Artificial Intelligence (AI) in Drug Discovery- Competitive Landscape, 2022

Artificial Intelligence (AI) In The Drug Discovery Market By Application (Target Identification, Molecule Screening, De Novo Drug Design, Drug Optimization And Repurposing, Preclinical And Clinical Testing, And Others), By Therapy Area (Oncology, Infectious Disease, Neurology, And Others), By Ai Technology (Machine Learning, Deep Learning, And Others), And By Region (North America, Europe, Japan, Asia-Pacific, And Row) is expected to increase the global AI drug discovery competitive market.

Artificial Intelligence is at the frontline of next-generation healthcare technology evolution and has entered the life sciences and the pharmaceutical sector extensively over the past few years. Competition for AI technologies in drug discovery has increased as more and more companies are turning towards the use of AI-aided computer tools and methodology due to the driving factor of AI in drug designing, drug screening, chemical synthesis, polypharmacology, and drug repurposing.

AI in Drug Discovery Market Dynamics:

The growing use of AI in the drug discovery market is anticipated due to the increasing number of collaborations to utilize AI in developing drugs, which is resulting in the development of robust pipelines across the pharma market. Recently, in January 2022, Exscientia-an AI-driven pharma tech company-collaborated with Sanofi to develop novel small molecule candidates across oncology and immunology, leveraging Exscientia's end-to-end AI-driven platform. Furthermore, many tech giants are also actively creating AI-driven drug discovery platforms and collaborating with pharmaceutical companies. In November 2021, IBM and Arctoris collaborated to accelerate closed-loop drug discovery with AI and cloud technology.

Therefore, an increase in demand for AI platforms globally leads to an increase in the demand for collaborations, thereby propelling the growth of the AI drug discovery competitive landscape.

Moreover, another key factor responsible for the growth of AI in the drug discovery market is the huge investment from companies in the R&D aspect, where AI platforms are being developed to be utilized in drug discovery. For instance, In December 2021, Verge Genomics secures USD 98 million in new financing to expand its end-to-end AI platform to transform drug discovery. Also, the rising number of startups operating in the AI spectrum for drug discovery is also increasing the competitive market.

Furthermore, other factors boosting the use of AI are due to the upper hand of using this advanced technology over conventional methods. As the conventional method is usually time-consuming and cost-intensive, the use of AI has significantly decreased the prolonged procedures of drug discovery and development and with much higher efficiency.

However, lack of skilled professionals, limited understanding regarding the use of AI, and scarcity of data sets in the field of drug discovery can hinder the growth of competitive landscape.

The ongoing Coronavirus (COVID-19) pandemic has led to significant growth in the AI drug discovery competitive market. This is due to the increased adoption of AI to discover and develop medications for COVID-19 treatment. For example, in March 2020, Predictive Oncology to use AI platform for drug and vaccine development of COVID-19.

AI in Drug Discovery Market Segment Analysis:

AI in the drug discovery market By Drug Discovery Application (Target Identification, Molecule Screening, De Novo Drug Design, Drug Optimization and Repurposing, Preclinical and Clinical Testing, and Others), By Therapy Area (Oncology, Infectious Disease, Neurology, and Others), By AI Technology (Machine Learning, Deep Learning, and Others), and By Region (North America, Europe, Japan, Asia-Pacific, and RoW).

In By Application Area segment of AI in drug discovery, Preclinical and Clinical testing is expected to dominate as the preclinical stage includes iterative testing, as well as essential feasibility and drug safety data for drug development. Therefore, preclinical studies necessitate utilization of analytics software in order to adhere to a strict scientific standard of drug development and to identify more sophisticated therapeutic candidates as they progress from the lab to clinical trials.

AI offers a lot of potential in both short and long-term clinical trials. AI technologies enable advances such as seamlessly merging phase I and phase II clinical trials, creating novel patient-centered endpoints, and gathering and analyzing Real World Data, which are critical for reforming clinical trials.

For instance, Genome Biologics, pioneer in molecular and functional drug positioning, uses AI platform GENIMAPS® and GENISYST® allows to study AI driven preclinical disease modeling. Genome Biologics identifies promising therapeutic candidates and FDA-approved drug repositioning molecules for cardiovascular and metabolic diseases. They combine the power of AI-based machine learning with transformative single cell in vitro and in vivo transgenesis to dramatically reduce the cost, time, and ethical burden of preclinical research in the pharmaceutical industry, leveraging the power of their patented AI driven preclinical multi-modality drug testing technology.

By Indication, Oncology segment is expected to have highest in the competitive landscape as many companies are adopting AI in the discovery of various cancers, and as companies are incorporating AI platforms to develop oncology drugs in their pipeline.

North America is expected to dominate the overall AI in Drug Discovery Market:

Among all the regions, the US is still firmly dominant in terms of its proportion of AI for drug discovery companies. Furthermore, the US also dominate in the R&D of novel drugs for different therapy area and funds invested in drug discovery and development. This is a rationale, given that the US is the headquarters of more than half of the world's AI drug discovery companies.

Moreover, the recent rise in the number of investors interested in the AI for Drug Discovery field in the US. As high adoption rate of AI technologies results in a large number of investments to boost the drug discovery industry; and a large number of cross-industry collaborations and partnerships, all of which are expected to fuel market growth. However, due to the rising use of advanced AI technologies and major companies' increased focus on expanding their footprint in emerging Asian countries.

AI in Drug Discovery Market Key Players:

Some of the key market players operating in the AI in drug discovery diagnostics market include: IBM, Exscientia, Alphabet, Benevolent AI, Microsoft, Atomwise, NVIDIA Corporation, Deep Genomics, Insilico Medicine, XtalPi, Cyclica, Cloud Pharmaceuticals, Iktos, Genesis Therapeutics, Evaxion Biotech, BERG, Numerate, Bioage, NuMedii, BioSymetrics, among others.

Recent Developmental Activities in the AI in Drug Discovery Diagnostics:

• In January 2022, Insilico Medicine and Centogene collaborated to accelerate the discovery of novel therapeutics using the AI platform to target Niemann-Pick disease type C. The duration of the research collaboration will initially last 20 weeks.

• In February 2022, CytoReason announced the extension of its collaboration agreement with Pfizer to leverage machine learning in drug development.

• In July 2021, Verge Genomics announced a three-year collaboration with Eli Lilly and Company to research and develop novel therapies for the treatment of amyotrophic lateral sclerosis (ALS), a devastating motor neuron disease using an AI-Driven All-in-Human Platform.

Key Takeaways from the AI in Drug Discovery Competitive Landscape Report Study

• The effect of the COVID-19 pandemic on this market is significant. To capture and analyze suitable indicators, our experts are closely watching the AI in drug discovery

• Top key product/services/technology developments, merger, acquisition, partnership, joint venture happened for last 2 years

• Key companies dominating the AI drug discovery market

• Various opportunities available for the other competitor in the AI in drug discovery

• What are the top performing segments in 2022

• Which is the top-performing regions and countries in the current AI drug discovery

Target Audience who can be benefited from this AI Drug Discovery Competitive Landscape Report Study

• AI drug discovery technology providers

• Research organizations and consulting companies

• AI in drug discovery related organizations, associations, forums, and other alliances

• Government and corporate offices

• Start-up companies, venture capitalists, and private equity firms

• Various End-users who want to know more about the AI in drug discovery competition and latest technological developments in the AI in drug discovery

Frequently Asked Questions For AI In Drug Discovery:

1. What is AI in drug discovery?

AI in the drug discovery and development process helps in screening chemical properties to find new drug targets and from molecular design to organizing databases of drugs and accelerating the drug discovery process.

2. What are the drivers for the Global AI in the drug discovery market?

The AI in drug discovery competitive landscape is witnessing a positive market growth owing to the increasing cross-industry collaboration and partnerships, increasing investments in the R&D of AI platforms in drug discovery, and rising number of startups operating in the AI spectrum for drug discovery, and advantages over conventional drug discovery procedures.

3. Who are the key players operating in the drug discovery market?

Some of the key market players operating in the AI in drug discovery diagnostics market include IBM, Exscientia, Alphabet, Benevolent AI, Microsoft, Atomwise, NVIDIA Corporation, Deep Genomics, Insilico Medicine, XtalPi, Cyclica, Cloud Pharmaceuticals, Iktos, Genesis Therapeutics, Evaxion Biotech, BERG, Numerate, Bioage, NuMedii, BioSymetrics, among others.

4. Which region has the highest share of AI in the drug discovery Market?

North America is still dominant in terms of its proportion of AI for drug discovery companies. This is due to fact that many companies have their headquarters situated in the US, a high amount of investment in the R&D of AI platforms, and increased collaboration.