Artificial Intelligence in Drug Discovery Market Incredible Growth 2023-2030 with Scope, Value, Size, Share Trends Analysis | IBM Corporation (IBM Watson Health), Benevolent AI, Biosymetrics, Euretos

Market Overview:

Artificial intelligence in drug discovery utilizes machine learning and deep learning algorithms to analyze large and complex datasets for drug discovery, drug repurposing and target identification. AI tools aid in rational drug design by generating novel molecules and compounds with desired properties to fight diseases.

Market Dynamics:

Growing investments in artificial intelligence by biopharmaceutical companies is expected to drive the growth of AI in drug discovery market during the forecast period. For instance, in 2022, Novartis invested $500 million in an AI drug discovery company Anthropic in its Series B funding round to develop AI-based drug discovery platforms. Similarly, Pfizer invested $500 million in Cerevance, an AI-based drug discovery company, in 2021. Moreover, increasing adoption of AI technologies such as machine learning and deep learning for target identification and validation, lead generation and optimization is also expected to support the market growth over the forecast period.

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Market Drivers

Growing Computational Power and Data Availability is Driving the Adoption of AI in Drug Discovery

Artificial intelligence is becoming increasingly effective for drug discovery due to improvements in data availability and computational power. Vast amounts of chemical, genomic and clinical data are now available to train machine learning models. Cloud computing also allows AI systems to access massive amounts of computing resources. This data-driven approach has the potential to accelerate each step of the drug discovery process from target identification and validation to lead optimization.

AI is Helping to Reduce Drug Discovery Costs and Timelines

Drug discovery is traditionally an expensive and time-consuming process. On average, it costs $2.6 billion and takes 10-15 years to develop a new drug. AI and machine learning can help pharmaceutical companies overcome some of the key challenges in drug discovery like target identification and validation which account for a significant portion of the costs and time. AI is allowing researchers to screen millions of potential drug compounds in silico and identify promising candidates much faster than traditional trial and error methods. This is helping to reduce costs, risks and timelines for new drug development.

Moreover, it will also include the opportunities available in micro markets for stakeholders to invest, a detailed analysis of the competitive landscape, and product services of key players. Analysis of Artificial Intelligence in Drug Discovery companies, key tactics followed by Leading Key Players:

◘ IBM Corporation (IBM Watson Health)
◘ Exscientia
◘ GNS Healthcare
◘ Alphabet Inc. (DEEPMIND)
◘ Benevolent AI
◘ Biosymetrics
◘ Euretos
◘ Berg LLC.
◘ Atomwise Inc.
◘ Insitro
◘ among others.

Note: Major Players are sorted in no particular order.

By Therapeutic Space:

◘ Oncology
◘ Neurodegenerative Diseases
◘ Cardiovascular Diseases
◘ Metabolic Diseases
◘ Infectious Diseases
◘ Other

By Application:

◘ Drug Optimization & Repurposing
◘ Preclinical Testing
◘ Others

Market Restrain

Lack of Explainability of AI Models

One of the major issues holding back the adoption of AI in drug discovery is the lack of explainability of most machine learning models. Pharmaceutical companies and drug regulators require a clear explanation and rationale behind any decisions involving human health. Black-box AI systems that provide predictions or recommendations without explaining the reasoning behind them pose challenges for validation and regulatory approval of drug candidates. Work is ongoing to develop more interpretable and transparent AI techniques for the life sciences domain.

Market Opportunity

Personalized Medicine and Precision Healthcare

The promise of personalized medicine and precision healthcare offers a major opportunity for applying AI in drug discovery. With rapid advances in genomics, vast amounts of health data are now available. AI can help analyze this complex molecular and health data to develop highly targeted therapies tailored for individual patients or small subsets of patients based on their genetics, lifestyle factors and disease profiles. This personalized approach has the potential to significantly improve outcomes for many common as well as rare diseases.

Market Trends

Growing Focus on Multi-omics Integration

There is a growing recognition that diseases are influenced by a complex interplay of genetic, molecular and environmental factors. As such, a more comprehensive multi-omics approach integrating diverse data types like genomics, epigenomics, transcriptomics, proteomics and metabolomics promises far better insights over any single omics in isolation. Major AI and pharmaceutical players are making significant investments in developing platforms and methods for seamless integration of various types of omics data to power the next generation of AI applications for drug discovery. This trendwill help deliver on the promise of precision medicine.

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The Study Objectives are:

A comprehensive insight into key players operating in the Artificial Intelligence in Drug Discovery Market and their corresponding data.
It includes product portfolio, annual revenue, expenditure on research and development, geographical presence, key developments in recent years, and growth strategies.
Regional analysis, which includes insight into the dominant market and corresponding market share.
It also includes various socio-economic factors affecting the evolution of the market in the region.
The report offers a comprehensive insight into different individuals from value chains such as raw materials suppliers, distributors, and stockholders.
Table of Contents:

Market scenario 2023

Chapter 1: Introduction, Market Driving Force Product Objective of Study and Research Scope the Artificial Intelligence in Drug Discovery market
Chapter 2: Exclusive Summary - the basic information of the Artificial Intelligence in Drug Discovery Market.
Chapter 3: Displaying the Market Dynamics- Drivers, Trends, and Challenges & Opportunities of the Artificial Intelligence in Drug Discovery
Chapter 4: Presenting the Artificial Intelligence in Drug Discovery Market Factor Analysis, Supply/Value Chain, PESTEL analysis, Market Entry, and Patent/Trademark Analysis.
Chapter 5: Displaying the by Type, End User, and Region/Country 2023 - 2030
Chapter 6: Evaluating the leading manufacturers of the Artificial Intelligence in Drug Discovery market which consists of its Competitive Landscape, Peer Group Analysis, Market positioning & Company Profile
Chapter 7: To evaluate the market by segments, by countries, and by Manufacturers/Companies with revenue share and sales by key countries in these various regions (2023-2030)
Chapters 8 & 9: Displaying the Appendix, Methodology, and Data Source

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Key Questions Answered:

What is the market size and CAGR of the Artificial Intelligence in Drug Discovery Market during the forecast period?
How is the growing demand impacting the growth of Artificial Intelligence in Drug Discovery Market shares?
What is the growing demand of the Market during the forecast period?
Who are the leading vendors in the market and what are their market shares?
What is the impact of the COVID-19 pandemic on the APAC Artificial Intelligence in Drug Discovery Market?

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