AI in drug discovery

Sci-Tech Snippets-12.

AI in drug discovery is expected to grow 6.6x by 2027.

Photo by Rashed Paykary on Unsplash

Analysis by MarketsandMarkets

AI in drug discovery

2022: $0.6 billion; 2027: $4 billion; CAGR: 45.7% (forecast period)

Major market segments

Natural language processing, context-aware computing, and deep learning (the largest)

Pivot companies

IBM Watson Health, Atomwise, Insilico Medicine, Exscientia, BenevolentAI, twoXAR, NVIDIA, NuMedii, Iktos, BenchSci, and Berg

What are the market enablers?

• Efficient and cost-effective drug development process
• Global demand for new drugs and therapies for chronic diseases (cancer, neurological disorders, and cardiovascular ailments)
• Availability of large data sets (electronic medical records, genomic databases, and clinical trial data)
• Sophistication in ML algorithms
• Collaboration among government, academia, and industry
• Promotion of R&D and AI in the healthcare sector by the government

What are the limitations of the day?

• Non-accessible, high-quality data sets for ML algorithms.
• The need for experimentation on living organisms requires time, resources, and funds.
• Issues like ethics, transparency, and accountability of AI technology in the health sector.
• Demographic biases in the data sets skew the results.

What are the future trends and benefits?

• ML in various domains of life science, including healthcare and pharma. (precise predictions of drug interaction, simulation of drug impact, and side effects)
• Generative AI for drug discovery (generate drug candidates)
• Cloud computing for drug discovery (enabling large-scale computing online)
• Virtual drug screening (simulations can speed up the process)
• Source integration (data→molecular+genetic+clinical for potential targets reducing clinical failures in the later stages)

What are the offerings?

Software products and services

What are the focus technologies?

• Lab informatics
• Modeling and simulation
• Reinforcement learning
• Unsupervised learning
• Supervised learning
• Other machine learning and deep learning technologies

What are the target applications in pharma and biotech?

• Neurological diseases
• Cancer treatments
• Cardiovascular treatments
• Metabolic diseases
• Epidemiology

What are the potential collaborators in life science?

• Academic research institutes
• Pharma and biotech companies
• Contract research organizations (private and government)

Deep Dive

AI in drug discovery market 2023

Disclaimer: The statements and opinions expressed in this blog are those of the author(s) and do not necessarily reflect the positions of Thoughtworks.