BANGALORE, India , Feb. 17, 2026 /PRNewswire/ — According to Valuates Reports, The global market for AI in Biotechnology was valued at USD 1033 Million in the year 2024 and is projected to reach a revised size of USD 1971 Million by 2031, growing at a CAGR of 10.6% during the forecast period.
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What are the key factors driving the growth of AI in the Biotechnology Market?
● AI in biotechnology is being shaped by platform-driven drug discovery models, multimodal data integration, and clinical-stage validation strategies.
● Market activity shows strong deployment of Machine Learning and Deep Learning for molecular generation, predictive biology, and translational modeling, while Natural Language Processing is increasingly used to extract insights from scientific literature, clinical documentation, and EHR records.
● AI is embedded across Drug Discovery & Development, Genomics & Precision Medicine, and Medical Imaging & Diagnostics workflows to reduce attrition, refine patient stratification, and accelerate regulatory readiness.
● Competitive intensity is centered on scalable AI platforms capable of integrating chemistry, biology, imaging, and clinical datasets into unified decision systems.
● The shift toward computational-first pipelines and real-world data integration is reinforcing AI’s operational centrality, driving the growth of the AI in Biotechnology market.
Source from Valuates Reports: https://reports.valuates.com/market-reports/QYRE-Auto-21X19811/global-ai-in-biotechnology
TRENDS INFLUENCING THE GROWTH OF THE AI IN BIOTECHNOLOGY MARKET:
Recent market momentum is concentrated around generative AI systems capable of designing novel compounds with optimized efficacy and safety profiles before laboratory synthesis. Deep learning architectures are being applied to molecular structure generation, binding affinity prediction, and toxicity modeling. Physics-informed simulation combined with ML is enabling more accurate interaction modeling between drug candidates and biological targets. These approaches are integrated directly into drug discovery pipelines, reducing reliance on traditional iterative screening. Predictive analytics are also being used to forecast translational success from preclinical to clinical phases. The commercialization of AI-designed therapeutic candidates demonstrates increasing industry confidence in computational discovery platforms, which in turn is driving the AI in Biotechnology Market growth.
A major trend involves combining genomic, transcriptomic, proteomic, imaging, and clinical datasets into unified AI models for oncology and rare disease programs. Machine Learning algorithms stratify patients into molecular subgroups, while deep neural networks analyze histopathology images alongside genomic markers. This integrated approach improves biomarker identification and treatment response prediction. Federated learning frameworks enable collaborative model training without direct data sharing, strengthening large-scale precision medicine initiatives. AI systems are increasingly embedded in immunotherapy development and targeted therapy selection workflows. The ability to align multimodal biological data with therapeutic decision-making is expanding AI deployment in precision medicine environments, which in turn is driving the AI in Biotechnology Market growth.
Market dynamics show a shift from experimental AI modeling toward clinical-stage validation of AI-designed candidates. Predictive models are being used to refine patient eligibility criteria, optimize cohort selection, and model treatment outcomes. AI-driven translational analytics connect phenotypic screening results with clinical biomarkers to enhance success probability. Digital pathology tools are supporting companion diagnostic development and trial endpoint analysis. This movement toward demonstrable clinical outcomes is strengthening investor and pharmaceutical partner confidence. The integration of AI into late-stage development workflows enhances regulatory readiness and commercialization pathways, which in turn is driving the AI in Biotechnology Market growth.
Natural Language Processing is increasingly used to build large-scale biomedical knowledge graphs linking genes, proteins, pathways, and therapeutic responses. NLP engines mine scientific publications, regulatory filings, and adverse event reports to support target identification and safety monitoring. Automated literature extraction accelerates hypothesis generation and reduces manual curation burdens. In genomics, NLP connects variant findings with documented clinical evidence to strengthen interpretation accuracy. Regulatory compliance workflows benefit from AI-assisted document review and pharmacovigilance monitoring. The growing need to transform unstructured biomedical information into structured research intelligence is expanding NLP integration across biotechnology operations, which in turn is driving the AI in Biotechnology Market growth.
A distinct trend involves merging physics-based molecular simulation with machine learning to enhance prediction reliability. AI-enhanced computational chemistry models simulate molecular interactions, stability, and solubility characteristics prior to synthesis. This convergence improves candidate prioritization and reduces laboratory validation cycles. Hybrid modeling frameworks are being used to address complex targets and previously undruggable pathways. These tools are integrated within early discovery platforms to refine structure–activity relationships and optimize compound libraries. The increased reliance on simulation-driven validation strengthens computational-first strategies in therapeutic development, which in turn is driving the AI in Biotechnology Market growth.
AI deployment in rare disease analytics and advanced genomic diagnostics is expanding through sequencing interpretation and biomarker detection systems. Machine Learning models identify pathogenic variants and correlate them with clinical phenotypes. Deep learning platforms analyze genomic patterns alongside patient data to support earlier diagnosis and personalized treatment pathways. Integration of genomic and imaging datasets enhances disease subtype classification. AI-based diagnostic tools are also used in translational research to identify novel therapeutic targets. As healthcare systems emphasize earlier intervention and precision targeting, AI-powered genomic diagnostics are becoming more embedded in biotechnology research programs, which in turn is driving the AI in Biotechnology Market growth.
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What are the major product types in the AI in Biotechnology market?
● Machine Learning (ML) & Deep Learning (DL)
● Natural Language Processing (NLP)
What are the main applications of AI in Biotechnology market?
● Drug Discovery & Development
● Genomics & Precision Medicine
● Medical Imaging & Diagnostics
Key Players in the AI in Biotechnology market:
● Artificial Intelligence
● Recursion Pharmaceuticals
● Exscientia Ltd
● XtalPi
● Schrödinger
● OWKIN, Inc.
● Evogene Ltd
● BioNTech
● MedySapiens
North America emphasizes AI-integrated drug discovery platforms, multimodal oncology programs, and clinical-stage validation pipelines supported by strong pharmaceutical partnerships. Asia-Pacific focuses on expanding computational chemistry capabilities, sequencing analytics, and AI-supported translational research backed by increasing biotechnology investment.
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What are some related markets to the AI in Biotechnology market?
– AI in Medical Writing Market
– AI In Predictive Toxicology Market
– Artificial Intelligence (AI) in Chemicals Market was valued at USD 2170 Million in the year 2024 and is projected to reach a revised size of USD 8942 Million by 2031, growing at a CAGR of 22.8% during the forecast period.
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– The global market for AI In Clinical Trials was valued at USD 52.3 Million in the year 2024 and is projected to reach a revised size of USD 82.4 Million by 2031, growing at a CAGR of 6.8% during the forecast period.
– The global market for Artificial Intelligence (AI) in Drug Discovery was valued at USD 1143 Million in the year 2024 and is projected to reach a revised size of USD 4642 Million by 2031, growing at a CAGR of 22.5% during the forecast period.
– AI in Hospital Management Market
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