ID: MRFR/Pharma/7918-HCR | February 2021 | Region: Global | 140 pages
AI in Drug Discovery Market is expected to hold a value of about USD 2,015.1 Million by 2025, and it is projected to register a CAGR of 40.8% from 2019 to 2025.
AI in Drug Discovery Market Synopsis
Artificial intelligence (AI) has captured the imagination and attention of medical industry professionals over the past couple of years as several companies, and large research hospitals have been working on perfecting these systems for clinical use. The first concrete examples of how AI (also called deep learning, machine learning, or artificial neural networks) will help clinicians are now being commercialized. These systems may offer a paradigm shift in clinician workflow to boost efficiency while improving care and patient throughput at the same time.
Integration of AI and machine learning tools in drug discovery & development applications could improve the healthcare outcome by increasing efficiency of drug discovery process, facilitating targeted molecule identification, minimizing the risk of adverse reactions during the trials, reducing the drug discovery timeframe, and most importantly reducing the cost of drug development for the drug manufacturer.
AI in Drug Discovery Market Drivers
Artificial intelligence in Healthcare is flourishing, and with unlimited opportunities provided by this advanced technology, the number of industry giants is taking an interest and pouring in money for the healthcare applications. IBM’s Watson, Google’s parent company Alphabet, and Philips are some of the tech giants who are investing in the AI for the healthcare sector. Apart from these, the number of pharmaceutical companies and a large number of startups all over the world are taking up initiatives and are involving themselves in the development of the AI & machine learning tools for the betterment of drug discovery and improvement in drug development outcomes.
The integration of artificial intelligence for healthcare applications is relatively new, and the industry is currently at its early stage. As the AI poses tremendous potential in the healthcare sector, a large number of companies are getting involved in the development of AI-based solutions for the healthcare sector. As of February 2020, more than 200 startups are operating in AI verticals for healthcare. Among these, startups such as Cyclica (Canada), DeepMatter (UK), MAbSilico (France), Molecule.one (Poland), Molomics (Spain), and others are working on designing new drug molecules and drug targets.
AI in Drug Discovery Market Restraints
Machine Learning: Machine learning (ML) approaches provide a set of tools that can improve decision making for specified questions with abundant, high-quality data. The application of ML can promote data-driven decision making with the potential to speed up the process and reduce failure rates in drug discovery and development. This segment is projected to have a larger share.
Deep Learning (DL) is another subset of AI, where models represent geometric transformations over many different layers. Deep learning technology utilizes a logic structure similar to the brain called neural ‘networks’ to recognize and discriminate patterns such as speech, image, and video. Deep learning has shown tremendous potential in drug discovery. It is expected to be the fastest-growing segment in AI in drug discovery market during the assessment period.
Some of the other technologies in the ai in drug discovery market include natural language processing, expert systems, and robotics.
The immuno-oncology segment is expected to hold the largest share in the global ai in drug discovery market. AI technology is showing promising outcomes in differentiating genetic mutations for precision medicine. By evaluating and pinpointing genetic mutations, oncologists are better able to treat their patients. AI application in drug discovery for cancer drugs has tremendous potential, and the number of companies is using AI for drug discovery for cancer drugs. For instance, Novartis is working with Microsoft’s AI platform to identify new ways to treat the deadly disease.
Neurodegenerative diseases are another important application of AI integration for drug discovery. Amyotrophic lateral sclerosis (ALS) is a severe neurodegenerative disease with no current treatment to slow the progression of the disease. BenevolentAI, an AI and machine learning start-up, is currently researching new therapies for amyotrophic lateral sclerosis (ALS). Neurodegenerative diseases segment is projected to grow at the fastest growth rate due to the increasing number of companies involved in the research for the same.
Cardiovascular disease affects the large population in the world, and the number of deaths due to cardiovascular disease is quite high. AI integration for drug discovery of cardiovascular disease drugs will help in formulating the drugs with high efficacy and low side effects. In January 2020, Bayer announced a collaboration with AI-driven company Exscientia to accelerate the discovery of small molecule drugs focused on cardiovascular diseases.
Metabolic diseases, another crucial health concern, where-in number of companies are making investments to discover novel drugs. For instance, in 2017, Sanofi and Exscientia signed a USD 273 million collaboration and license option deal to discover bispecific small-molecule drugs against metabolic diseases.
The others segment includes indications such as gastrointestinal diseases, skin diseases, and other rare diseases.
Finding new targets is a tedious task in drug discovery, and it takes months and years with the conventional methodology. AI integration will improve this process, making it faster. Some of the companies working on this aspect of drug discovery are AI Therapeutics, Adagene, Alphanosos, Antiverse, and few others.
This segment is expected to take the largest share in the global AI in drug discovery market. Some of the companies working in this aspect of drug discovery are A2A Pharmaceuticals, Accutar, Acellera, SEngine Precision Medicine, and a few others.
Drug repurposing is finding novel applications of existing drugs in the ai in drug discovery market. Some of the companies operating in this aspect are BioXcel Therapeutics, Healx, and few others.
Drug designing is a critical process where one has to consider the polypharmacology, pharmacokinetics, and structural pharmacogenomics of molecules. The segment is the fourth largest segment in the ai in drug discovery market. Some of the companies operating in this aspect of drug discovery are Cyclica, DeepMatter, Fetch Biosciences, Molomics, and few others.
There are two facets of this process, designing the preclinical tests and running them. Some of the companies operating in designing the preclinical tests are BenchSci and Desktop Genetics, where are companies like Arctoris, Emerald Cloud Lab, and Strateos few others are involved in running the preclinical tests.
The contract research organization segment is expected to be the second-largest in the global AI in drug discovery market. AI integration is expected to reduce the drug discovery and development costs by a huge margin, which will benefit the service providers as well as the innovators.
Understanding the 3D structure of the drug molecule and filtering the desired molecules is a crucial task in drug discovery. AI integration in the research is expected to help the researchers at ground level in understanding the structures better and improving the overall quality of academics and research.
AI in Drug Discovery Market Key Players
|Forecast Units||Value (USD Million)|
|Report Coverage||Revenue Forecast, Competitive Landscape, Growth Factors, and Trends|
|Segments Covered||By Product Type, Molecule Type, Technology, Indication, Application, End-User and Region|
|Geographies Covered||North America, Europe, Asia-Pacific, and Rest of the World (RoW)|
|Key Vendors||Microsoft Corporation, IBM Corporation, Google (A Subsidiary of Alphabet Inc.), Atomwise, Inc., Deep Genomics, Cloud Pharmaceuticals, Inc., Insilico Medicine, BenevolentAI, Exscientia, Cyclica, Bioage, Numerate, Numedii, Inc., Envisagenics, Twoxar, Incorporated, Owkin, Inc., Xtalpi, Inc., Verge Genomics, Berg LLC|
|Key Market Opportunities||
|Key Market Drivers||