중동 및 아프리카 약물 발견 시장에서의 인공지능(AI) – 2029년까지의 산업 동향 및 예측

중동 및 아프리카 약물 발견 시장 분석 및 통찰력에서의 인공지능(AI)  

인공 지능(AI)은 의료 산업에서 수익성 있는 기술이 될 것으로 기대됩니다. AI를 구현하면 약물 제조 공정의 R&D 격차가 줄어들고 약물의 표적 제조에 도움이 됩니다. 따라서 바이오제약 회사는 시장 점유율을 높이기 위해 AI로 전환하고 있습니다. 약물 발견을 위한 AI는 기계를 사용하여 인간의 지능을 시뮬레이션하여 약물 개발 절차에서 복잡한 과제를 해결하는 기술입니다.

임상 시험 프로세스에서 AI 솔루션을 도입하면 가능한 장애물을 제거하고 임상 시험 주기를 단축하며 임상 시험 프로세스의 생산성과 정확성을 높일 수 있습니다. 약물 발견을 위한 AI의 기술적 발전과 약물 발견 프로세스에 관련된 총 시간 단축은 예측 기간 동안 시장 성장을 견인하는 다른 요인입니다. 그러나 낮은 품질과 일관되지 않은 사용 가능한 데이터는 시장 성장을 방해할 것입니다. 또한 기술과 관련된 높은 비용과 기술적 한계는 시장 성장을 제한할 것입니다.

Data Bridge Market Research는 중동 및 아프리카의 약물 발견 시장에서 인공지능(AI)이 2029년까지 5억 4,876만 달러에 도달할 것으로 예상하며, 예측 기간 동안 CAGR은 47.1%라고 분석했습니다. 소프트웨어는 약물 발견 시장에서 AI 사용을 상용화하기 위한 기술 발전의 빠른 발전으로 인해 시장에서 가장 큰 기술 부문을 차지합니다. 이 시장 보고서는 또한 가격 분석, 특허 분석 및 기술 발전에 대한 심층적인 내용을 다룹니다.        

Middle East and Africa Artificial Intelligence (AI) in Drug Discovery Market Definition

AI has caught the attention and minds of medical technology practitioners in the past few years, as several companies and major research laboratories have worked to perfect these technologies for clinical use. The first commercialized demonstrations of how AI (also known as Deep Learning (DL), Machine Learning (ML), or Artificial Neural Networks (ANNs)) could assist clinicians are now available. These systems could lead to a paradigm shift in clinician workflow, and increase productivity while simultaneously enhancing treatment and patient throughput. AI for drug discovery is a technology that uses machines to simulate human intelligence to solve complicated challenges in the drug development procedure. The adoption of AI solutions in the clinical trial process eliminates possible obstacles, reduces clinical trial cycle time, and increases the productivity and accuracy of the clinical trial process. Therefore, the adoption of these advanced AI solutions in drug discovery processes is gaining popularity amongst life science industry stakeholders. In the pharmaceutical sector, it aids in the discovery of novel compounds, therapeutic target identification, and the development of customized medications. AI platforms used for drug discovery can prove to be a feasible option for deriving insights into the discovery of drugs to treat and minimize the severity of various chronic diseases.

Middle East and Africa Artificial Intelligence (AI) in Drug Discovery Market Dynamics

This section deals with understanding the market drivers, advantages, opportunities, restraints, and challenges. All of this is discussed in detail below:

Drivers

• Rise in incidence of chronic diseases propels need for AI in drug discovery

The incidence of chronic diseases is increasing at a rapid pace across the globe. According to the Centers for Disease Control and Prevention (CDC), six in 10 adults in the U.S. have a chronic disease. Furthermore, the CDC also highlights that chronic diseases such as heart disease and diabetes are the leading causes of death in the U.S. Such statistics shed light on the growing prevalence of chronic diseases and the need to bring down the fatality rate caused due to these diseases.

AI platforms used for drug discovery can prove to be a feasible option for deriving insights into the discovery of drugs to treat and minimize the severity of various chronic diseases. Thus, these factors are expected to act as a driver for the market growth during the forecast period.

• Strategic collaborations, partnerships, and products launch

AI has the potential to transform drug discovery by rapidly accelerating the R&D timeline, making drug development cheaper and faster, and improving the probability of approval. AI can also increase the effectiveness of drug repurposing research.

An increase in cross-industry alliances and collaborations drives the market. Rise in relevance of AI in drug discovery & development, and a surge in funding for R&D activities, including AI technology in the field of drug research, are projected to propel the global market growth. Hence, increase in cross-industry collaborations and partnerships is driving the market.

Restraint

• High cost associated with technology and technical limitations

The current healthcare sector is facing several complex challenges, such as the increased cost of drugs and therapies, and society needs specific significant changes in this area. The entire success of AI depends on the availability of a substantial amount of data because these data are used for the subsequent training provided to the system. Access to data from various database providers can incur extra costs for a company. Clinical trials are directed towards establishing the safety and efficacy of a drug product in humans for a particular disease condition and require six to seven years along with a substantial financial investment. However, only one out of ten molecules entering these trials gain successful clearance, which is a massive loss for the industry. These failures can result from inappropriate patient selection, shortage of technical requirements, and poor infrastructure. Thus, increasing costs with the technology is acting as a restraint for the market growth.

Opportunity

• Rise in the investments for R&D

The rise in R&D activities and increasing adoption of cloud-based services and applications will provide beneficial opportunities for market growth.

The industry of AI in biopharma continues to grow after a long period of sepsis. This is reflected in the ongoing flow of investments and increase in the number of collaborations between pharmaceutical corporations and AI companies in 2021 to the previous years. The Biopharma industry’s growth is largely influenced by the active engagement of leading pharmaceutical corporations in AI-related investments. The number of scientific publications in the field of AI in Biopharma, and research collaborations between pharma companies and AI-expertise vendors are rapidly increasing, yet, some pharma corporations are still critical of AI applications. ML and AI applications in the pharmaceutical and healthcare industries lead to the formation of a new interdisciplinary field of data-driven drug discovery in healthcare. Thus, rise in investment in R&D activities is acting as an opportunity for market growth.

Challenge

• Lack of skilled professionals

The shortage of skilled professionals is expected to hamper the market growth. The employees have to re-train or learn new skill sets to work efficiently on the complex AI machines to get the desired results for the drug. This challenge that prevents full-fledged adoption of AI in the pharmaceutical industry includes the lack of skilled personnel to operate AI-based platforms, limited budget for small organizations, apprehension of replacing humans leading to job loss, skepticism about the data generated by AI, and the black box phenomenon (that is, how the conclusions are reached by the AI platform). The shortage of skills acts as a major hindrance to drug discovery through AI, discouraging companies to adopt AI-based machines for drug discovery.

As skill demands are too high, it has manifested as a challenge to retain and manage skill-specified professionals. Moreover, technological advancement is another aspect that leads to the increased demand for skilled professionals. There is an urgent need for the education of professionals for AI-based technology. Lack of trained and experienced professionals and persistent skill gaps limit the employability prospects and access to quality jobs. It is therefore apparent that the availability of professionals equipped with adequate skills is challenging the market growth.

Post-COVID-19 Impact on Middle East and Africa Artificial Intelligence (AI) In Drug Discovery Market

The COVID-19 outbreak had a beneficial impact on the expansion of AI in drug discovery industry due to its widespread use by various organizations for the identification as well as screening of existing medicines used in the treatment of COVID-19. AI is useful in detecting active chemicals for the prevention of SARS-CoV, HIV, SARS-CoV-2, influenza virus, and others. During the pandemic, economies all over the world relied on AI-based medication discovery rather than traditional vaccine detection processes, which take years to create and are equally expensive, contributing to the growth of the market.

Manufacturers are making various strategic decisions to bounce back post-COVID-19. The players are conducting multiple R&D activities to improve the technology involved in the Wireless microphone. With this, the companies will bring advanced and accurate AI software to the market.

Recent Developments

• In March 2022, NVIDIA Corporation launched Clara Holoscan MGX to develop and deploy real-time AI applications. Clara Holoscan MGX expands the Clara Holoscan platform to provide an all-in-one, medical-grade reference architecture, as well as long-term software support, to accelerate innovation in the medical device industry. This will help the company for better AI performance in health sector for surgery, diagnostics, and drug discovery.
• In May 2022, Benevolent AI, a leading clinical-stage AI-enabled drug discovery company, announced that AstraZeneca has selected an additional novel target for Idiopathic Pulmonary Fibrosis (IPF) for its drug development portfolio, resulting in a milestone payment to Benevolent AI.  This is the third novel target from the collaboration that has been identified using the Benevolent Platform across two disease areas, IPF and chronic kidney disease, and subsequently validated and selected for portfolio entry by AstraZeneca. This builds upon the recent extension of the collaboration with AstraZeneca to include two new disease areas, systemic lupus erythematosus, and heart failure, signed in January 2022. This has helped the company to make its collaboration stronger.

Middle East and Africa Artificial Intelligence (AI) In Drug Discovery Market Scope

Middle East and Africa Artificial Intelligence (AI) in drug discovery market is segmented into application, technology, drug type, offering, indication, and end use. The growth among segments helps you analyze niche pockets of growth and strategies to approach the market and determine your core application areas and the difference in your target markets.

 APPLICATION

• Novel Drug Candidates
• Drug Optimization and Repurposing Preclinical Testing and Approval
• Drug Monitoring
• Finding New Diseases Associated Targets and Pathways
• Understanding Disease Mechanisms
• Aggregating and Synthesizing Information
• Formation & Qualification of Hypotheses
• De Novo Drug Design
• Finding Drug Targets of an Old Drug
• Others

Based on application, the market is segmented into novel drug candidates, drug optimization and repurposing preclinical testing and approval, drug monitoring, finding new diseases associated targets and pathways, understanding disease mechanisms, aggregating and synthesizing information, formation & qualification of hypotheses, de novo drug design, finding drug targets of an old drug, and others.

 TECHNOLOGY

• Machine Learning (ML)
• Deep Learning (DL)
• Natural Language Processing (NLP)
• Others

Based on technology, the market is segmented into Machine Learning (ML), Deep Learning (DL), Natural Language Processing (NLP), and others.

 DRUG TYPE

• Small Molecule
• Large Molecule

Based on drug type, the market is segmented into small molecule and large molecule.

OFFERING

• Software
• Services

Based on offering, the market is segmented into software and services.

INDICATION

• Immuno-Oncology
• Neurodegenerative Diseases
• Cardiovascular Diseases
• Metabolic Diseases
• Others

Based on indication, the market is segmented into immuno-oncology, neurodegenerative diseases, cardiovascular diseases, metabolic diseases, and others.

END USE

• Pharmaceutical & Biotechnology Companies
• Contract Research Organizations (CROs)
• Research Centers and Academic Institutes
• Others

Based on end use, the market is segmented into pharmaceutical & biotechnology companies, Contract Research Organizations (CROs), research centers and academic institutes, and others.

Middle East and Africa Artificial Intelligence (AI) in Drug Discovery Market Regional Analysis/Insights

Middle East and Africa Artificial Intelligence (AI) in drug discovery market is analyzed and market size information is provided by application, technology, drug type, offering, indication, and end use.

The countries covered in this market report are U.A.E, Israel, South Africa, Saudi Arabia, Egypt, rest of Middle East and Africa.

• In 2022, Middle East and Africa is dominating due to the increase in government funding. South Africa is expected to grow due to rise in R&D activities for AI in drug discovery.

The country section of the report also provides individual market impacting factors and changes in regulation in the market domestically that impact the current and future trends of the market. Data points such as new sales, replacement sales, country demographics, regulatory acts, and import-export tariffs are some of the major pointers used to forecast the market scenario for individual countries. Also, presence and availability of Middle East and Africa brands and their challenges faced due to large or scarce competition from local and domestic brands, and impact of sales channels are considered while providing forecast analysis of the country data.

Competitive Landscape and Middle East and Africa Artificial Intelligence (AI) In Drug Discovery Market Share Analysis

Middle East and Africa Artificial Intelligence (AI) in drug discovery market competitive landscape provides details by competitor. Details included are company overview, company financials, revenue generated, market potential, investment in research and development, new market initiatives, production sites and facilities, company strengths and weaknesses, product launch, product trials pipelines, product approvals, patents, product width and breath, application dominance, technology lifeline curve. The above data points provided are only related to the company’s focus on the Middle East and Africa Artificial Intelligence (AI) in drug discovery market.

이 시장에서 활동하는 주요 기업으로는 NVIDIA Corporation, IBM Corp., Atomwise Inc., Microsoft, Benevolent AI, Aria Pharmaceuticals, Inc., DEEP GENOMICS, Exscientia, Cloud, Insilico Medicine, Cyclica, NuMedii, Inc., Envisagenics, Owkin Inc., BERG LLC, Schrödinger, Inc., XtalPi Inc., BIOAGE Inc. 등이 있습니다.

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