Exscientia AI-driven

by Exscientia  · Based in United Kingdom → — Pioneering AI-driven solutions in drug discovery
Oncology pharmacology rare-diseases
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Varies by collaboration and milestones
Documentation ProvidedRegulatory Status Disclosed

Overview

Recursion, which has integrated Exscientia’s platform, is at the forefront of AI-driven drug discovery, leveraging a unique blend of automated chemistry, deep learning models, and robotics. The company’s mission is to decode biology and accelerate the development of new medicines, addressing the high failure rates and inefficiencies of traditional drug discovery methods.

The Recursion OS drug discovery and development platform is built on a foundation of vast proprietary biological and chemical datasets, exceeding 50 petabytes. This data spans phenomics, transcriptomics, proteomics, ADME, and de-identified patient data, collected through an automated wet lab utilizing robotics and computer vision to conduct millions of cell experiments weekly. This continuous feedback loop allows for rapid iteration and optimization in the drug discovery process.

For physicians, this platform translates into the potential for faster development of novel, high-quality drug candidates, particularly for conditions with high unmet needs such as aggressive cancers and rare diseases. The platform has demonstrated significant improvements in speed, efficiency, and reduced costs from hit identification to IND-enabling studies compared to traditional pharmaceutical averages, aiming to bring better medicines to patients more quickly.

Recursion’s approach involves training intelligent machine learning models on their extensive datasets to rapidly identify new targets and design highly optimized molecules. This capability is further enhanced by strategic partnerships with industry leaders and computational titans, including NVIDIA, with whom they built BioHive-2, one of biopharma’s most powerful supercomputers.

Reviewed by Pouyan Golshani, MD — Interventional Radiologist

Key Features

  • AI-driven drug design
  • Automated chemistry and robotics
  • Deep learning models for target identification
  • Proprietary biological and chemical datasets (>50 petabytes)
  • High-throughput screening and experimentation
  • Integrated drug discovery and development platform (Recursion OS)
  • Supercomputing capabilities (BioHive-2 with NVIDIA)
  • Pipeline of potential first-in-class and best-in-class treatments
  • Strategic partnerships for accelerated discovery

Use Cases

  • Accelerated drug candidate identification
  • Optimization of molecular design
  • Discovery of treatments for oncology
  • Development of therapies for rare diseases
  • Reducing drug discovery timelines and costs
  • Decoding complex biological pathways

What Physicians Need to Know

Molecule Screening Capability
Exscientia utilizes generative AI algorithms to design and optimize drug candidates in silico, significantly reducing the number of costly experiments and accelerating the design process by up to 70%. Their automated robotic labs synthesize and test these AI-designed candidates, with data fed back to improve the generative AI algorithms, creating a continuous Design-Make-Test-Learn (DMTL) cycle. This approach allows for the rapid testing and understanding of complex targets and mechanisms, focusing on flexibility rather than traditional high-throughput screening.
Clinical Trial Matching
Exscientia's platform has been used to identify the right drug combinations to trial with patients. They have also developed a precision oncology platform that leverages deep learning and high-content imaging of primary human cancer tissues to guide treatment selection and improve patient outcomes in clinical studies. This includes studies like EXALT-1, which demonstrated improved outcomes for late-stage hematological cancer patients by guiding personalized treatment recommendations.
Real-World Evidence Analysis
Exscientia's algorithms are trained on publicly available pharmacology data and proprietary in-house data, which includes patient tissue samples, genomics, single-cell transcriptomics, and medical literature. Their personalized medicine platform integrates primary human tissue samples into early target and drug discovery research, allowing for the incorporation of patient, disease, and clinically relevant data into decisions on potential new medicine candidates earlier in the process.
Genomic Data Integration
Exscientia's algorithms are trained on genomic data, including single-cell transcriptomics. They integrate machine learning, data from primary human tumor samples, and multi-omics sequencing capabilities to predict the tumor efficacy of drug candidates. This integration is crucial for precision medicine, allowing for the tailoring of treatments to an individual's DNA and improving AI models for drug discovery.
Literature Mining
Exscientia's algorithms are trained on medical literature. They are also involved in ongoing PhD research programs that include next-generation text mining in drug discovery. This capability helps in rapidly analyzing large volumes of data from scientific research and clinical studies to extract hidden information and identify disease-causing genes and their roles in personalized medicine.
Target Identification
Exscientia's AI-driven platform is used for target identification, applying deep learning to genome-scale datasets to increase the probability of clinical translation. They define precise target product profiles (TPPs) based on patient needs and use AI engineers to design algorithms that generate potential drug candidates to meet these TPPs. Exscientia actively identifies novel compounds internally and for partners.
Safety Signal Detection
Exscientia aims to iron out chemical liabilities to make safer and more effective drug candidates before testing them in patients. While the provided information doesn't detail specific safety signal detection *features* within the platform, the overall goal of their AI-driven design process is to develop safer drugs, which inherently involves minimizing potential adverse effects.
Collaboration Features
Exscientia actively collaborates with pharmaceutical companies and academic institutions, including Sanofi, Bristol Myers Squibb, Merck KGaA, and MD Anderson Cancer Center. These collaborations involve joint efforts in target identification, small molecule drug design, lead optimization, and clinical development, leveraging Exscientia's AI platform with partner expertise and data.
Publication Support
Exscientia's research and findings are published in peer-reviewed journals, such as Cancer Discovery and Blood Cancer Discovery. They also support the next generation of researchers through collaborative training partnerships and PhD studentships in AI drug discovery, which includes research in areas like next-generation text mining.
Physician Tip

Exscientia's AI-driven platform aims to accelerate drug discovery and develop more effective and safer medicines. Physicians should be aware that this technology is designed to create highly optimized drug candidates, potentially leading to novel therapies with improved patient outcomes and reduced side effects. The integration of patient tissue samples and genomic data in early discovery phases suggests a move towards more personalized medicine approaches. While the platform focuses on early-stage drug development, the goal is to bring better-targeted medicines to patients faster.

Exscientia's platform is built on Amazon Web Services (AWS), leveraging generative AI models, active learning, machine learning, and physics-based systems. It integrates with automated robotic lab equipment for synthesis and testing, creating a closed-loop Design-Make-Test-Learn (DMTL) cycle. The platform also utilizes large language models (LLMs) through Amazon Bedrock. Exscientia's approach involves integrating diverse data sources, including publicly available pharmacology data, proprietary in-house data from patient tissue samples, genomics, single-cell transcriptomics, and medical literature.

Details

Category Drug Discovery & Research, Oncology AI
Pricing Varies by collaboration and milestones
  • Exscientia utilizes various pricing models, including milestone-based payments, where compensation increases based on the speed of discovery and the quality of identified candidates
  • They also engage in strategic partnerships that involve upfront investments and potential milestone payments, and royalties on net sales
  • For example, a collaboration with Sanofi involved a $100M upfront investment and up to $5.2B in milestones for co-developing ~15 drug candidates
  • Another deal with Merck involved $674 million to discover novel small molecule drug candidates
DeploymentCloud-based (AWS) and on-premise robotic labs
Mobile AppNone
Target SizeLarge pharmaceutical companies, biotech companies, and research institutions
Compliance
BAA AvailableUnknown AI-estimated
HIPAA CompliantUnknown AI-estimated
FDA Status Not applicable AI-estimated

Exscientia's AI-designed drug candidates have entered clinical trials, which are a requirement for US Food and Drug Administration approval. Their first AI-designed molecule entered clinical trials in early 2020. As of February 2023, the company had helped develop three drug candidates in clinical trials. As of May 2026, six AI-designed molecules from Exscientia have entered clinical trials.
Integrations
EHR Not specified
Specialties Oncology, Pharmacology, Rare Diseases

Social Proof

Customersunknown
Notable
BayerTakedaNVIDIA

What the Web Says

Exscientia's AI-driven platform is designed to revolutionize drug discovery by accelerating the identification and development of new therapies, aiming to make the process faster and more cost-effective than traditional methods. The company leverages AI and machine learning throughout the entire drug design and development cycle, from target identification to clinical trials. Exscientia has also formed strategic partnerships with major pharmaceutical companies and research institutions to advance its oncology and immunology programs.

Overall: Mixed

Strengths

  • Accelerates drug discovery timelines significantly, potentially reducing years to months.
  • Increases productivity and efficiency in delivering drug candidates.
  • Reduces costs associated with traditional drug discovery methods.
  • Enables the design of patient-centric drug candidates with an improved probability of success.
  • Has successfully delivered AI-designed drugs into clinical trials.
  • Utilizes a data-agnostic approach, incorporating various experimental information to build robust machine learning models.

Limitations

  • AI models can depend on incomplete or biased datasets, potentially limiting reliability, especially for rare diseases or unpublished failed experiments.
  • The technology is not a panacea, and traditional lab experiments and human trials cannot be entirely eliminated.
  • Some Reddit discussions express skepticism about AI biotech companies, questioning their long-term success and financial viability.
  • One Reddit user noted that AI is sometimes used as an excuse for failing development programs.
  • There are concerns about job stability within AI biotech companies, with reports of layoffs.
  • One trial for a solid tumor treatment was stopped to refocus the internal pipeline.

Based on reviews from: The Silicon Review, Axial, Reddit, Fierce Biotech, Darden Report, Seeking Alpha, AWS, Shimadzu UK, Pharmaceutical Technology, YouTube, MD Anderson Cancer Center, Cancer Discovery, Endpoints News, G2, Capterra

Last updated: 2026-10-04

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Press & Coverage

Amazon News
Exscientia Launches AWS AI-powered Platform to Advance Drug Discovery
Exscientia announced an expanded collaboration with Amazon Web Services (AWS) to power its end-to-end drug discovery and automation platform using AI and machine learning services. This aims to accelerate early drug development and reduce costs by securely and efficiently designing drug candidates.
2024-07
unknown
Recursion-Exscientia merger consolidates AI in drug discovery field
Recursion Pharmaceuticals and Exscientia are merging, a deal expected to be official in early 2025, to create a leading AI-driven drug discovery company with a robust clinical pipeline. The merger aims to advance the industrialization of drug discovery and is expected to result in 10 clinical readouts over the next 18 months.
2024-08
Recursion Pharmaceuticals, Inc.
Recursion and Exscientia, two leaders in the AI drug discovery space, have officially combined to advance the industrialization of drug discovery
The business combination of Recursion and Exscientia was completed on November 20, 2024, with Exscientia becoming a wholly owned subsidiary of Recursion, creating a vertically-integrated and technology-enabled drug discovery platform. This merger aims to combine their expertise to accelerate drug discovery and development.
2024-11
unknown
Exscientia Q2 and H1 2024 Business Update
Exscientia's Q2 and H1 2024 update highlighted advancements in its AI-driven pipeline, including acquiring full rights to its CDK7 inhibitor, GTAEXS617, currently in Phase 1/2 trials. The company is also progressing with other drug candidates and expanding its use of AWS for its drug discovery and automation platform.
2024-08
Endpoints News
Exscientia ends cancer study as it focuses on other trials of AI-driven drug candidates
Exscientia is discontinuing a Phase Ib/IIa trial for its oral capsule EXS21546 in solid tumors to refocus its internal pipeline. The company plans to prioritize other R&D work, including a Phase I/II study of the CDK7 asset GTAEXS617 and the preclinical LSD1 inhibitor EXS74539.
2023-10
Recursion Pharmaceuticals, Inc.
Exscientia Announces AI Drug Discovery Collaboration with Merck KGaA, Darmstadt, Germany
Exscientia announced a new multi-year collaboration with Merck KGaA, Darmstadt, Germany, to discover novel small molecule drug candidates across oncology, neuroinflammation, and immunology. Exscientia is eligible to receive up to $674 million in milestones plus tiered royalties for three initial projects.
2023-09
BioSpace
Exscientia Announces Sixth Molecule Created Through Generative AI Platform to Enter Clinical Stage
Exscientia announced that DSP-2342, a molecule created through its AI-driven drug discovery platform in collaboration with Sumitomo Pharma, will enter Phase 1 clinical trials in the United States. This marks the sixth AI-designed molecule from Exscientia to reach the clinical stage.
2023-05
Shimadzu UK
Exscientia: Accelerating towards the future of drug discovery
Exscientia is accelerating drug discovery by integrating AI and machine learning, enabling faster generation of new molecules and delivering compounds into clinical development. The company has also established a state-of-the-art automation facility to further automate the discovery process.
unknown

Videos

Product demos, reviews, and walkthroughs for Exscientia AI-driven.

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Frequently Asked Questions

Exscientia's AI platform leverages machine learning to analyze vast datasets of chemical compounds and biological targets, predicting optimal molecular designs and synthesizing them more efficiently. This iterative AI-driven design-make-test cycle significantly reduces the time and resources typically required for lead identification and optimization, potentially bringing new drug candidates to clinical trials faster.
Exscientia's AI primarily utilizes large datasets of chemical structures, biological assay results, and publicly available scientific literature. While their core drug discovery process doesn't directly involve patient-derived data, any collaborations that might touch upon such data would adhere to strict regulatory guidelines like HIPAA and GDPR, ensuring data anonymization and ethical use.
Yes, several other companies are developing AI platforms for drug discovery, including BenevolentAI, Recursion Pharmaceuticals, and Insilico Medicine. Exscientia differentiates itself through its full-stack capabilities, encompassing target identification, de novo molecular design, and preclinical development, often leading to wholly-owned drug candidates rather than solely providing AI tools.
While the upfront investment in AI platforms can be substantial, the long-term cost-effectiveness stems from significantly reduced timelines and higher success rates in identifying viable drug candidates, minimizing costly late-stage failures. Exscientia often operates on a partnership model, sharing risks and rewards, which can make AI-driven discovery more accessible to various institutions.
While powerful, Exscientia's AI, like all AI, is limited by the quality and quantity of its training data. Complex biological systems with limited existing data, or 'undruggable' targets with poorly understood mechanisms, may still pose significant challenges. Additionally, the AI's predictions still require experimental validation, and unforeseen biological complexities can emerge during later development stages.
Exscientia emphasizes the use of diverse and high-quality datasets to train its AI models, actively working to mitigate potential biases. They also employ rigorous validation processes and collaborate with human experts throughout the drug discovery pipeline to ensure that the AI's outputs are scientifically sound and ethically responsible, aiming for broad applicability and safety across patient populations.

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Suggest an Edit → | Last Verified: 2026-07-04 | First Added: 2026-07-04

Investors who backed Exscientia AI-driven

Funded through the company that built this tool.

Pivotal Life Sciences
VC T3
San Francisco, United States
$5M-$25M
Pivotal Life Sciences (formerly Pivotal bioVenture Partners or related entities) is a healthcare-specialist investment firm focused on life sciences, with deep exposure…
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