United States AI in Drug Discovery and Development Market 2031 | Growth Drivers, Key Players & Investment Opportunities

Market Size and Growth

The Global AI in drug discovery and development market reached US$ 6.24 billion in 2024 and is expected to reach US$ 34.05 billion by 2033, growing at a CAGR of 18.5% during the forecast period 2025-2033.

Key Development & Future Pipelines:

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➢In Dec 2025-FDA approves AIM-NASH, the first AI tool for liver-disease drug trials
The AI system interprets liver biopsies for MASH/MASLD

➢In Dec 2025-Excelsior Sciences secures $95M for AI-accelerated small-molecule R&D
Its automated "smart-bloccs chemistry" compresses synthesis timelines from months to weeks.

➢In Dec 2025-ChemLex raises $45M to build a fully autonomous AI 'self-driving lab'
The Singapore-based 24/7 lab integrates robotics + AI to accelerate chemistry and design workflows.

➢In Dec 2025-Multi-agent AI platforms (e.g., MADD) advance end-to-end discovery
These agentic systems now generate, score, optimize, and rescreen molecules in automated loops.

➢In Dec 2025-AI boosts predictive accuracy in toxicity & hit discovery models

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♦ Future Pipelines, Approvals & What's Coming Next (2025-2030)

➢AI-Designed Drugs Entering Phase II/III
Insilico's Rentosertib and other AI-generated molecules are progressing fast, with several late-stage trial entries expected by 2026-2028-paving the way for the first fully AI-designed drug approvals this decade.

➢Next-Gen AI Drug Pipelines Emerging
• CHARM Therapeutics-3D deep-learning (DragonFold) for oncology
• Enveda Biosciences-AI + natural-product discovery for inflammation & neuro.

➢AI-Engineered Proteins & Biologics
Platforms like Nabla Bio (Takeda partner) are designing next-gen biologics via AI ("JAM"). First-in-human studies expected in 1-2 years, expanding AI beyond small molecules.

➢Rise of Agentic & LLM-Driven Discovery
Systems like MADD, PharmAgents, DrugAgent are automating the entire pipeline:
Target→Molecule→Screening→Optimization
This will enable near-autonomous R&D for startups, CROs & academia by 2026-2029.

➢ Self-Driving AI Labs Scaling Globally
Companies such as ChemLex and Excelsior are deploying autonomous 24/7 robotic labs, shrinking discovery cycles from months to days.

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➢Regulatory Momentum
The FDA qualification of AIM-NASH reported today by Times of India marks the first AI tool approved for liver-disease clinical-trial workflows.
More approvals expected for:
• AI biomarkers
• AI imaging
• AI-driven patient-stratification
These will transform clinical trials by 2027-2030.

➢ AI Targeting Global-Health Diseases
New AI models now improve drug-response prediction for TB, malaria & genetic variations in emerging markets expanding AI's relevance in global health.

➢Toxicity, Hit Prediction & ADMET Models Getting More Accurate

Key Players:

=> Alphabet (Google DeepMind), Atomwise Inc., BenevolentAI, BioMap, BioSymetrics, DEEP Genomics, Euretos, Exscientia, IBM, and Iktos. Among others.

Key Segments:

By Technology

Machine Learning (ML) drives 35% share, enabling predictive modeling, target identification, and virtual screening with high efficiency. Natural Language Processing (NLP) holds 25%, supporting literature mining, clinical data analysis, and real-world evidence generation. Generative AI contributes 20%, increasingly applied for de novo molecule design, lead optimization, and protein structure prediction. Others account for 20%, including hybrid AI approaches and emerging computational platforms.

By Application

Target Discovery & Validation leads with 18% share, as AI accelerates identification of novel therapeutic targets. Hit Discovery & Virtual Screening holds 15%, facilitating rapid in silico evaluation of compound libraries. Screening contributes 14%, improving throughput and accuracy in compound assessment. Hit-to-Lead accounts for 12%, while Lead Optimization holds 11%, leveraging AI to enhance potency and selectivity. Pre-Clinical Testing and Clinical Trials together form 20%, reflecting AI integration in study design, patient stratification, and safety prediction. Others represent 10%, including AI-assisted ADMET prediction and pharmacokinetic modeling.

By Region

North America - 42% Share
North America dominates due to high adoption of AI in pharma, extensive computational infrastructure, and robust R&D investments.

Europe - 25% Share
Europe accounts for 25%, driven by government-backed AI initiatives, strong pharma ecosystem, and early adoption of AI-driven clinical trials.

Asia-Pacific - 20% Share
Asia-Pacific contributes 20%, led by rapid growth in biotech startups, increasing digitalization in drug R&D, and government incentives in China, India, and Japan.

South America - 7% Share
South America represents 7%, with growth supported by emerging pharma companies and regional collaborations in AI-driven research.

Middle East & Africa - 6% Share
Middle East & Africa hold 6%, benefiting from investment in life sciences research infrastructure and AI initiatives in healthcare and pharma sectors.

FAQ

What is the current size of the AI in drug discovery and development Market?

A: In 2024, the AI in drug discovery and development Market was valued at US$ 6.24 billion, reflecting its strong industry presence.

Q2: How large is the AI in drug discovery and development Market expected to be by 2033?

A: By 2033, industry forecasts suggest the AI in drug discovery and development Market will grow to around US$ 34.05 billion, demonstrating significant expansion.

Q3: What is the growth rate of the AI in drug discovery and development Market?

A: The market is projected to expand at a compound annual growth rate (CAGR) of 18.5% during the forecast period from 2025 to 2033.

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