiming Venture Partners, Pavilion Capital, and Eight Roads Ventures. The D round included previous investors Warburg Pincus, B Capital, Qiming Venture Partners, BOLD Capital Partners, Pavilion Capital, and new investors such as BHR Investment, with D+ round being led by Prosperity7 (the diversified growth fund of Aramco Ventures). Insilico Medicine’s platform uses deep learning to analyze large datasets of patient data and identify new drug targets and personalized treatment options.
Recursion Pharmaceuticals, a Salt Lake City-based company that uses AI to develop new therapies for rare diseases, raised $436 million in a Series E funding round in August 2021. The round was led by Baillie Gifford and joined by several other investors, including Foresite Capital and Counterpoint Global. Recursion Pharmaceuticals’ platform uses machine learning to analyze large datasets of cell images and identify new drug targets for rare diseases.
MegaRobo Technologies, a Beijing-based company that provides robotics and AI for life sciences research raised $300 million (mega-round) in a Series C in June 2022. The round was led by Goldman Sachs’ private investment arm, venture capital company GGV Capital and Asia Investment Capital and joined by Sinovation Ventures, Pavilion Capital, Starr Capital, Yumeng Capital, Redview Capital and Harvest Capital, as well as investment bank Taihecap. MegaRobo intends to boost its investment in research and development, enlarge its production capacity, and further expand its presence in various areas ranging from stem cell therapy and genetics to traditional Chinese medicine.
BigHat Biosciences, a California-based startup that uses AI to develop safer, more effective antibody therapies for patients using machine learning and synthetic biology raised $75 million in a Series B in July 2022. The round was led by Section 32, with participation from new investors Amgen Ventures, Bristol Myers Squibb, Quadrille Capital, Gaingels, GRIDS Capital, and including prior investors Andreessen Horowitz, 8VC, and AME Cloud Ventures. The new funding will be used to scale the capacity of MillinerTM, an integrated AI/ML- wet lab platform, advance therapeutic programs toward human clinical trials, and further aggressive expansion.
DeepCell, a California-based startup leading the way in utilizing AI-powered techniques to classify and isolate single cells for both basic and translational research purposes, raised $73 million in a Series B in July 2022. The round was led by Koch Disruptive Technologies and joined by Bridger Healthcare, Horizons Ventures, Casdin Capital and prior investors Andreessen Horowitz and Bow Capital as well as Jeff Dean, Head of Google Brain, and Matt Mcllwain, Managing Director at Madrona Venture Group. The recently acquired funding will provide Deepcell with the resources to sustain its product development efforts and facilitate the timely release of an innovative benchmark for obtaining biological insights via single cell analysis.
ConcertAI a Massachusetts-based company that provides real-world data (RWD) solutions for life sciences and healthcare companies, raised $150 million round in a Series C in March 2022. The round was led by Sixth Street. Being $1,9B valuation startup, the company has previously already worked with such giants as Pfizer and Bristol-Myers Squibb. In June 2021, the company revealed a five-year partnership with the U.S. Food and Drug Administration (FDA) aimed at leveraging ConcertAI’s RWD and AI technologies to explore the application of real-world data in the advancement of cancer treatments.
These ten examples demonstrate the growing interest and investment in AI-based drug discovery technologies. The applications of AI in drug discovery are vast and varied, including drug repurposing, target identification, lead optimization, and toxicity prediction.
While AI in drug discovery is a highly active and competitive field, it is still not an overcrowded space for new companies to enter the market. While there are many players in the space, there is still significant room for innovation and differentiation, as well as opportunities to target specific niches within the industry, such as precision and personalised medicine.
- Most equity deals in the healthcare industry involve AI technologies in Healthcare, including AI in Drug Discovery
- AI equity deals have increased significantly during past 5 years and have been growing more
- Biotech and Drug Discovery keep been evergreen industries for the investors even in 2022-2023
For today, AI in Drug Discovery encounts 1400+ active investors and only 700 companies, which still offers a room for interesting investment opportunities, as well as an increasing number of partnerships and M&A deals with big pharma companies, which also provide rather fast exits.