Drug Discovery Informatics Market by Component (Services, Software), Therapeutic Area (Cardiovascular Diseases, Infectious Diseases, Metabolic Disorders), Application, End User, Deployment - Global Forecast 2025-2032
Description
The Drug Discovery Informatics Market was valued at USD 3.19 billion in 2024 and is projected to grow to USD 3.51 billion in 2025, with a CAGR of 10.20%, reaching USD 6.95 billion by 2032.
A concise orientation to how integrated computational platforms and advisory services have repositioned discovery informatics as a strategic capability across research organizations
The drug discovery informatics landscape has evolved from niche computational support into a central strategic capability embedded across discovery workflows, enabling faster hypothesis testing and more reproducible decision making. Early innovations in sequence analysis and molecular modeling matured into integrated informatics platforms that now span bioinformatics, cheminformatics, data integration, and machine learning. These capabilities are increasingly used not only to accelerate target identification and lead discovery but also to improve translational relevance by bridging preclinical and clinical data.
Industry actors are navigating a more complex technology stack characterized by cloud-native deployments, hybrid architectures, and modular software ecosystems that allow teams to compose solutions from best-of-breed components. Concurrently, services organizations are expanding advisory, systems integration, and managed operations to help end users extract value from these tools. As an introductory framing, the interplay between software innovation and service delivery is shaping how organizations define competitive advantage and allocate R&D resources. The remainder of this summary examines the forces driving this transition, the implications of recent trade and policy developments, and pragmatic segmentation, regional, and company-level considerations that leaders should incorporate into near-term strategies.
How the convergence of machine learning, interoperable modular platforms, and hybrid compute models is redefining discovery workflows and decision timing
Transformative shifts in discovery informatics are being driven by several interlocking advances that collectively change how research programs are organized and executed. First, the infusion of machine learning and generative modeling into both bioinformatics and cheminformatics has created qualitatively different pathways for hypothesis generation, enabling de novo design, property optimization, and prioritization with higher confidence. Second, the adoption of modular, API-driven architectures and interoperable data standards is reducing friction between siloed applications, allowing pipelines to be assembled and iterated more rapidly. Third, the rise of cloud-native platforms and hybrid operational models is enabling elastic compute and collaborative workflows across geographically dispersed teams, which in turn supports larger ensemble simulations and federated learning approaches.
These shifts are accompanied by operational changes: organizations are investing in data governance, reproducibility tooling, and cross-functional talent that blends domain expertise with data science. As a result, decision making is shifting upstream, with computational teams influencing experimental design and go/no-go determinations earlier in the pipeline. The combined effect is a faster feedback loop between in silico prediction and wet-lab validation, increasing the pace of learning while also demanding more robust validation frameworks and risk management for algorithmic outputs.
How tariff-driven trade shifts in 2025 are reshaping procurement, infrastructure choices, and cross-border collaboration strategies for informatics and lab operations
The cumulative impact of evolving tariff policies and trade measures enacted in and around 2025 has introduced operational complexities that shape procurement strategies, supply chain resilience, and cross-border collaborations in discovery informatics. Hardware acquisition and maintenance, for example, have become subject to additional scrutiny as tariff differentials alter the relative cost of on-premise server deployments versus cloud consumption, prompting organizations to reassess long-term infrastructure commitments. In many cases, procurement teams are favoring cloud-based or hybrid solutions to mitigate near-term capital exposure and to preserve agility in the face of shifting import costs.
Beyond hardware, tariffs and related policy actions influence the movement of specialized reagents, laboratory instruments, and high-performance compute components, which affects timelines for validation studies and scaled experiments. These pressures are catalyzing greater emphasis on software portability, containerization, and vendor-agnostic pipelines that can be redeployed across regions to avoid single-point dependencies. Moreover, collaborative research agreements and cross-border data-sharing arrangements are being reconfigured to account for regulatory and cost implications, reinforcing the value of distributed teams and multi-supplier strategies that can maintain continuity of research activities despite trade volatility.
An integrated segmentation framework that maps components, applications, deployment modes, end users, and therapeutic focuses to reveal focused opportunity areas
Key segmentation insights reveal where value creation is concentrated and how organizations can align capabilities with scientific priorities. When examining component segmentation, software and services form complementary pillars: Services encompass consulting, systems integration, and support and maintenance that embed tools into operational workflows, while software splits into bioinformatics and cheminformatics domains. Within bioinformatics, functional specialization includes genomics informatics, proteomics informatics, and transcriptomics informatics, each supporting distinct discovery questions; cheminformatics divides into molecular docking, QSAR modeling, and virtual screening, which together enable ligand design and prioritization.
Application-oriented segmentation highlights functional use cases that drive adoption. ADMET prediction comprises metabolism prediction, pharmacokinetics prediction, and toxicity prediction that de-risk candidate selection. Lead discovery spans high-throughput screening informatics, hit-to-lead informatics, and virtual screening informatics focused on hit quality and progression. Molecular modeling and simulation include molecular dynamics, QSAR modeling, and structure-based design enabling mechanistic insight, while target identification draws on genomic analysis, proteomic analysis, and target validation informatics to convert biological signals into tractable programs. Deployment considerations distinguish cloud from on-premise environments, with cloud options further categorized into hybrid, private, and public models and on-premise implementations described by client server and enterprise server topologies. Finally, end-user segmentation separates academic research institutes, contract research organizations, and pharmaceutical and biotechnology companies, while therapeutic area focus ranges across cardiovascular, infectious, metabolic, neuroscience, and oncology domains. Together, these segmentation dimensions provide a multidimensional lens for product strategy, commercial targeting, and R&D alignment.
Regional dynamics and ecosystem differences across major geographies that shape adoption, compliance approaches, talent distribution, and partnership models
Regional dynamics continue to exert a strong influence on adoption patterns, talent availability, regulatory posture, and collaboration networks across the Americas, Europe, Middle East & Africa, and Asia-Pacific. In the Americas, vibrant biotech clusters and investor capital are paired with deep expertise in translational informatics, creating fertile ground for partnerships between computational platform providers and translational research organizations. Infrastructure maturity and strong cloud provider presence support rapid pilot-to-scale transitions, while competitive labor markets incentivize automation and reproducibility investments.
In Europe, Middle East & Africa, regulatory emphasis on data protection and harmonized clinical frameworks drives a conservative but steady adoption of federated and privacy-preserving approaches, and regional centers of excellence are leveraging public-private collaborations to advance omics-driven projects. Meanwhile, in Asia-Pacific, rapid commercialization cycles, large patient cohorts, and concentrated manufacturing capabilities are accelerating adoption of high-throughput informatics and AI-assisted design, with a growing emphasis on localized data strategies and region-specific validation. These regional profiles underline the importance of tailoring deployment, partnership, and compliance strategies to the local ecosystem to maximize operational resilience and scientific impact.
How vendors are combining platform extensibility, domain-tuned modeling, and services capability to create durable differentiation and accelerate customer adoption
Company-level behavior in the discovery informatics space is increasingly characterized by strategic combinations of product innovation, ecosystem partnerships, and services-led commercialization. Leading software providers are prioritizing extensible platforms, open APIs, and stronger integrations with laboratory automation and data lakes to reduce time to value for end users. At the same time, service firms are expanding capabilities in consulting, integration, and managed operations to help customers orchestrate complex toolchains and to ensure sustained performance through support and maintenance offerings.
Competitive differentiation is emerging through investments in domain-specialized AI models, validated reference workflows, and prebuilt connectors that reduce integration friction. Strategic partnerships between software firms and cloud providers or systems integrators are becoming more common as a way to offer turnkey solutions that combine compute scalability with domain expertise. As organizations look to operationalize models at scale, successful companies are those that can demonstrate reproducible results, robust validation frameworks, and clear pathways to embed informatics outputs into experimental decision making. This market behavior favors vendors who balance technical excellence with consultative services that accelerate customer adoption and operational maturity.
Practical strategic moves that align architectural flexibility, validation rigor, hybrid procurement, and talent development to strengthen competitive positioning
Industry leaders should prioritize a set of actionable moves that align technological capability with operational resilience and strategic focus. Invest in modular, interoperable architectures that allow rapid substitution of components and avoid vendor lock-in, which reduces exposure to supply chain and tariff-driven disruptions while enabling faster integration of emerging algorithms. Strengthen data governance, reproducibility controls, and model validation practices to create defensible decision-making processes that integrate computational predictions with experimental evidence. This includes formalizing validation datasets and cross-team protocols so that model outputs are consistently evaluated against wet-lab results.
Additionally, develop hybrid procurement strategies that blend cloud and on-premise resources to optimize cost, performance, and compliance needs; this approach supports both burst compute for simulation-heavy workloads and controlled environments for sensitive data. Expand partnerships with specialized service providers to accelerate deployment and to build internal capabilities through co-delivery and knowledge transfer. Finally, cultivate multidisciplinary talent by investing in training programs that bridge biology, cheminformatics, and data science, thereby embedding computational thinking across discovery teams and reducing time-to-insight.
A transparent and reproducible approach combining practitioner interviews, technical literature synthesis, and triangulation to validate strategic inferences
The research methodology underpinning this analysis integrates qualitative expert interviews, thematic synthesis of technological developments, and triangulation against documented policy and operational trends to ensure balanced conclusions. Primary inputs include conversations with practitioners across pharmaceutical, biotechnology, and contract research organizations to understand real-world workflows, procurement drivers, and pain points associated with adoption. Secondary inputs involve a structured review of technical literature, regulatory guidance, and published descriptions of platform architectures and deployment patterns to contextualize statements about interoperability and infrastructure.
Analysis was performed by mapping use cases to segmentation dimensions, assessing regional regulatory and ecosystem differences, and synthesizing company behaviors into observable strategies. Care was taken to verify claims about technology readiness and operational implications through multiple practitioner perspectives and by cross-referencing technical descriptions of methods such as molecular dynamics, QSAR, and generative modeling. The resulting approach emphasizes transparency in assumptions, reproducible logic in mapping segmentation to strategy, and pragmatic recommendations that are traceable to the underlying qualitative evidence.
A concise synthesis that emphasizes modular architecture, validation discipline, and hybrid operational strategies as pillars for translational impact in discovery informatics
In conclusion, discovery informatics has transitioned into a central enabler of discovery productivity and translational rigor, shaped by advances in AI, modular architectures, and cloud-enabled computing. These technical changes are accompanied by operational shifts that favor hybrid deployments, stronger validation frameworks, and service partnerships that bridge capability gaps. Together, these trends increase the demand for integrative solutions that reduce friction across bioinformatics and cheminformatics domains and that provide robust governance for algorithmic outputs.
Leaders who adopt a pragmatic, layered approach-prioritizing modular technology stacks, hybrid procurement, rigorous validation, and cross-disciplinary talent development-will be positioned to harness computational advances while managing regulatory and supply-chain complexities. The synthesis presented here highlights the levers that organizations can pull to accelerate translational impact and to make informed choices about platform investments, partnerships, and organizational design moving forward.
Note: PDF & Excel + Online Access - 1 Year
A concise orientation to how integrated computational platforms and advisory services have repositioned discovery informatics as a strategic capability across research organizations
The drug discovery informatics landscape has evolved from niche computational support into a central strategic capability embedded across discovery workflows, enabling faster hypothesis testing and more reproducible decision making. Early innovations in sequence analysis and molecular modeling matured into integrated informatics platforms that now span bioinformatics, cheminformatics, data integration, and machine learning. These capabilities are increasingly used not only to accelerate target identification and lead discovery but also to improve translational relevance by bridging preclinical and clinical data.
Industry actors are navigating a more complex technology stack characterized by cloud-native deployments, hybrid architectures, and modular software ecosystems that allow teams to compose solutions from best-of-breed components. Concurrently, services organizations are expanding advisory, systems integration, and managed operations to help end users extract value from these tools. As an introductory framing, the interplay between software innovation and service delivery is shaping how organizations define competitive advantage and allocate R&D resources. The remainder of this summary examines the forces driving this transition, the implications of recent trade and policy developments, and pragmatic segmentation, regional, and company-level considerations that leaders should incorporate into near-term strategies.
How the convergence of machine learning, interoperable modular platforms, and hybrid compute models is redefining discovery workflows and decision timing
Transformative shifts in discovery informatics are being driven by several interlocking advances that collectively change how research programs are organized and executed. First, the infusion of machine learning and generative modeling into both bioinformatics and cheminformatics has created qualitatively different pathways for hypothesis generation, enabling de novo design, property optimization, and prioritization with higher confidence. Second, the adoption of modular, API-driven architectures and interoperable data standards is reducing friction between siloed applications, allowing pipelines to be assembled and iterated more rapidly. Third, the rise of cloud-native platforms and hybrid operational models is enabling elastic compute and collaborative workflows across geographically dispersed teams, which in turn supports larger ensemble simulations and federated learning approaches.
These shifts are accompanied by operational changes: organizations are investing in data governance, reproducibility tooling, and cross-functional talent that blends domain expertise with data science. As a result, decision making is shifting upstream, with computational teams influencing experimental design and go/no-go determinations earlier in the pipeline. The combined effect is a faster feedback loop between in silico prediction and wet-lab validation, increasing the pace of learning while also demanding more robust validation frameworks and risk management for algorithmic outputs.
How tariff-driven trade shifts in 2025 are reshaping procurement, infrastructure choices, and cross-border collaboration strategies for informatics and lab operations
The cumulative impact of evolving tariff policies and trade measures enacted in and around 2025 has introduced operational complexities that shape procurement strategies, supply chain resilience, and cross-border collaborations in discovery informatics. Hardware acquisition and maintenance, for example, have become subject to additional scrutiny as tariff differentials alter the relative cost of on-premise server deployments versus cloud consumption, prompting organizations to reassess long-term infrastructure commitments. In many cases, procurement teams are favoring cloud-based or hybrid solutions to mitigate near-term capital exposure and to preserve agility in the face of shifting import costs.
Beyond hardware, tariffs and related policy actions influence the movement of specialized reagents, laboratory instruments, and high-performance compute components, which affects timelines for validation studies and scaled experiments. These pressures are catalyzing greater emphasis on software portability, containerization, and vendor-agnostic pipelines that can be redeployed across regions to avoid single-point dependencies. Moreover, collaborative research agreements and cross-border data-sharing arrangements are being reconfigured to account for regulatory and cost implications, reinforcing the value of distributed teams and multi-supplier strategies that can maintain continuity of research activities despite trade volatility.
An integrated segmentation framework that maps components, applications, deployment modes, end users, and therapeutic focuses to reveal focused opportunity areas
Key segmentation insights reveal where value creation is concentrated and how organizations can align capabilities with scientific priorities. When examining component segmentation, software and services form complementary pillars: Services encompass consulting, systems integration, and support and maintenance that embed tools into operational workflows, while software splits into bioinformatics and cheminformatics domains. Within bioinformatics, functional specialization includes genomics informatics, proteomics informatics, and transcriptomics informatics, each supporting distinct discovery questions; cheminformatics divides into molecular docking, QSAR modeling, and virtual screening, which together enable ligand design and prioritization.
Application-oriented segmentation highlights functional use cases that drive adoption. ADMET prediction comprises metabolism prediction, pharmacokinetics prediction, and toxicity prediction that de-risk candidate selection. Lead discovery spans high-throughput screening informatics, hit-to-lead informatics, and virtual screening informatics focused on hit quality and progression. Molecular modeling and simulation include molecular dynamics, QSAR modeling, and structure-based design enabling mechanistic insight, while target identification draws on genomic analysis, proteomic analysis, and target validation informatics to convert biological signals into tractable programs. Deployment considerations distinguish cloud from on-premise environments, with cloud options further categorized into hybrid, private, and public models and on-premise implementations described by client server and enterprise server topologies. Finally, end-user segmentation separates academic research institutes, contract research organizations, and pharmaceutical and biotechnology companies, while therapeutic area focus ranges across cardiovascular, infectious, metabolic, neuroscience, and oncology domains. Together, these segmentation dimensions provide a multidimensional lens for product strategy, commercial targeting, and R&D alignment.
Regional dynamics and ecosystem differences across major geographies that shape adoption, compliance approaches, talent distribution, and partnership models
Regional dynamics continue to exert a strong influence on adoption patterns, talent availability, regulatory posture, and collaboration networks across the Americas, Europe, Middle East & Africa, and Asia-Pacific. In the Americas, vibrant biotech clusters and investor capital are paired with deep expertise in translational informatics, creating fertile ground for partnerships between computational platform providers and translational research organizations. Infrastructure maturity and strong cloud provider presence support rapid pilot-to-scale transitions, while competitive labor markets incentivize automation and reproducibility investments.
In Europe, Middle East & Africa, regulatory emphasis on data protection and harmonized clinical frameworks drives a conservative but steady adoption of federated and privacy-preserving approaches, and regional centers of excellence are leveraging public-private collaborations to advance omics-driven projects. Meanwhile, in Asia-Pacific, rapid commercialization cycles, large patient cohorts, and concentrated manufacturing capabilities are accelerating adoption of high-throughput informatics and AI-assisted design, with a growing emphasis on localized data strategies and region-specific validation. These regional profiles underline the importance of tailoring deployment, partnership, and compliance strategies to the local ecosystem to maximize operational resilience and scientific impact.
How vendors are combining platform extensibility, domain-tuned modeling, and services capability to create durable differentiation and accelerate customer adoption
Company-level behavior in the discovery informatics space is increasingly characterized by strategic combinations of product innovation, ecosystem partnerships, and services-led commercialization. Leading software providers are prioritizing extensible platforms, open APIs, and stronger integrations with laboratory automation and data lakes to reduce time to value for end users. At the same time, service firms are expanding capabilities in consulting, integration, and managed operations to help customers orchestrate complex toolchains and to ensure sustained performance through support and maintenance offerings.
Competitive differentiation is emerging through investments in domain-specialized AI models, validated reference workflows, and prebuilt connectors that reduce integration friction. Strategic partnerships between software firms and cloud providers or systems integrators are becoming more common as a way to offer turnkey solutions that combine compute scalability with domain expertise. As organizations look to operationalize models at scale, successful companies are those that can demonstrate reproducible results, robust validation frameworks, and clear pathways to embed informatics outputs into experimental decision making. This market behavior favors vendors who balance technical excellence with consultative services that accelerate customer adoption and operational maturity.
Practical strategic moves that align architectural flexibility, validation rigor, hybrid procurement, and talent development to strengthen competitive positioning
Industry leaders should prioritize a set of actionable moves that align technological capability with operational resilience and strategic focus. Invest in modular, interoperable architectures that allow rapid substitution of components and avoid vendor lock-in, which reduces exposure to supply chain and tariff-driven disruptions while enabling faster integration of emerging algorithms. Strengthen data governance, reproducibility controls, and model validation practices to create defensible decision-making processes that integrate computational predictions with experimental evidence. This includes formalizing validation datasets and cross-team protocols so that model outputs are consistently evaluated against wet-lab results.
Additionally, develop hybrid procurement strategies that blend cloud and on-premise resources to optimize cost, performance, and compliance needs; this approach supports both burst compute for simulation-heavy workloads and controlled environments for sensitive data. Expand partnerships with specialized service providers to accelerate deployment and to build internal capabilities through co-delivery and knowledge transfer. Finally, cultivate multidisciplinary talent by investing in training programs that bridge biology, cheminformatics, and data science, thereby embedding computational thinking across discovery teams and reducing time-to-insight.
A transparent and reproducible approach combining practitioner interviews, technical literature synthesis, and triangulation to validate strategic inferences
The research methodology underpinning this analysis integrates qualitative expert interviews, thematic synthesis of technological developments, and triangulation against documented policy and operational trends to ensure balanced conclusions. Primary inputs include conversations with practitioners across pharmaceutical, biotechnology, and contract research organizations to understand real-world workflows, procurement drivers, and pain points associated with adoption. Secondary inputs involve a structured review of technical literature, regulatory guidance, and published descriptions of platform architectures and deployment patterns to contextualize statements about interoperability and infrastructure.
Analysis was performed by mapping use cases to segmentation dimensions, assessing regional regulatory and ecosystem differences, and synthesizing company behaviors into observable strategies. Care was taken to verify claims about technology readiness and operational implications through multiple practitioner perspectives and by cross-referencing technical descriptions of methods such as molecular dynamics, QSAR, and generative modeling. The resulting approach emphasizes transparency in assumptions, reproducible logic in mapping segmentation to strategy, and pragmatic recommendations that are traceable to the underlying qualitative evidence.
A concise synthesis that emphasizes modular architecture, validation discipline, and hybrid operational strategies as pillars for translational impact in discovery informatics
In conclusion, discovery informatics has transitioned into a central enabler of discovery productivity and translational rigor, shaped by advances in AI, modular architectures, and cloud-enabled computing. These technical changes are accompanied by operational shifts that favor hybrid deployments, stronger validation frameworks, and service partnerships that bridge capability gaps. Together, these trends increase the demand for integrative solutions that reduce friction across bioinformatics and cheminformatics domains and that provide robust governance for algorithmic outputs.
Leaders who adopt a pragmatic, layered approach-prioritizing modular technology stacks, hybrid procurement, rigorous validation, and cross-disciplinary talent development-will be positioned to harness computational advances while managing regulatory and supply-chain complexities. The synthesis presented here highlights the levers that organizations can pull to accelerate translational impact and to make informed choices about platform investments, partnerships, and organizational design moving forward.
Note: PDF & Excel + Online Access - 1 Year
Table of Contents
191 Pages
- 1. Preface
- 1.1. Objectives of the Study
- 1.2. Market Segmentation & Coverage
- 1.3. Years Considered for the Study
- 1.4. Currency
- 1.5. Language
- 1.6. Stakeholders
- 2. Research Methodology
- 3. Executive Summary
- 4. Market Overview
- 5. Market Insights
- 5.1. Integration of AI-driven phenotypic screening data into cloud-based informatics platforms to accelerate novel target identification
- 5.2. Adoption of federated learning frameworks for secure cross-institutional data sharing in preclinical drug discovery workflows
- 5.3. Implementation of quantum machine learning algorithms for high-throughput virtual screening of complex chemical libraries
- 5.4. Application of multi-omics data integration tools to inform predictive models for precision therapeutic development
- 5.5. Expansion of real-world evidence analytics in drug discovery pipelines using advanced natural language processing approaches
- 5.6. Deployment of blockchain-enabled provenance tracking systems for chemical compound validation and research reproducibility
- 5.7. Utilization of digital twin modeling to simulate patient-specific drug responses and optimize lead compound selection
- 6. Cumulative Impact of United States Tariffs 2025
- 7. Cumulative Impact of Artificial Intelligence 2025
- 8. Drug Discovery Informatics Market, by Component
- 8.1. Services
- 8.1.1. Consulting
- 8.1.2. Integration
- 8.1.3. Support And Maintenance
- 8.2. Software
- 8.2.1. Bioinformatics
- 8.2.1.1. Genomics Informatics
- 8.2.1.2. Proteomics Informatics
- 8.2.1.3. Transcriptomics Informatics
- 8.2.2. Cheminformatics
- 8.2.2.1. Molecular Docking
- 8.2.2.2. Qsar Modeling
- 8.2.2.3. Virtual Screening
- 9. Drug Discovery Informatics Market, by Therapeutic Area
- 9.1. Cardiovascular Diseases
- 9.2. Infectious Diseases
- 9.3. Metabolic Disorders
- 9.4. Neuroscience
- 9.5. Oncology
- 10. Drug Discovery Informatics Market, by Application
- 10.1. Admet Prediction
- 10.1.1. Metabolism Prediction
- 10.1.2. Pharmacokinetics Prediction
- 10.1.3. Toxicity Prediction
- 10.2. Lead Discovery
- 10.2.1. High Throughput Screening Informatics
- 10.2.2. Hit To Lead Informatics
- 10.2.3. Virtual Screening Informatics
- 10.3. Molecular Modeling Simulation
- 10.3.1. Molecular Dynamics
- 10.3.2. Qsar Modeling
- 10.3.3. Structure Based Design
- 10.4. Target Identification
- 10.4.1. Genomic Analysis
- 10.4.2. Proteomic Analysis
- 10.4.3. Target Validation Informatics
- 11. Drug Discovery Informatics Market, by End User
- 11.1. Academic Research Institutes
- 11.2. Contract Research Organizations
- 11.3. Pharmaceutical Biotechnology Companies
- 12. Drug Discovery Informatics Market, by Deployment
- 12.1. Cloud
- 12.2. On Premise
- 13. Drug Discovery Informatics Market, by Region
- 13.1. Americas
- 13.1.1. North America
- 13.1.2. Latin America
- 13.2. Europe, Middle East & Africa
- 13.2.1. Europe
- 13.2.2. Middle East
- 13.2.3. Africa
- 13.3. Asia-Pacific
- 14. Drug Discovery Informatics Market, by Group
- 14.1. ASEAN
- 14.2. GCC
- 14.3. European Union
- 14.4. BRICS
- 14.5. G7
- 14.6. NATO
- 15. Drug Discovery Informatics Market, by Country
- 15.1. United States
- 15.2. Canada
- 15.3. Mexico
- 15.4. Brazil
- 15.5. United Kingdom
- 15.6. Germany
- 15.7. France
- 15.8. Russia
- 15.9. Italy
- 15.10. Spain
- 15.11. China
- 15.12. India
- 15.13. Japan
- 15.14. Australia
- 15.15. South Korea
- 16. Competitive Landscape
- 16.1. Market Share Analysis, 2024
- 16.2. FPNV Positioning Matrix, 2024
- 16.3. Competitive Analysis
- 16.3.1. BioSolveIT GmbH
- 16.3.2. Certara, Inc.
- 16.3.3. Clarivate PLC
- 16.3.4. Dassault Systèmes SE
- 16.3.5. Dotmatics Ltd
- 16.3.6. Genedata AG
- 16.3.7. InSilico Medicine
- 16.3.8. International Business Machines Corporation
- 16.3.9. Jubilant Biosys Ltd.
- 16.3.10. Optibrium Ltd
- 16.3.11. Oracle Corporation
- 16.3.12. PerkinElmer Inc.
- 16.3.13. Schrödinger, Inc.
- 16.3.14. TIBCO Software Inc.
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