Artificial Intelligence (AI) in Drug Discovery - Thematic Research
Description
Artificial Intelligence (AI) in Drug Discovery - Thematic Research
Summary
In recent years, driven by the COVID-19 pandemic, the pharma industry has undergone increased levels of digital transformation. The availability of ultra-large datasets and technological advances has led to more interest in the use of artificial intelligence (AI) and big data analytics across the pharma value chain, from drug discovery and clinical trial design, right through to sales and marketing.
Drug R&D is an incredibly expensive and time-consuming process. And while the inception of computer-aided drug design (CADD) 40 years ago has significantly enhanced drug discovery, there is still a low success rate, with just 10% of candidates making it into clinical development.
The ever-growing amount of biomedical data requires more advanced technologies and computing power to support faster and more efficient hit generation. AI is being increasingly used to enhance CADD methods, as it can rapidly assimilate big data. It can be used to quickly identify and validate drug targets, screen billions of potential molecules for hit generation and optimization, and predict patient response of treatment. This can significantly reduce the time and cost to get a drug to market, particularly in areas of unmet need such as rare diseases or new antibiotics. However, AI will face challenges such as data quality, education and overcoming hype, and skills shortages.
Over the past 3-4 years, there has been increased interest in the use of AI in drug discovery, as witnessed by the emergence of an ever-growing number of start-ups operating in this area, increasing number of drug discovery partnerships, and record levels of investment. While most drugs developed using AI are in early stages of development, there have been some recent major milestones, including the first drug developed by AI to enter clinical trials and the repurposing of an already marketed drug to treat COVID-19.
Scope
Summary
In recent years, driven by the COVID-19 pandemic, the pharma industry has undergone increased levels of digital transformation. The availability of ultra-large datasets and technological advances has led to more interest in the use of artificial intelligence (AI) and big data analytics across the pharma value chain, from drug discovery and clinical trial design, right through to sales and marketing.
Drug R&D is an incredibly expensive and time-consuming process. And while the inception of computer-aided drug design (CADD) 40 years ago has significantly enhanced drug discovery, there is still a low success rate, with just 10% of candidates making it into clinical development.
The ever-growing amount of biomedical data requires more advanced technologies and computing power to support faster and more efficient hit generation. AI is being increasingly used to enhance CADD methods, as it can rapidly assimilate big data. It can be used to quickly identify and validate drug targets, screen billions of potential molecules for hit generation and optimization, and predict patient response of treatment. This can significantly reduce the time and cost to get a drug to market, particularly in areas of unmet need such as rare diseases or new antibiotics. However, AI will face challenges such as data quality, education and overcoming hype, and skills shortages.
Over the past 3-4 years, there has been increased interest in the use of AI in drug discovery, as witnessed by the emergence of an ever-growing number of start-ups operating in this area, increasing number of drug discovery partnerships, and record levels of investment. While most drugs developed using AI are in early stages of development, there have been some recent major milestones, including the first drug developed by AI to enter clinical trials and the repurposing of an already marketed drug to treat COVID-19.
Scope
- Key players in the AI in drug discovery space: this includes specialist AI vendors that partner with pharma companies to support their drug discovery efforts, such as Insilico Medicine, Atomwise, Recursion Pharmaceuticals, and Exscientia, leading technology companies such as Alphabet, IBM, and Microsoft, and adopters of AI in drug discovery, which includes big pharma as well as other start-ups and small biotech companies.
- Thematic briefing: this includes a definition of AI and how it (predominantly machine learning) is being applied to drug discovery, such as identifying drug targets, virtual screening of compounds, de novo drug design, drug repurposing, and identification of treatment response biomarkers.
- Trends: This section looks at key trends impacting the AI in drug discovery space. Industry trends include the impact of COVID-19 on digital transformation in pharma, rising cost of R&D, availability of ultra-large biomedical datasets, companies building in-house AI capabilities, formation of industry AI consortia, and use of AI in precision and personalized medicine. Technology trends include the role of technology giants in drug discovery, big data, cloud, and quantum computing. Macroeconomic trends include skills shortages in AI, and an increase in the number of deals related to AI in drug discovery including partnerships and funding. Regulatory trends include ICMRA recommendations to support regulatory bodies with challenges posed by using AI to develop drugs.
- Industry analysis with a detailed analysis of drugs discovered using AI from GlobalData's Drugs Database, as well a comprehensive deals section. It also includes case studies, survey and poll data, hiring trends, and social media analysis.
- Value chain which looks at different uses of AI in drug discovery, including identification and validation of drug targets, virtual screening of compounds and de novo drug design, drug repurposing, and identification of treatment response biomarkers.
- Companies: Examples of companies in the AI in drug discovery space including leading AI technology vendors, specialist AI vendors, and adopters of AI in drug discovery.
- See who the leading players are in the AI in drug discovery space.
- See how the competitive landscape is evolving, with a review of company activity including strategic partnerships and funding deals, as well as mergers and acquisitions (M&A).
- See what trends are driving the use of AI in drug discovery.
- See an analysis of drugs discovered by AI, including by company, phase of development, therapy area, and molecule type.
Table of Contents
77 Pages
- Executive Summary
- Figure 1: Examples of leading players in AI in drug discovery and where do they sit in the value chain?
- Figure 2: Key components of machine learning
- Healthcare trends
- Technology trends
- Macroeconomic trends
- Regulatory trends
- Market size and growth forecasts
- Analysis of drugs discovered using AI
- Survey data on the adoption of AI in pharma
- Deals
- Case studies
- Hiring trends
- Company filings trends
- Social media trends
- Figure 23: AI in drug discovery value chain
- Target identification and validation
- Generation of molecule leads/de novo drug design
- Drug repurposing
- Response biomarker discovery
- Leading AI technology vendors
- Specialist AI vendors in drug discovery
- Leading pharma adopters of AI in drug discovery
- Who’s who
- Thematic screen
- Valuation screen
- Abbreviations
- Related Reports
- Bibliography
- Digital Healthcare Analyst
- Senior Director of Thematic Analysis
- Global Head and EVP of Healthcare Operations and Strategy
- Figure 32: Our five-step approach for generating a sector scorecard
- About GlobalData
- Contact Us
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