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AI Drug Discovery
Overview
AI promises accurate and faster drug discovery
AI drug discovery is the process of applying advanced algorithms and machine learning to analyze large biological and chemical datasets to find potential drug candidates. AI’s ability to rapidly and precisely analyze highly complex scientific data enables faster drug development at lower costs, aiding in reviving the declining R&D efficiency in the pharmaceutical industry. Studies have shown that using AI in drug discovery reduces the time taken by a factor of 15 and increases success rates. In addition, the application of AI in drug discovery is creating new opportunities in areas like rare diseases and personalized medicine. These developments have resulted in many large pharmaceutical companies collaborating with AI companies to gain access to drug discovery services to speed up regulatory approvals and market entry for their drug candidates.
However, the widespread adoption of AI in drug discovery is tempered by several challenges, including a shortage of high-quality, unbiased data, restrictions on data sharing, complexities in patenting AI-generated drugs, and the high demand for scarce AI talent. These risks must be navigated carefully to fully harness AI's potential in revolutionizing drug discovery.
Industry Updates
Market Sizing
The global AI Drug Discovery market could reach USD 4.8 billion–14.4 billion by 2027
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Use cases
AI drug discovery, as the name implies, is primarily applied across applications such as drug discovery and development, digital pathology, and diagnosis in the healthcare sector. Biotechnology companies have integrated AI into their drug discovery and development processes, leveraging its capabilities to identify antibody and drug candidates and drug targets. This integration spans from preclinical stages—where AI aids in lead optimization—to the efficient design and management of clinical trials, revolutionising the entire pharmaceutical development pipeline.
In addition, AI platforms play a role in identifying biomarkers for diagnosing diseases like cancer and assessing disease progression within the healthcare sector.
We have identified key AI drug discovery use cases below:
Market Mapping
The AI drug discovery industry consists of:
AI SaaS companies : A large number of startups in the AI drug discovery space operate as AI SaaS (Software-as-a-Service) companies providing their ML platform to other pharma companies. These pure play startups often specialize in a specific stage of the drug discovery process, and have not expanded into latter stages of drug development.
AI drug discovery and development companies : Majority of the well funded startups in the AI drug discovery space operate as drug discovery and development companies. These are biotechnology companies that have integrated AI as a core component of their in-house drug development process. They typically have a few in-house drug development programs and also collaborate with pharma companies to develop certain drugs.
Pharma incumbents : Large pharma companies operate in this space through collaborations with AI startups or tech companies or by developing in-house AI capabilities.
Tech incumbents : Tech companies which are known for their strong AI capabilities have also entered the industry and offer machine learning tools to support drug discovery. Tech incumbents simply offer ML tools and are not specialized in any particular segment. Hence, they are not included in the market map.
Over 90% of the startups are at the Minimum Viable Product/Go-to-Market stages. Most AI SaaS platform operators are yet to achieve stable revenue despite collaborations with pharma companies. AI biotechnology startups have not commercialized their drug with only a few reaching the clinical trial stage.
AI Drug Discovery & Development
AI SaaS | Data Aggregation and Research
AI SaaS | Biomarker Development
AI SaaS | Preclinical Experiments
AI SaaS | Drug Discovery
The Disruptors
Disruptors’ value proposition comes from their specialization and unique technology
Both artificial intelligence (AI) Software-as-a-Service (SaaS) companies and AI-driven biotechnology startups often collaborate with big pharma companies to leverage from the benefits of synergy or due to the lack of funding/expertise to develop drugs in-house. Given that most big pharma companies have also integrated AI into their drug development process in some form, disruptors’ value proposition comes from their specialization and unique technologies.
Funding History
Competitive Analysis
Incumbents
Collaborations with artificial intelligence companies companies remain common among big pharma
Incumbents consist of large pharma companies which have integrated artificial intelligence (AI) into their drug development process in some form or entered into collaborations with AI startups and technology companies that offer AI-driven solutions for drug discovery.
Leading big pharma companies have developed in-house AI capabilities, but have not disclosed the extent to which AI is being used in the drug development process. All leading big pharma companies are actively engaged in collaborations with AI startups or tech companies. This is because building in-house capabilities to match the specialization offered by startups is challenging, especially with the scarcity of personnel skilled in both AI and biology.
Notable Investors
