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市場調查報告書
商品編碼
1519968
2024-2031 年全球人工智慧藥物發現市場Global AI in Drug Discovery Market 2024-2031 |
藥物發現中的人工智慧市場規模、佔有率和趨勢分析報告,按組件(軟體和服務)、治療領域(腫瘤學、傳染病、神經病學、代謝疾病、心血管疾病、免疫學等)、應用(藥物最佳化和再利用) 、臨床前測試和藥物篩選)以及最終用戶(製藥和生物技術公司、合約研究組織 (CRO) 和研究中心以及學術和政府機構),預測期 (2024-2031)
預計在預測期內(2024-2031 年),藥物發現市場中的人工智慧將以 40.3% 的指數複合年成長率成長。市場成長歸因於對藥物最佳化、有影響力的目標重點和虛擬篩選的需求增加,Al 透過鼓勵全球更多樣化的測試來加快研究速度。根據美國食品藥物管理局(FDA)藥物評估與研究中心(CDER)統計,2023年,美國將有55種新藥核准上市。 Rett 表示,到2023 年,批准的55 種新藥中有28 種(即51.0%)獲得孤兒藥稱號,因為它們針對的是罕見疾病,包括損害神經系統的退化性疾病、念珠菌血症和侵襲性念珠菌症(這是一種嚴重且危及生命的真菌感染)綜合症,一種影響大腦發育的遺傳性神經系統疾病。
AI in Drug Discovery Market Size, Share & Trends Analysis Report by Component (Software and Services), by Therapeutic Area (Oncology, Infectious Diseases, Neurology, Metabolic Diseases, Cardiovascular Diseases, Immunology, and others), by Application (Drug Optimization and Repurposing, Preclinical Testing and Drug Screening) and by End-Users (Pharmaceutical & Biotechnology Companies, Contract Research Organizations (CROs) and Research Centers and Academic & Government Institutes), Forecast Period (2024-2031)
AI in drug discovery market is anticipated to grow at an exponential CAGR of 40.3% during the forecast period (2024-2031). The market growth is attributed to the increased demand for drug optimization, impactful target emphasis, and virtual screening, Al speeds up research by encouraging more diverse testing globally. According to the US Food and Drug Administration (FDA) Center for Drug Evaluation and Research (CDER), in 2023, 55 novel drugs were approved and marketed in the US. In 2023, 28 of 55, or 51.0% of novel drug approvals received orphan drug designation as they target rare diseases, including degenerative disease that damages the nervous system, Candidemia, and invasive candidiasis, which are serious and life-threatening fungal infections, Rett syndrome, a genetic, neurological disorder that affects brain development.
Market Dynamics
Increasing Role of Machine Learning (ML) in Predicting Drug Efficacy and Toxicity
Predicting the toxicity and efficacy of possible therapeutic molecules is one of the main uses of AI in medicinal chemistry. Traditional drug development methods frequently depend on labor-intensive and time-consuming experiments to evaluate a compound's possible effects on the human body. These restrictions can be solved by AI methods such as machine learning. ML algorithms can spot patterns and trends that human researchers would miss based on the examination of copious amounts of data. In comparison to employing traditional techniques, this can allow the proposal of new bioactive substances with minimal side effects in a significantly faster manner.
Integration of AI in Drug Discovery Process and Potential Cost Savings
Developing new molecules with characteristics and functions is another important way that AI is being used in medication discovery. Conventional techniques frequently rely on the labor- and slow-intensive process of identifying and modifying already-existing molecules. On the other hand, AI-based methods can make it possible to build new compounds quickly and effectively with desired characteristics and activities. For instance, to propose new therapeutic drug molecules with desirable properties such as solubility and activity, a deep learning (DL) algorithm was recently trained on a dataset of known drug compounds and their corresponding properties. This shows the potential of these methods for the quick and effective design of new drug candidates.
Market Segmentation
Oncology is Projected to Hold the Largest Segment
The oncology segment is expected to hold the largest share of the market. The primary factors supporting the growth include enhancing current AI systems that are made to sift through massive data sets and derive insight. AI can be leveraged to provide patients with individualized treatments. In addition to providing patient histories and genetic sequences, scans can be used to establish a pattern for early cancer detection and patient-specific medication delivery. For instance, in April 2024, the National Institutes of Health (NIH) developed an artificial intelligence (AI) tool that uses data from individual cells inside tumors to predict whether a person's cancer will respond to a specific drug. In the new study, the researchers investigated whether they could use a machine learning technique called transfer learning to train an AI model to predict drug responses using widely available bulk RNA sequencing data, but then fine-tune that model using single-cell RNA sequencing data.
Drug Optimization and Repurposing Segment to Hold a Considerable Market Share
The drug optimization and repurposing segment is expected to hold a considerable share of the market. Drug efficacy, as well as side effects, can be researched using innovative AI systems such as Deep Learning (DL) and drug modeling. The development of AI technology has also facilitated the study, comparison, and repurposing of medications into more efficient forms, reducing adverse effects and increasing overall efficacy. For instance, in January 2024, Accenture announced it has made a strategic investment, through Accenture Ventures, in QuantHealth, an AI-powered clinical trial design company that simulates clinical trials in the cloud, allowing pharmaceutical and biotech companies to more quickly and cost-effectively develop treatments for patients. By simulating trials at scale, QuantHealth's platform can lower risks, and expedite, and optimize drug development.
Global AI in drug discovery market is further segmented based on geography including North America (the US, and Canada), Europe (UK, Italy, Spain, Germany, France, and the Rest of Europe), Asia-Pacific (India, China, Japan, South Korea, and Rest of Asia-Pacific), and the Rest of the World (the Middle East & Africa, and Latin America).
Growing Research and Pharmaceutical & Biotechnology Companies in Asia-Pacific
North America Holds Major Market Share
North America holds a significant share owing to numerous prominent AI in drug discovery companies and providers such as NuMedii, Inc., NVIDIA Corp., Recursion Pharmaceuticals Inc., Schrodinger, Inc., and XtalPi Inc. in the region. The market growth is attributed to increasing medication research, design, and repurposing, major US IT corporations have collaborated with esteemed institutes. AI is also being used to analyze diseases and draw relevant conclusions that can help with disease management. According to the US Food and Drug Administration (US FDA), in March 2024, a significant increase in the number of drug and biologic application submissions using AI/ML components over the past few years, with more than 100 submissions reported in 2021. Healthcare organizations in the drug development area are implementing generative AI to open up new business potential for physicians, pharmaceutical companies, and hospitals. These include developing smarter digital assistants, collecting better patient data to support early disease identification, and searching for billions of pharmacological molecules to advance medicine. For instance, in March 2024, NVIDIA Healthcare introduced generative AI Microservices to advance drug discovery, MedTech, and Digital Health. Additionally, NVIDIA accelerated software development kits and tools, including Parabricks(R), MONAI, NeMo(TM), Riva, and Metropolis, can now be accessed as NVIDIA CUDA-X(TM) microservices to accelerate healthcare workflows for drug discovery, medical imaging, and genomics analysis.
The major companies serving the AI in drug discovery market include Merck KGaA, NVIDIA Corp., Recursion Pharmaceuticals Inc., Schrodinger, Inc., and Tencent Holdings Ltd., among others. The market players are increasingly focusing on business expansion and product development by applying strategies such as collaborations, mergers, and acquisitions to stay competitive in the market.
Recent Development