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計算生物學市場 - 2018-2028 年全球產業規模、佔有率、趨勢、機會和預測，按應用、工具、服務、最終用戶、地區、競爭預測和機會細分，2018-2028F

Computational Biology Market - Global Industry Size, Share, Trends, Opportunity, and Forecast, 2018-2028 Segmented By Application, By Tool, By Service, By End User, By Region, By Competition Forecast & Opportunities, 2018-2028F
出版日期: | 出版商: TechSci Research | 英文 190 Pages | 商品交期: 2-3個工作天內

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簡介目錄

2022年，全球計算生物學市場估值達到48.9億美元，預計在預測期內將出現顯著成長，預計到2028年年複合成長率(CAGR)為7.49%。全球計算生物學市場涉及利用計算技術（包括演算法、資料分析和數學建模）來理解和審查生物資料。該領域在生命科學的各個領域發揮關鍵作用，包括基因組學、蛋白質組學、藥物發現和個人化醫療。

主要市場促進因素

生物數據的爆炸性成長

市場概況
預測期 2024-2028
2022 年市場規模 48.9億美元
2028 年市場規模 75.1億美元
2023-2028 年年複合成長率 7.49%
成長最快的細分市場 藥物發現與疾病建模
最大的市場 北美洲

生物學領域已經進入了一個新時代，其特徵是生物資料前所未有的爆炸性成長。從基因組定序到複雜生物系統的研究，產生的資料量和複雜性令人震驚。海量的資料催生了計算生物學領域，該領域利用先進的演算法和資料分析技術來理解這些豐富的資訊。基因組定序一直是生物資料激增的驅動力。 2003 年完成的人類基因組計畫標誌著基因組學的一個重要里程碑，但這只是開始。如今，高通量定序技術使得快速且經濟高效地對整個基因組進行定序成為可能。這產生了龐大的基因組資料儲存庫，為遺傳學、演化和疾病易感性提供了重要的見解。基因組學只是生物資料爆炸的一個面向。研究基因表現模式的轉錄組學和專注於蛋白質的蛋白質組學也促進了資料的湧入。研究人員現在可以檢查生物體的整個轉錄組或蛋白質組，從而深入了解基因調控、蛋白質功能和疾病機制。單細胞測序技術將生物學研究提升到了更精細的水平。科學家現在可以分析組織內的單一細胞，而不是研究組織或細胞群。這項技術徹底改變了我們對細胞異質性、組織發育和疾病進展的理解。然而，它會產生大量資料，需要複雜的計算分析。多個組學資料來源（基因組學、轉錄組學、蛋白質組學、代謝組學等）的整合是全面理解複雜生物系統的強大方法。然而，它使資料量倍增。計算生物學在協調和解釋這些綜合資料集、實現對生物現象的整體洞察方面發揮關鍵作用。製藥業依靠計算生物學來加速藥物發現。透過分析大量化合物資料集及其與生物分子的相互作用，研究人員可以識別潛在的候選藥物、預測其功效並最佳化其特性。這種數據驅動的方法顯著減少了將新藥推向市場的時間和成本。

基因組學的進展

過去幾十年來，基因組學領域取得了顯著的進步，徹底改變了我們對遺傳學、疾病和生命本身複雜性的理解。這一轉變的核心是基因組學和計算生物學之間的協同作用。 2003 年完成的人類基因組計畫標誌著基因體學的轉捩點。對人類基因組中的所有基因進行繪圖和測序是一項巨大的合作努力。這項里程碑式的成就為基因組學革命奠定了基礎，促進了高通量 DNA 定序技術的快速發展。新一代定序 (NGS) 技術的出現改變了基因組學的遊戲規則。這些儀器可以在短時間內對大量 DNA 進行定序，單次運行即可產生數 TB 的資料。資料輸出的指數級成長需要先進的計算工具和專業知識來有效地處理和分析資料。高通量測序的激增導致了基因組資料的爆炸性成長。研究人員現在不僅可以對人類基因組進行定序，還可以對無數其他物種的基因組進行定序，揭示對演化、遺傳多樣性和疾病遺傳基礎的重要見解。如此豐富的資料刺激了對計算生物學解決方案提取有意義資訊的需求。經濟實惠的直接面對消費者的 DNA 檢測的出現使基因組學變得普惠大眾。個人現在可以獲得他們的遺傳訊息，這可以提供對血統、疾病傾向和生活方式建議的見解。人們對個人基因組學日益成長的興趣產生了對能夠分析和解釋這些個體基因譜的計算工具的巨大需求。基因組醫學利用基因組資料來指導臨床決策。它能夠識別與疾病相關的基因突變，促進早期診斷，並支持個人化治療計劃。隨著基因組醫學越來越融入醫療保健系統，計算生物學工具在將基因組資訊轉化為可行的見解方面發揮核心作用。傳統的基因組技術經常分析細胞群，掩蓋組織內的多樣性。單細胞基因組學技術現在允許研究人員研究單一細胞，揭示複雜的細胞異質性。這些技術產生巨大的資料集，需要計算方法來揭示複雜的細胞景觀。

藥物發現與開發

藥物發現和計算生物學領域正在經歷令人興奮的融合。隨著製藥業競相開發創新藥物，計算生物學成為不可或缺的盟友。對治療從癌症到罕見遺傳性疾病等多種疾病的新型藥物化合物的需求持續成長。藥物發現是一個漫長且資源密集的過程，但它對於改善醫療保健結果和患者的生活品質至關重要。計算生物學透過加速藥物開發的各個階段提供關鍵支持。計算生物學允許研究人員進行電腦（基於電腦）藥物篩選。這種方法涉及模擬潛在藥物化合物與目標分子（例如蛋白質或酶）之間的相互作用。透過虛擬篩選數千種化合物，研究人員可以更快、更低成本地識別潛​​在的候選藥物。計算生物學在預測藥物-標靶相互作用方面發揮關鍵作用。演算法和機器學習模型分析生物資料，以確定藥物分子如何與特定細胞標靶相互作用。這種預測能力顯著縮短了藥物開發時間並減少了實驗失敗。一旦確定了潛在的候選藥物，計算生物學就有助於最佳化其特性。研究人員可以修改先導化合物的化學結構，以增強其功效、降低毒性並提高生物利用度。這個迭代過程被稱為先導最佳化，在很大程度上依賴計算建模和模擬。了解疾病所涉及的潛在生物學途徑對於藥物開發至關重要。計算生物學工具透過分析複雜的組學資料來幫助闡明這些途徑。這些知識指導研究人員確定關鍵標靶並開發調節特定生物過程的藥物。

協作和跨產業夥伴關係

在當今互聯的世界中，協作和夥伴關係是創新和進步的強大催化劑。全球計算生物學市場也不例外，從跨產業合作中受益匪淺。計算生物學領域的合作促進了知識和專業知識的交流。學術機構和研究組織往往擁有前沿的研究成果，而製藥公司則帶來實際的藥物開發經驗。當這些實體聚集在一起時，它們將理論見解與現實世界的應用結合，推動該領域的創新。計算生物學的主要挑戰之一是獲取高品質的生物資料。研究組織和技術公司之間的合作可以提供寶貴的資料資源。例如，公私合作夥伴關係可以使研究人員能夠存取大型資料集，使他們能夠進行全面分析並開發更準確的模型。協作努力可以匯集人力和財力資源。這種資源協同可以加速研發進程。當多個實體為一個專案做出貢獻時，就有可能處理更廣泛和複雜的任務，例如大規模基因組研究或藥物發現計劃。計算生物學本質上涉及多個學科，包括生物學、電腦科學和統計學。合作計畫通常涉及來自這些不同背景的研究人員。這種跨學科方法鼓勵新的視角和創造性的問題解決，從而帶來單一組織內不可能的突破。製藥業擴大轉向計算生物學來進行藥物發現。製藥公司和計算生物學專家之間的合作可以加快潛在候選藥物的識別。跨產業合作夥伴關係促進了計算工具的應用，以預測藥物與標靶的相互作用並最佳化先導化合物。

主要市場挑戰

數據複雜性和數量

生物資料的指數成長是一把雙面刃。雖然它提供了豐富的資訊，但它在資料複雜性和數量方面也提出了重大挑戰。處理、儲存和分析海量資料集需要強大的運算基礎設施和高效的演算法。

資料隱私和安全

生物資料，尤其是基因組資訊，非常敏感，受到嚴格的隱私法規的約束。確保資料隱私同時允許進行有意義的分析是一種微妙的平衡。計算生物學市場必須解決這些問題，以獲得公眾信任並遵守不斷發展的資料保護法。

互通性和標準化

計算生物學工具和平台的資料格式和分析方法通常各不相同。缺乏標準化阻礙了資料共享和協作。建立通用資料標準和可互通的工具對於克服這項挑戰至關重要。

熟練勞動力短缺

計算生物學領域需要多學科技能，包括生物學、電腦科學、數學和統計學。這些領域缺乏具備專業知識的專業人士，這使得組織很難找到並留住合格的人才。

主要市場趨勢

單細胞組學革命

單細胞測序和組學技術正在迅速發展。這些技術使研究人員能夠剖析複雜組織內單一細胞的分子特徵。隨著單細胞資料解析度的提高，計算生物學將在分析和解釋這些複雜的資料集方面發揮關鍵作用。期待為單細胞組學分析量身定做的演算法和工具的創新。

空間轉錄組學

空間轉錄組學是一個將基因組學與空間資訊結合的新興領域。它使研究人員能夠繪製組織內基因表現的圖譜，從而深入了解細胞的空間組織。空間資料分析的計算方法將受到很高的需求，這為研究組織結構和疾病機制提供了新的方法。

多組學整合

整合多個組學資料來源，例如基因組學、轉錄組學、蛋白質組學和代謝組學，提供生物系統的整體視圖。促進多組學資料整合和分析的計算工具的需求量很大，使研究人員能夠發現複雜的相互作用和途徑。

區塊鏈資料安全

資料安全和隱私在計算生物學中至關重要，特別是在處理敏感的基因組資訊時。區塊鏈技術可望實現安全、透明的資料管理，確保生物資料的完整性和隱私性。期望看到基於區塊鏈的資料安全和可追溯性解決方案。

細分市場洞察

服務洞察

根據服務類別，到 2022 年，合約細分市場將成為全球計算生物學市場的主導者。這可以歸因於合約服務與全球提供的內部服務相比的成本效益。合約研究組織 (CRO) 服務提供者與客戶密切合作，制定量身定做的計劃，從而成為市場成長的催化劑。

最終使用者見解

預計商業部門將成為市場收入的主要貢獻者。政府和商業實體對基因工程研發（R＆D）以及創新藥物開發的投資增加是導致計算生物學需求增加的重要因素。

例如，2021 年 5 月，世界衛生組織 (WHO) 和瑞士聯邦簽署了一份合作備忘錄 (MoU)，建立首個 WHO BioHub 設施，作為 WHO BioHub 系統的一部分。該設施位於瑞士施皮茨，是安全接收、定序、儲存和製備生物材料以分發給其他實驗室的中心。它還在風險評估中發揮著至關重要的作用，並支持全球針對病原體的準備工作。同樣，歐盟委員會對「地平線 2020」計畫的大量投資旨在消除創新障礙，促進改善公共和私營部門之間的合作，從而促進創新。這些發展預計將促進對計算生物學不斷成長的需求，從而推動該細分市場的收入成長。

區域洞察

北美目前在計算生物學市場佔據主導地位，預計將在未來幾年保持領先地位。尤其是美國，是合成生物學領域的領導者，合成生物學是一門專注於生物系統的設計、操作和重新編程的新興學科。自 2005 年以來，美國政府一直大力支持計算生物學和合成生物學，為其發展投入了超過 10 億美元。美國政府在推動計算生物學方面的年平均投資估計約為 1.4 億美元。

個人化醫療的興起促進了醫療機構、政府機構和研究人員之間的合作，以加速創造有效的治療方法。例如，2020年，Summit Biolabs Inc.與科羅拉多個人化醫療中心（CCPM）建立了全面的策略合作夥伴關係，進行唾液液體活體組織切片測試的研究、開發和商業化，以用於癌症的早期檢測、新冠病毒的診斷。19. 其他病毒感染。同樣，2020 年4 月，HealthCare Global Enterprises 和Strand Life Sciences 推出了StrandAdvantage500，這是一種基於下一代定序(NGS) 的檢測方法，可在統一的工作流程中評估從患者腫瘤中提取的DNA 和RNA中與癌症相關的遺傳改變。此外，2021年7月，Indivumed GmbH推出了“travel”，這是一個專為腫瘤學和精準醫學設計的創新人工智慧發現平台。該平台將 IndivuType 廣泛的多組學資料與複雜的疾病模型、高度先進的自動化機器學習工具以及一整套先進的分析功能相結合。

美國的整體計算生物學市場預計在未來幾年將大幅成長，這主要是由於在藥物開發方面的大量投資，這是全球最高的。

目錄

第 1 章：產品概述

  • 市場定義
  • 市場範圍
    • 涵蓋的市場
    • 研究年份
    • 主要市場區隔

第 2 章：研究方法

  • 研究目的
  • 基線方法
  • 主要產業夥伴
  • 主要協會和二手資料來源
  • 預測方法
  • 數據三角測量與驗證
  • 假設和限制

第 3 章：執行摘要

  • 市場概況
  • 主要市場細分概述
  • 主要市場參與者概述
  • 重點地區/國家概況
  • 市場促進因素、挑戰、趨勢概述

第 4 章：客戶之聲

第 5 章：全球計算生物學市場展望

  • 市場規模及預測
    • 按價值
  • 市佔率及預測
    • 按應用（細胞和生物模擬、藥物發現和疾病建模、臨床前藥物開發、臨床試驗、人體模擬軟體）
    • 依工具（資料庫、基礎設施（硬體）、分析軟體和服務）
    • 按服務（內部、聯絡）
    • 按最終用戶（學術界、工業界和商業界）
    • 按地區
    • 按公司分類 (2022)
  • 產品市場地圖
    • 按應用
    • 按工具
    • 按服務
    • 按最終用戶
    • 按地區

第 6 章：北美計算生物學市場展望

  • 市場規模及預測
    • 按價值
  • 市佔率及預測
    • 按應用（細胞和生物模擬、藥物發現和疾病建模、臨床前藥物開發、臨床試驗、人體模擬軟體）
    • 依工具（資料庫、基礎設施（硬體）、分析軟體和服務）
    • 按服務（內部、聯絡）
    • 按最終用戶（學術界、工業界和商業界）
    • 按國家/地區
  • 北美：國家分析
    • 美國
    • 加拿大
    • 墨西哥

第 7 章：歐洲計算生物學市場展望

  • 市場規模及預測
    • 按價值
  • 市佔率及預測
    • 按應用（細胞和生物模擬、藥物發現和疾病建模、臨床前藥物開發、臨床試驗、人體模擬軟體）
    • 依工具（資料庫、基礎設施（硬體）、分析軟體和服務）
    • 按服務（內部、聯絡）
    • 按最終用戶（學術界、工業界和商業界）
    • 按國家/地區
  • 歐洲：國家分析
    • 德國
    • 英國
    • 法國
    • 義大利
    • 西班牙

第 8 章：亞太計算生物學市場展望

  • 市場規模及預測
    • 按價值
  • 市佔率及預測
    • 按應用（細胞和生物模擬、藥物發現和疾病建模、臨床前藥物開發、臨床試驗、人體模擬軟體）
    • 依工具（資料庫、基礎設施（硬體）、分析軟體和服務）
    • 按服務（內部、聯絡）
    • 按最終用戶（學術界、工業界和商業界）
    • 按國家/地區
  • 亞太地區：國家分析
    • 中國
    • 日本
    • 印度
    • 澳洲
    • 韓國

第 9 章：南美洲計算生物學市場展望

  • 市場規模及預測
    • 按價值
  • 市佔率及預測
    • 按應用（細胞和生物模擬、藥物發現和疾病建模、臨床前藥物開發、臨床試驗、人體模擬軟體）
    • 依工具（資料庫、基礎設施（硬體）、分析軟體和服務）
    • 按服務（內部、聯絡）
    • 按最終用戶（學術界、工業界和商業界）
    • 按國家/地區
  • 南美洲：國家分析
    • 巴西
    • 阿根廷
    • 哥倫比亞

第 10 章：中東和非洲計算生物學市場展望

  • 市場規模及預測
    • 按價值
  • 市佔率及預測
    • 按應用（細胞和生物模擬、藥物發現和疾病建模、臨床前藥物開發、臨床試驗、人體模擬軟體）
    • 依工具（資料庫、基礎設施（硬體）、分析軟體和服務）
    • 按服務（內部、聯絡）
    • 按最終用戶（學術界、工業界和商業界）
    • 按國家/地區
  • MEA：國家分析
    • 南非計算生物學
    • 沙烏地阿拉伯計算生物學
    • 阿拉伯聯合大公國運算生物學
    • 科威特計算生物學

第 11 章：市場動態

  • 促進要素
  • 挑戰

第 12 章：市場趨勢與發展

  • 近期發展
  • 併購
  • 產品發布

第 13 章：波特的五力分析

  • 產業競爭
  • 新進入者的潛力
  • 供應商的力量
  • 客戶的力量
  • 替代產品的威脅

第14章：競爭格局

  • 商業概覽
  • 產品供應
  • 最近的發展
  • 財務（據報導）
  • 主要人員
  • SWOT分析
    • Dassault Systemes SE
    • Certara Inc
    • Chemical Computing Group ULC
    • Compugen Ltd
    • Rosa & Co. LLC
    • GeneData AG
    • Insilico Biotechnology AG
    • Instem PLC
    • Strand Life Sciences Pvt Ltd
    • Schrodinger Inc

第 15 章：策略建議

第 16 章：關於我們與免責聲明

簡介目錄
Product Code: 16136

In 2022, the Global Computational Biology Market reached a valuation of USD 4.89 billion and is expected to experience significant growth in the projected period, with an anticipated Compound Annual Growth Rate (CAGR) of 7.49% through 2028. The Global Computational Biology Market pertains to the utilization of computational techniques, which encompass algorithms, data analysis, and mathematical modeling, to comprehend and scrutinize biological data. This field plays a pivotal role across various domains of life sciences, encompassing genomics, proteomics, drug discovery, and personalized medicine.

Key Market Drivers

Explosion in Biological Data

Market Overview
Forecast Period2024-2028
Market Size 2022USD 4.89 Billion
Market Size 2028USD 7.51 Billion
CAGR 2023-20287.49%
Fastest Growing SegmentDrug Discovery and Disease Modelling
Largest MarketNorth America

The field of biology has entered a new era, one characterized by an unprecedented explosion in biological data. From the sequencing of genomes to the study of complex biological systems, the volume and complexity of data being generated are staggering. This deluge of data has given rise to the field of computational biology, which utilizes advanced algorithms and data analysis techniques to make sense of this wealth of information. The sequencing of genomes has been a driving force behind the surge in biological data. The Human Genome Project, completed in 2003, marked a significant milestone in genomics, but it was just the beginning. Today, high-throughput sequencing technologies have made it possible to rapidly and cost-effectively sequence entire genomes. This has led to a vast repository of genomic data, providing critical insights into genetics, evolution, and disease susceptibility. Genomics is just one facet of the biological data explosion. Transcriptomics, which studies gene expression patterns, and proteomics, which focuses on proteins, have also contributed to the data influx. Researchers can now examine the entire transcriptome or proteome of an organism, offering insights into gene regulation, protein function, and disease mechanisms. Single-cell sequencing technologies have taken biological research to a finer level of granularity. Instead of studying tissues or populations of cells, scientists can now analyze individual cells within a tissue. This technology has revolutionized our understanding of cellular heterogeneity, tissue development, and disease progression. However, it generates massive amounts of data that require sophisticated computational analysis. The integration of multiple omics data sources (genomics, transcriptomics, proteomics, metabolomics, etc.) is a powerful approach for understanding complex biological systems comprehensively. However, it multiplies the volume of data exponentially. Computational biology plays a pivotal role in harmonizing and interpreting these integrated datasets, enabling holistic insights into biological phenomena. The pharmaceutical industry relies on computational biology to accelerate drug discovery. By analyzing vast datasets of chemical compounds and their interactions with biological molecules, researchers can identify potential drug candidates, predict their efficacy, and optimize their properties. This data-driven approach significantly reduces the time and cost of bringing new drugs to market.

Advancements in Genomics

The field of genomics has witnessed remarkable advancements over the past few decades, revolutionizing our understanding of genetics, diseases, and the intricacies of life itself. At the heart of this transformation is the synergy between genomics and computational biology. The Human Genome Project, completed in 2003, marked a turning point in genomics. It was a massive collaborative effort to map and sequence all the genes in the human genome. This monumental achievement set the stage for a genomics revolution, catalyzing the rapid development of high-throughput DNA sequencing technologies. Next-generation sequencing (NGS) technologies emerged as game-changers in genomics. These instruments can sequence vast quantities of DNA in a short time, generating terabytes of data in a single run. This exponential increase in data output necessitated advanced computational tools and expertise to process and analyze the data efficiently. The proliferation of high-throughput sequencing has led to an explosion of genomic data. Researchers can now sequence not only human genomes but also the genomes of countless other species, uncovering critical insights into evolution, genetic diversity, and the genetic basis of diseases. This abundance of data fuels the demand for computational biology solutions to extract meaningful information. The advent of affordable direct-to-consumer DNA testing has made genomics accessible to the masses. Individuals can now obtain their genetic information, which can provide insights into ancestry, disease predispositions, and lifestyle recommendations. This growing interest in personal genomics generates a significant need for computational tools that can analyze and interpret these individual genetic profiles. Genomic medicine leverages genomic data to guide clinical decision-making. It enables the identification of genetic mutations linked to diseases, facilitates early diagnosis, and supports personalized treatment plans. As genomic medicine becomes more integrated into healthcare systems, computational biology tools play a central role in translating genomic information into actionable insights. Traditional genomic techniques often analyze populations of cells, masking the diversity within tissues. Single-cell genomics technologies now allow researchers to study individual cells, unveiling intricate cellular heterogeneity. These techniques generate immense datasets, necessitating computational methods to unravel the complex cellular landscapes.

Drug Discovery and Development

The realms of drug discovery and computational biology are experiencing an exciting convergence. As the pharmaceutical industry races to develop innovative drugs, computational biology has emerged as an indispensable ally. The need for novel pharmaceutical compounds to treat a wide range of diseases, from cancer to rare genetic disorders, continues to grow. Drug discovery is a lengthy and resource-intensive process, but it's essential for improving healthcare outcomes and patient quality of life. Computational biology provides crucial support by accelerating various stages of drug development. Computational biology allows researchers to conduct in-silico (computer-based) drug screening. This approach involves simulating the interaction between potential drug compounds and target molecules, such as proteins or enzymes. By virtually screening thousands of compounds, researchers can identify potential drug candidates faster and with lower costs. Computational biology plays a pivotal role in predicting drug-target interactions. Algorithms and machine learning models analyze biological data to determine how a drug molecule will interact with specific cellular targets. This predictive capability significantly shortens the drug development timeline and reduces experimental failures. Once potential drug candidates are identified, computational biology aids in optimizing their properties. Researchers can modify the chemical structure of lead compounds to enhance their efficacy, reduce toxicity, and improve bioavailability. This iterative process, known as lead optimization, relies heavily on computational modeling and simulations. Understanding the underlying biological pathways involved in diseases is critical for drug development. Computational biology tools help elucidate these pathways by analyzing complex omics data. This knowledge guides researchers in identifying key targets and developing drugs that modulate specific biological processes.

Collaboration and Cross-Industry Partnerships

In today's interconnected world, collaboration and partnerships are powerful catalysts for innovation and progress. The Global Computational Biology Market is no exception, benefiting significantly from cross-industry collaborations. Collaborations in the field of computational biology facilitate the exchange of knowledge and expertise. Academic institutions and research organizations often possess cutting-edge research findings, while pharmaceutical companies bring practical drug development experience. When these entities come together, they combine theoretical insights with real-world applications, driving innovation in the field. One of the primary challenges in computational biology is access to high-quality biological data. Collaboration between research organizations and technology firms can provide valuable data resources. Public-private partnerships, for example, can make large datasets accessible to researchers, enabling them to conduct comprehensive analyses and develop more accurate models. Collaborative efforts allow for the pooling of resources, both human and financial. This resource synergy can accelerate research and development processes. When multiple entities contribute to a project, it becomes possible to tackle more extensive and complex tasks, such as large-scale genomic studies or drug discovery initiatives. Computational biology inherently involves multiple disciplines, including biology, computer science, and statistics. Collaborative projects often involve researchers from these diverse backgrounds. This interdisciplinary approach encourages fresh perspectives and creative problem-solving, leading to breakthroughs that might not have been possible within a single organization. The pharmaceutical industry is increasingly turning to computational biology for drug discovery. Collaborations between pharmaceutical companies and computational biology experts can expedite the identification of potential drug candidates. Cross-industry partnerships facilitate the application of computational tools to predict drug-target interactions and optimize lead compounds.

Key Market Challenges

Data Complexity and Volume

The exponential growth of biological data is a double-edged sword. While it provides a wealth of information, it also presents a significant challenge in terms of data complexity and volume. Handling, storing, and analyzing massive datasets require robust computational infrastructure and efficient algorithms.

Data Privacy and Security

Biological data, especially genomic information, is sensitive and subject to strict privacy regulations. Ensuring data privacy while allowing for meaningful analysis is a delicate balance. The computational biology market must address these concerns to gain public trust and comply with evolving data protection laws.

Interoperability and Standardization

Computational biology tools and platforms often vary in their data formats and analysis methods. This lack of standardization hinders data sharing and collaboration. Establishing common data standards and interoperable tools is essential to overcome this challenge.

Shortage of Skilled Workforce

The field of computational biology requires a multidisciplinary skill set, encompassing biology, computer science, mathematics, and statistics. There is a shortage of professionals with expertise in these areas, making it challenging for organizations to find and retain qualified talent.

Key Market Trends

Single-Cell Omics Revolution

Single-cell sequencing and omics technologies are rapidly gaining momentum. These techniques allow researchers to dissect the molecular profiles of individual cells within complex tissues. As the resolution of single-cell data improves, computational biology will play a critical role in analyzing and interpreting these intricate datasets. Expect innovations in algorithms and tools tailored for single-cell omics analysis.

Spatial Transcriptomics

Spatial transcriptomics is an emerging field that combines genomics with spatial information. It enables researchers to map gene expression within tissues, providing insights into the spatial organization of cells. Computational methods for spatial data analysis will be in high demand, offering new ways to study tissue architecture and disease mechanisms.

Multi-Omics Integration

Integrating multiple omics data sources, such as genomics, transcriptomics, proteomics, and metabolomics, provides a holistic view of biological systems. Computational tools that facilitate the integration and analysis of multi-omics data will be in high demand, enabling researchers to uncover intricate interactions and pathways.

Blockchain for Data Security

Data security and privacy are paramount in computational biology, particularly when handling sensitive genomic information. Blockchain technology holds promise for secure and transparent data management, ensuring the integrity and privacy of biological data. Expect to see blockchain-based solutions for data security and traceability.

Segmental Insights

Service Insights

Based on the category of Service, the Contract segment emerged as the dominant player in the global market for computational biology in 2022. This can be attributed to the cost-effectiveness of contract services compared to the in-house services offered globally. Providers of Contract Research Organization (CRO) services collaborate closely with clients to create tailored plans, thereby acting as a catalyst for market growth.

On the other hand, the in-house segment is projected to experience the most rapid growth. In-house services grant companies' greater control over their internal operations, as they directly employ these services. This approach offers advantages such as cost savings and time efficiency, contributing to its accelerated growth.

End User Insights

The commercial sector is anticipated to be the primary contributor to market revenue. Increased investments in Research and Development (R&D) in genetic engineering and the development of innovative medicines by both government and commercial entities are significant factors contributing to the heightened demand for computational biology.

As an example, in May 2021, the World Health Organization (WHO) and the Swiss Confederation inked a Memorandum of Understanding (MoU) to establish the inaugural WHO BioHub Facility as part of the WHO BioHub System. Situated in Spiez, Switzerland, this facility serves as a hub for the secure reception, sequencing, storage, and preparation of biological materials for distribution to other laboratories. It also plays a crucial role in risk assessments and supports global preparedness against pathogens. Similarly, substantial investments from the European Commission into the Horizon 2020 program aim to eliminate innovation barriers and promote improved collaboration between the public and private sectors, fostering innovation. These developments are expected to bolster the rising demand for computational biology, consequently driving revenue growth in this market segment.

Regional Insights

North America presently holds the dominant position in the computational biology market and is expected to maintain its leadership for several more years. The United States, in particular, stands as the frontrunner in the field of synthetic biology, which is an emerging discipline focused on the design, manipulation, and reprogramming of biological systems. The U.S. government has been a substantial supporter of computational biology and synthetic biology since 2005, channeling over USD 1 billion toward their development. The annual average investment by the U.S. government in advancing computational biology is estimated at approximately USD 140 million.

The rise of personalized medicine has fostered collaborative initiatives among medical institutions, government bodies, and researchers to expedite the creation of effective treatments. For instance, in 2020, Summit Biolabs Inc. and the Colorado Center for Personalized Medicine (CCPM) established a comprehensive strategic partnership to conduct research, development, and commercialization of saliva liquid-biopsy tests for the early detection of cancer, diagnosis of COVID-19, and other viral infections. Similarly, in April 2020, HealthCare Global Enterprises and Strand Life Sciences introduced the StrandAdvantage500, a Next-Generation Sequencing (NGS) based assay that assesses cancer-related genetic alterations in DNA and RNA extracted from a patient's tumor in a unified workflow. Furthermore, in July 2021, Indivumed GmbH launched "travel," an innovative AI discovery platform designed for oncology and precision medicine. This platform combines IndivuType's extensive multi-omics data with sophisticated disease models, highly advanced automated Machine Learning tools, and a comprehensive suite of advanced analytical capabilities.

The overall computational biology market in the United States is poised for substantial growth in the coming years, primarily due to the significant investments made in drug development, which are the highest worldwide.

Key Market Players

  • Dassault Systemes SE
  • Certara Inc
  • Chemical Computing Group ULC
  • Compugen Ltd
  • Rosa & Co. LLC
  • GeneData AG
  • Insilico Biotechnology AG
  • Instem PLC
  • Strand Life Sciences Pvt Ltd
  • Schrodinger Inc

Report Scope:

In this report, the Global Computational Biology Market has been segmented into the following categories, in addition to the industry trends which have also been detailed below:

Computational Biology Market, By Application:

  • Cellular and Biological Simulation
  • Drug Discovery and Disease Modelling
  • Preclinical Drug Development
  • Clinical Trials
  • Human Body Simulation Software

Computational Biology Market, By Tool:

  • Databases
  • Infrastructure (Hardware)
  • Analysis Software and Services

Computational Biology Market, By Service:

  • In-house
  • Contact

Computational Biology Market, By End User:

  • Academics
  • Industry and Commercials

Computational Biology Market, By Region:

  • North America
  • United States
  • Canada
  • Mexico
  • Europe
  • Germany
  • United Kingdom
  • France
  • Italy
  • Spain
  • Asia-Pacific
  • China
  • Japan
  • India
  • Australia
  • South Korea
  • South America
  • Brazil
  • Argentina
  • Colombia
  • Middle East & Africa
  • South Africa
  • Saudi Arabia
  • UAE
  • Kuwait

Competitive Landscape

  • Company Profiles: Detailed analysis of the major companies present in the Global Computational Biology Market.

Available Customizations:

  • Global Computational Biology market report with the given market data, Tech Sci Research offers customizations according to a company's specific needs. The following customization options are available for the report:

Company Information

  • Detailed analysis and profiling of additional market players (up to five).

Table of Contents

1. Product Overview

  • 1.1. Market Definition
  • 1.2. Scope of the Market
    • 1.2.1. Markets Covered
    • 1.2.2. Years Considered for Study
    • 1.2.3. Key Market Segmentations

2. Research Methodology

  • 2.1. Objective of the Study
  • 2.2. Baseline Methodology
  • 2.3. Key Industry Partners
  • 2.4. Major Association and Secondary Sources
  • 2.5. Forecasting Methodology
  • 2.6. Data Triangulation & Validation
  • 2.7. Assumptions and Limitations

3. Executive Summary

  • 3.1. Overview of the Market
  • 3.2. Overview of Key Market Segmentations
  • 3.3. Overview of Key Market Players
  • 3.4. Overview of Key Regions/Countries
  • 3.5. Overview of Market Drivers, Challenges, Trends

4. Voice of Customer

5. Global Computational Biology Market Outlook

  • 5.1. Market Size & Forecast
    • 5.1.1. By Value
  • 5.2. Market Share & Forecast
    • 5.2.1. By Application (Cellular and Biological Simulation, Drug Discovery and Disease Modelling, Preclinical Drug Development, Clinical Trials, Human Body Simulation Software)
    • 5.2.2. By Tool (Databases, Infrastructure (Hardware), Analysis Software and Services)
    • 5.2.3. By Service (In-house, Contact)
    • 5.2.4. By End User (Academics, Industry and Commercials)
    • 5.2.5. By Region
    • 5.2.6. By Company (2022)
  • 5.3. Product Market Map
    • 5.3.1. By Application
    • 5.3.2. By Tool
    • 5.3.3. By Service
    • 5.3.4. By End User
    • 5.3.5. By Region

6. North America Computational Biology Market Outlook

  • 6.1. Market Size & Forecast
    • 6.1.1. By Value
  • 6.2. Market Share & Forecast
    • 6.2.1. By Application (Cellular and Biological Simulation, Drug Discovery and Disease Modelling, Preclinical Drug Development, Clinical Trials, Human Body Simulation Software)
    • 6.2.2. By Tool (Databases, Infrastructure (Hardware), Analysis Software and Services)
    • 6.2.3. By Service (In-house, Contact)
    • 6.2.4. By End User (Academics, Industry and Commercials)
    • 6.2.5. By Country
  • 6.3. North America: Country Analysis
    • 6.3.1. United States Computational Biology Market Outlook
      • 6.3.1.1. Market Size & Forecast
        • 6.3.1.1.1. By Value
      • 6.3.1.2. Market Share & Forecast
        • 6.3.1.2.1. By Application
        • 6.3.1.2.2. By Tool
        • 6.3.1.2.3. By Service
        • 6.3.1.2.4. By End User
    • 6.3.2. Canada Computational Biology Market Outlook
      • 6.3.2.1. Market Size & Forecast
        • 6.3.2.1.1. By Value
      • 6.3.2.2. Market Share & Forecast
        • 6.3.2.2.1. By Application
        • 6.3.2.2.2. By Tool
        • 6.3.2.2.3. By Service
        • 6.3.2.2.4. By End User
    • 6.3.3. Mexico Computational Biology Market Outlook
      • 6.3.3.1. Market Size & Forecast
        • 6.3.3.1.1. By Value
      • 6.3.3.2. Market Share & Forecast
        • 6.3.3.2.1. By Application
        • 6.3.3.2.2. By Tool
        • 6.3.3.2.3. By Service
        • 6.3.3.2.4. By End User

7. Europe Computational Biology Market Outlook

  • 7.1. Market Size & Forecast
    • 7.1.1. By Value
  • 7.2. Market Share & Forecast
    • 7.2.1. By Application (Cellular and Biological Simulation, Drug Discovery and Disease Modelling, Preclinical Drug Development, Clinical Trials, Human Body Simulation Software)
    • 7.2.2. By Tool (Databases, Infrastructure (Hardware), Analysis Software and Services)
    • 7.2.3. By Service (In-house, Contact)
    • 7.2.4. By End User (Academics, Industry and Commercials)
    • 7.2.5. By Country
  • 7.3. Europe: Country Analysis
    • 7.3.1. Germany Computational Biology Market Outlook
      • 7.3.1.1. Market Size & Forecast
        • 7.3.1.1.1. By Value
      • 7.3.1.2. Market Share & Forecast
        • 7.3.1.2.1. By Application
        • 7.3.1.2.2. By Tool
        • 7.3.1.2.3. By Service
        • 7.3.1.2.4. By End User
    • 7.3.2. United Kingdom Computational Biology Market Outlook
      • 7.3.2.1. Market Size & Forecast
        • 7.3.2.1.1. By Value
      • 7.3.2.2. Market Share & Forecast
        • 7.3.2.2.1. By Application
        • 7.3.2.2.2. By Tool
        • 7.3.2.2.3. By Service
        • 7.3.2.2.4. By End User
    • 7.3.3. France Computational Biology Market Outlook
      • 7.3.3.1. Market Size & Forecast
        • 7.3.3.1.1. By Value
      • 7.3.3.2. Market Share & Forecast
        • 7.3.3.2.1. By Application
        • 7.3.3.2.2. By Tool
        • 7.3.3.2.3. By Service
        • 7.3.3.2.4. By End User
    • 7.3.4. Italy Computational Biology Market Outlook
      • 7.3.4.1. Market Size & Forecast
        • 7.3.4.1.1. By Value
      • 7.3.4.2. Market Share & Forecast
        • 7.3.4.2.1. By Application
        • 7.3.4.2.2. By Tool
        • 7.3.4.2.3. By Service
        • 7.3.4.2.4. By End User
    • 7.3.5. Spain Computational Biology Market Outlook
      • 7.3.5.1. Market Size & Forecast
        • 7.3.5.1.1. By Value
      • 7.3.5.2. Market Share & Forecast
        • 7.3.5.2.1. By Application
        • 7.3.5.2.2. By Tool
        • 7.3.5.2.3. By Service
        • 7.3.5.2.4. By End User

8. Asia-Pacific Computational Biology Market Outlook

  • 8.1. Market Size & Forecast
    • 8.1.1. By Value
  • 8.2. Market Share & Forecast
    • 8.2.1. By Application (Cellular and Biological Simulation, Drug Discovery and Disease Modelling, Preclinical Drug Development, Clinical Trials, Human Body Simulation Software)
    • 8.2.2. By Tool (Databases, Infrastructure (Hardware), Analysis Software and Services)
    • 8.2.3. By Service (In-house, Contact)
    • 8.2.4. By End User (Academics, Industry and Commercials)
    • 8.2.5. By Country
  • 8.3. Asia-Pacific: Country Analysis
    • 8.3.1. China Computational Biology Market Outlook
      • 8.3.1.1. Market Size & Forecast
        • 8.3.1.1.1. By Value
      • 8.3.1.2. Market Share & Forecast
        • 8.3.1.2.1. By Application
        • 8.3.1.2.2. By Tool
        • 8.3.1.2.3. By Service
        • 8.3.1.2.4. By End User
    • 8.3.2. Japan Computational Biology Market Outlook
      • 8.3.2.1. Market Size & Forecast
        • 8.3.2.1.1. By Value
      • 8.3.2.2. Market Share & Forecast
        • 8.3.2.2.1. By Application
        • 8.3.2.2.2. By Tool
        • 8.3.2.2.3. By Service
        • 8.3.2.2.4. By End User
    • 8.3.3. India Computational Biology Market Outlook
      • 8.3.3.1. Market Size & Forecast
        • 8.3.3.1.1. By Value
      • 8.3.3.2. Market Share & Forecast
        • 8.3.3.2.1. By Application
        • 8.3.3.2.2. By Tool
        • 8.3.3.2.3. By Service
        • 8.3.3.2.4. By End User
    • 8.3.4. Australia Computational Biology Market Outlook
      • 8.3.4.1. Market Size & Forecast
        • 8.3.4.1.1. By Value
      • 8.3.4.2. Market Share & Forecast
        • 8.3.4.2.1. By Application
        • 8.3.4.2.2. By Tool
        • 8.3.4.2.3. By Service
        • 8.3.4.2.4. By End User
    • 8.3.5. South Korea Computational Biology Market Outlook
      • 8.3.5.1. Market Size & Forecast
        • 8.3.5.1.1. By Value
      • 8.3.5.2. Market Share & Forecast
        • 8.3.5.2.1. By Application
        • 8.3.5.2.2. By Tool
        • 8.3.5.2.3. By Service
        • 8.3.5.2.4. By End User

9. South America Computational Biology Market Outlook

  • 9.1. Market Size & Forecast
    • 9.1.1. By Value
  • 9.2. Market Share & Forecast
    • 9.2.1. By Application (Cellular and Biological Simulation, Drug Discovery and Disease Modelling, Preclinical Drug Development, Clinical Trials, Human Body Simulation Software)
    • 9.2.2. By Tool (Databases, Infrastructure (Hardware), Analysis Software and Services)
    • 9.2.3. By Service (In-house, Contact)
    • 9.2.4. By End User (Academics, Industry and Commercials)
    • 9.2.5. By Country
  • 9.3. South America: Country Analysis
    • 9.3.1. Brazil Computational Biology Market Outlook
      • 9.3.1.1. Market Size & Forecast
        • 9.3.1.1.1. By Value
      • 9.3.1.2. Market Share & Forecast
        • 9.3.1.2.1. By Application
        • 9.3.1.2.2. By Tool
        • 9.3.1.2.3. By Service
        • 9.3.1.2.4. By End User
    • 9.3.2. Argentina Computational Biology Market Outlook
      • 9.3.2.1. Market Size & Forecast
        • 9.3.2.1.1. By Value
      • 9.3.2.2. Market Share & Forecast
        • 9.3.2.2.1. By Application
        • 9.3.2.2.2. By Tool
        • 9.3.2.2.3. By Service
        • 9.3.2.2.4. By End User
    • 9.3.3. Colombia Computational Biology Market Outlook
      • 9.3.3.1. Market Size & Forecast
        • 9.3.3.1.1. By Value
      • 9.3.3.2. Market Share & Forecast
        • 9.3.3.2.1. By Application
        • 9.3.3.2.2. By Tool
        • 9.3.3.2.3. By Service
        • 9.3.3.2.4. By End User

10. Middle East and Africa Computational Biology Market Outlook

  • 10.1. Market Size & Forecast
    • 10.1.1. By Value
  • 10.2. Market Share & Forecast
    • 10.2.1. By Application (Cellular and Biological Simulation, Drug Discovery and Disease Modelling, Preclinical Drug Development, Clinical Trials, Human Body Simulation Software)
    • 10.2.2. By Tool (Databases, Infrastructure (Hardware), Analysis Software and Services)
    • 10.2.3. By Service (In-house, Contact)
    • 10.2.4. By End User (Academics, Industry and Commercials)
    • 10.2.5. By Country
  • 10.3. MEA: Country Analysis
    • 10.3.1. South Africa Computational Biology Market Outlook
      • 10.3.1.1. Market Size & Forecast
        • 10.3.1.1.1. By Value
      • 10.3.1.2. Market Share & Forecast
        • 10.3.1.2.1. By Application
        • 10.3.1.2.2. By Tool
        • 10.3.1.2.3. By Service
        • 10.3.1.2.4. By End User
    • 10.3.2. Saudi Arabia Computational Biology Market Outlook
      • 10.3.2.1. Market Size & Forecast
        • 10.3.2.1.1. By Value
      • 10.3.2.2. Market Share & Forecast
        • 10.3.2.2.1. By Application
        • 10.3.2.2.2. By Tool
        • 10.3.2.2.3. By Service
        • 10.3.2.2.4. By End User
    • 10.3.3. UAE Computational Biology Market Outlook
      • 10.3.3.1. Market Size & Forecast
        • 10.3.3.1.1. By Value
      • 10.3.3.2. Market Share & Forecast
        • 10.3.3.2.1. By Application
        • 10.3.3.2.2. By Tool
        • 10.3.3.2.3. By Service
        • 10.3.3.2.4. By End User
    • 10.3.4. Kuwait Computational Biology Market Outlook
      • 10.3.4.1. Market Size & Forecast
        • 10.3.4.1.1. By Value
      • 10.3.4.2. Market Share & Forecast
        • 10.3.4.2.1. By Application
        • 10.3.4.2.2. By Tool
        • 10.3.4.2.3. By Service
        • 10.3.4.2.4. By End User

11. Market Dynamics

  • 11.1. Drivers
  • 11.2. Challenges

12. Market Trends & Developments

  • 12.1. Recent Development
  • 12.2. Mergers & Acquisitions
  • 12.3. Product Launches

13. Porter's Five Forces Analysis

  • 13.1. Competition in the Industry
  • 13.2. Potential of New Entrants
  • 13.3. Power of Suppliers
  • 13.4. Power of Customers
  • 13.5. Threat of Substitute Products

14. Competitive Landscape

  • 14.1. Business Overview
  • 14.2. Product Offerings
  • 14.3. Recent Developments
  • 14.4. Financials (As Reported)
  • 14.5. Key Personnel
  • 14.6. SWOT Analysis
    • 14.6.1. Dassault Systemes SE
    • 14.6.2. Certara Inc
    • 14.6.3. Chemical Computing Group ULC
    • 14.6.4. Compugen Ltd
    • 14.6.5. Rosa & Co. LLC
    • 14.6.6. GeneData AG
    • 14.6.7. Insilico Biotechnology AG
    • 14.6.8. Instem PLC
    • 14.6.9. Strand Life Sciences Pvt Ltd
    • 14.6.10. Schrodinger Inc

15. Strategic Recommendations

16. About Us & Disclaimer