市場調查報告書
商品編碼
1380467
全球計算生物學市場 2023-2030Global Computational Biology Market 2023-2030 |
預計全球計算生物學市場在預測期內將以 15.8% 的CAGR成長。計算生物學是利用資料分析、數學建模和計算模擬來理解生物系統和關係。這是一個跨學科領域,使用計算方法來分析大量生物資料,例如基因序列、細胞群或蛋白質樣本。癌症、心臟病和糖尿病等慢性疾病是全球主要的健康問題。計算生物學被用於開發慢性病的新藥和治療方法,以及改善這些疾病的診斷和管理。這是市場成長的關鍵驅動力之一。計算生物學工具可用於分析基因組、轉錄組和蛋白質資料數據的大型資料集,以識別在慢性疾病的發生和進展中發揮作用的新基因和蛋白質。一旦確定了這些新的藥物標靶,研究人員就可以開始開發針對這些分子的新藥。計算生物學工具可用於設計更有效且副作用更少的新藥。例如,計算生物學工具可用於模擬藥物分子與其目標蛋白質之間的相互作用,以識別最有效的藥物結構。計算生物學工具可用於在臨床試驗之前預測藥物的毒性和功效。這有助於節省藥物開發過程中的時間和金錢。計算生物學工具可用於開發新的診斷測試並為慢性病患者制定個人化的治療計劃。例如,計算生物學工具可用於分析患者的遺傳圖譜,以確定對該特定患者最有效且最安全的治療方法。
計算生物學如何用於開發治療慢性疾病的新藥物的一個例子是標靶癌症療法的開發。標靶癌症療法是針對參與癌細胞生長和存活的特定分子的藥物。計算生物學工具被用來識別癌症的新藥物標靶,設計新的癌症標靶療法,並預測臨床試驗中癌症標靶療法的療效。計算生物學如何用於改善慢性病的診斷和管理的另一個例子是糖尿病個人化醫療方法的發展。糖尿病是一種慢性疾病,會影響人體產生或使用胰島素(一種調節血糖水平的荷爾蒙)的能力。計算生物學工具被用來分析糖尿病患者的基因圖譜,以確定針對該特定患者的最有效和最安全的治療方法。
Title: Global Computational Biology Market Size, Share & Trends Analysis Report Market by Application (Drug discovery and development, Clinical trials, Human body simulation software, Preclinical drug development, and Others (Cellular and biological simulation)), by Service (Software platforms Infrastructure and hardware, Consulting services), End-use(Academia and research, Pharmaceutical and biotechnology companies, Clinical diagnostics companies)Forecast Period (2023-2030).
The global Computational biology market is anticipated to grow at a considerable CAGR of 15.8% during the forecast period. Computational biology is the use of data analysis, mathematical modeling, and computational simulations to understand biological systems and relationships. It's an interdisciplinary field that uses computational methods to analyze large collections of biological data, such as genetic sequences, cell populations, or protein samples. Chronic diseases, such as cancer, heart disease, and diabetes, are a major global health problem. Computational biology is being used to develop new drugs and treatments for chronic diseases, as well as to improve the diagnosis and management of these diseases. This is one of the key drivers for the market growth. Computational biology tools can be used to analyze large datasets of genomic, transcriptomic, and proteomic data to identify new genes and proteins that play a role in the development and progression of chronic diseases. Once these new drug targets have been identified, researchers can begin to develop new drugs that target these molecules. Computational biology tools can be used to design new drugs that are more effective and have fewer side effects. For example, computational biology tools can be used to simulate the interaction between a drug molecule and its target protein to identify the most effective drug structure. Computational biology tools can be used to predict the toxicity and efficacy of drugs before they are tested in clinical trials. This can help to save time and money in the drug development process. Computational biology tools can be used to develop new diagnostic tests and to develop personalized treatment plans for patients with chronic diseases. For example, computational biology tools can be used to analyze the patient's genetic profile to identify the most effective and safest treatments for that particular patient.
One example of how computational biology is being used to develop new drugs for chronic diseases is the development of targeted cancer therapies. Targeted cancer therapies are drugs that target specific molecules that are involved in the growth and survival of cancer cells. Computational biology tools are being used to identify new drug targets for cancer, to design new targeted cancer therapies, and to predict the efficacy of targeted cancer therapies in clinical trials. Another example of how computational biology is being used to improve the diagnosis and management of chronic diseases is the development of personalized medicine approaches for diabetes. Diabetes is a chronic disease that affects the body's ability to produce or use insulin, a hormone that regulates blood sugar levels. Computational biology tools are being used to analyze the genetic profile of patients with diabetes to identify the most effective and safest treatments for that particular patient.
The global Computational biology market is segmented based on application, service, and end-use. Based on the application, the market is segmented into drug discovery and development, clinical trials, human body simulation software, preclinical drug development, and others (cellular and biological simulation) and others. Based on service, the market is sub-segmented into software platforms, infrastructure, and hardware, consulting services). Based on end-use, the market is sub-segmented into academia and research, pharmaceutical and biotechnology companies, clinical diagnostics companies, and others.
Pharmaceutical and biotechnology companies hold the major market share in the global computational biology market by end-use. This is because pharmaceutical and biotechnology companies are at the forefront of developing new drugs and treatments for diseases, and computational biology is playing an increasingly important role in this process. One example of how pharmaceutical and biotechnology companies are using computational biology is in the drug discovery process. Traditionally, drug discovery has been a long and expensive process, involving the screening of millions of compounds to find a few that may have potential therapeutic effects. However, computational biology tools can be used to accelerate this process by helping researchers to identify promising drug targets and to design new drugs that are more likely to be effective.
The global Computational biology market is further segmented based on geography, including North America (the US and Canada), Europe (Italy, Spain, Germany, France, and others), Asia-Pacific (India, China, Japan, South Korea, and others), and the Rest of the World (the Middle East & Africa and Latin America). Among these, North America holds the major market share for many reasons, one of which is the presence of major pharmaceutical and biotechnology companies, as well as leading academic institutions.
The European region is experiencing a consistent growth rate in the global market. The growth of the computational biology market in Europe is being driven by increasing investments in drug discovery and development, as well as the growing adoption of personalized medicine. Europe is a major hub for pharmaceutical and biotechnology companies, and these companies are investing heavily in drug discovery and development. Computational biology is playing an increasingly important role in this process, helping researchers to identify new drug targets, design new drugs, and predict the toxicity and efficacy of drugs. Personalized medicine is a new approach to healthcare that affects treatments to the individual patient's genetic profile. Computational biology tools are essential for personalized medicine, as they can be used to analyze the patient's genetic profile to identify the most effective and safest treatments for that particular patient.
The major companies serving the global Computational biology market include: Dassault Systemes, Illumina, Inc., QIAGEN GmbH, Schrodinger, LLC., and Thermo Fisher Scientific Inc. among others. The market players are considerably contributing to the market growth by the adoption of various strategies, including mergers and acquisitions, partnerships, collaborations, and new product launches, to stay competitive in the market. For instance, in May 2022, global professional services firm ZS acquired a Danish informatics and systems biology company named Intomics. The 42 members of Intomics will join ZS's staff of 12,000 employees worldwide and strengthen its team of molecular natives who combine scientific, data science, and technology expertise with a research mindset to advance the adoption of in-silico methods in drug discovery.