BenchSci Blog

150 Startups Using Artificial Intelligence in Drug Discovery

Written by Simon Smith | Nov 8, 2017 2:57:05 PM

Some time ago, I wrote about how we're now in the long-tail of machine learning in drug discovery. I noted that we're moving past generalist applications of AI such as IBM Watson's to more specific, purpose-built tools. This got me thinking: What are all the startups applying artificial intelligence in drug discovery currently?

Accordingly, I did some research and developed the following list, which I have grouped roughly by research phase. I update this list monthly. I'm sure that I've missed some, so please add any I've missed in the comments. Here's what I've uncovered so far:

  1. Aggregate and Synthesize Information
  2. Understand Mechanisms of Disease
  3. Establish Biomarkers
  4. Generate Data and Models
  5. Repurpose Existing Drugs
  6. Generate Novel Drug Candidates
  7. Validate and Optimize Drug Candidates
  8. Design Drugs
  9. Design Preclinical Experiments
  10. Run Preclinical Experiments
  11. Design Clinical Trials
  12. Recruit for Clinical Trials
  13. Optimize Clinical Trials
  14. Publish Data
  15. Analyze Real World Evidence

Aggregate and Synthesize Information


Uses AI to: Process raw phenotypic, imaging, drug, and genomic data sets. Allows researchers to: Integrate rapid analytics and machine learning capabilities into existing business processes to improve care, enhance discoveries, gain insight into business, and enable fast data-driven decisions.


Uses AI to: Create curated databases from the analysis of published scientific literature. Allows researchers to: Extract structured biological knowledge to power drug discovery applications.


Uses AI to: Read scientific articles and extract causal associations. Allows researchers to: Search for cause and effect relationships and gather evidence on how biomedical entities interact.


Uses AI to: Find hidden connections and new insights in diverse, linked datasets. Allows researchers to: Understand and treat disease by connecting data in new ways.


Uses AI to: Infer and organize knowledge from thousands of genomic, phenotypic, and clinical datasets. Allows researchers to: Identify new targets and biomarkers, repurpose drugs, and identify disease pathways amenable to combination therapy.


Uses AI to: Build a database of therapy evidence. Allows researchers to: Quickly answer any comparative cost and outcome question.


Uses AI to: Analyze multi-omics next-generation sequencing data for contextual, systems-level insights. Allows researchers to: Reveal previously unseen patterns across large, heterogeneous datasets to predict targets and biomarkers.


Uses AI to: Respond to verbal questions and requests in a lab setting. Allows researchers to: Increase efficiency, improve lab safety, keep current on relevant new research, and manage inventory.


Uses AI to: Generate insights from billions of disparate data points from thousands of data sources. Allows researchers to: Improve decision-making by seeing information in context from biomedical data sources including publications, clinical trials, congresses, and theses.


Uses AI to: Learn underlying correlations in fragmented datasets with incomplete information. Allows researchers to: Estimate missing knowledge of how candidate drugs act on proteins, to aid design of new drug cocktails that activate proteins to cure disease.


Uses AI to: Monitor millions of data points for signs of breakthrough events. Allows researchers to: Detect early signals of innovation.

Uses AI to: Establish and find the similarity of document "fingerprints" based on a combination of keyword extraction, word embeddings, neural topic modeling, and other natural language understanding techniques. Allows researchers to: Build reading lists faster, with more precision and interdisciplinary inspiration.


Uses AI to: Analyze large amounts of data fast and provide explainable, auditable results. Allows researchers to: Understand and extract meaning from internal data sets, especially unstructured ones.


Uses AI to: Understand voice-based commands and transcribe voice-based notes. Allows researchers to: Take notes and organize lab documentation faster and with less effort.


Uses AI to: Extract and analyze text. Allows researchers to: Answer a range of life science questions with natural language queries.


Uses AI to: Analyze and organize biomedical research. Allows researchers to: Receive a personalized feed of the most relevant and important research as it's published.


Uses AI to: Find patterns in biomedical data and infer hypotheses for investigation. Allows researchers to: Upload datasets and have them analyzed in the context of global biomedical knowledge, leading to new diagnostic and treatment strategies, particularly for personalized medicine.


Uses AI to: Extract knowledge in real-time from commercial, scientific, and regulatory literature. Allows researchers to: Identify competitive white space, eliminate blind spots in research, and discover disease similarities by phenotype for clinical trial design.


Uses AI to: Build intelligence from distributed datasets, including through privacy-safe transfer and federated learning. Allows researchers to: Overcome the problem of data-sharing in healthcare to automate diagnostics, predict treatment outcomes, and optimize clinical trials.


Uses AI to: Analyze over 114 million chemical structures, clinical trial information, regulatory details, toxicity data, and over 121 million patents and other sources. Allows researchers to: Validate chemical development projects.


Uses AI to: Organize and prioritize data in a contextual manner, enabling interactive 3D diagrams illustrating biological information. Allows researchers to: Rapidly generate testable hypotheses from complex, omic, and multi-omic data sets.

Plex Research

Uses AI to: Allow for intuitive searches on the world's biomedical research data. Allows researchers to: Find relevant results for drug discovery-related queries such as compounds for a specified target.


Uses AI to: Retrieve and visualize relevant results from multiple biomedical data sources. Allows researchers to: Gain a deeper understanding of a topic and avoid missing key information.


Uses AI to: Search and organize information in research papers, clinical trial listings, and patents. Allows researchers to: Save time searching for information while receiving more relevant and personalized results.


Uses AI to: Curate, in combination with human expertise, millions of scientific papers from thousands of publications. Allows researchers to: Stay up-to-date with new scientific publications and patents.


Uses AI to: Enable natural language search on billions of rows of data from any source. Allows researchers to: Speed analysis of clinical trial results and historical genomics data.

Understand Mechanisms of Disease

Cambridge Cancer Genomics

Uses AI to: Predict cancer progression from tumor DNA in blood samples. Allows researchers to: Determine treatment response and relapse earlier, and use Bayesian adaptive clinical trial design to increase the success of late stage trials.


Uses AI to: Organize and standardize immune-related gene, protein, cell, and microbiome data into a single, machine-readable, cell-level view of the immune-system. Allows researchers to: Gain novel insights related to mechanisms of disease, clinical markers, and drug discovery and validation.


Uses AI to: Analyze 200 omics databases, connecting published literature, experimental data, and clinical data. Allows researchers to: Get insight into how molecular mechanisms influence cell and tissue functions, and how these in turn influence phenotypes and disease pathology.


Uses AI to: Link phenotypic traits to genetic mutations. Allows researchers to: Discover new clinical signs, symptoms, and genes for biomarkers, and access data to develop, test, and market precision medicines.


Uses AI to: Transform all available data about Parkinson's disease into machine-readable graphs. Allows researchers to: Have a complete map of Parkinson's disease—the "Human Parkinsome"—and use it to better understand how the disease works, identify biomarkers, develop new targets, and evaluate treatments.


Uses AI to: Find combinations of genomic, phenotypic, and clinical features that define disease risk, prognosis, and therapy response in a complex disease population. Allows researchers to: Find novel drug targets in existing datasets, identify drug repurposing opportunities, and improve biomarker-driven patient stratification strategies.

Phenomic AI

Uses AI to: Analyze cell and tissue phenotypes in microscopy data. Allows researchers to: Rapidly and accurately profile single cells in microscopy images.


Uses AI to: Analyze metabolomic data along with other large-scale molecular information such as data from genes, proteins, drugs, and diseases. Allows researchers to: Find disease pathways, novel drug targets, new therapeutic effects for existing drugs, molecular mechanisms for pharmacological effects, and new biomarkers.

Sensyne Health

Uses AI to: Analyze ethically sourced, clinically curated, anonymised patient data from NHS Foundation Trusts. Allows researchers to: Discern potential new physiological pathways and identify subgroups of patients most likely to respond well to treatments.

Structura Biotechnology

Uses AI to: Enable high-throughput structure discovery of proteins and molecular complexes from cryo-EM data. Allows researchers to: Discover and understand the detailed three-dimensional structure of important protein molecules, complexes, and drug targets.

Establish Biomarkers

Uses AI to: Reliably find complex patterns in high-dimensionality biomedical data. Allows researchers to: Predict disease status, stratify patients, and provide decision support in drug development and precision medicine.


Uses AI to: Uncover clinically relevant blood-based biomarker patterns and relationships. Allows researchers to: Enable personalized approaches to therapy selection and a better understanding of complex diseases like cancer.

Evoke Neuroscience

Uses AI to: Identify unique neural signatures of Alzheimer’s disease in electroencephalography data. Allows researchers to: Monitor response to Alzheimer's disease treatments, including in clinical trials.


Uses AI to: Analyze genomic data related to cancer and other diseases. Allows researchers to: Discover biomarkers associated with drug responses.

Generate Data and Models

TARA Biosystems

Uses AI to: Generate models from data on mature cardiac tissue engineered from induced-pluripotent stem cells. Allows researchers to: Predict the physiological effects of different drugs for heart conditions.

Repurpose Existing Drugs


Uses AI to: Analyze data from sources including patient stem cells. Allows researchers to: Discover drugs for neurodenerative diseases, including ALS.


Uses AI to: Analyze data to find non-obvious, mechanism-of-action based associations between compounds, molecular targets, and diseases. Allows researchers to: Reposition late preclinical stage drugs in multiple sclerosis, mitochondrial diseases, oncology, epilepsy and chronic fatigue syndrome / myalgic encephalopathy.


Uses AI to: Find applications for existing approved drugs or clinically validated candidates. Allows researchers to: Develop a pipeline of product candidates in immuno-oncology, neuroscience, and rare diseases.


Uses AI to: Match existing drugs with rare diseases. Allows researchers to: Repurpose existing drugs to accelerate treatment of rare diseases.

Lantern Pharma

Uses AI to: Analyze genetic signals and molecular markers for patient response to drugs. Allows researchers to: Find clinical uses for validated cancer treatments whose development has been discontinued.


Uses AI to: Screen and reposition known drugs in unrelated indications at new, lower doses. Allows researchers to: Identify synergistic combinations of repositioned drugs for diseases with high unmet medical needs.


Uses AI to: Synthesize knowledge from multiple biomedical sources (using nference technology). Allows researchers to: Discover potential rare disease indications and subsets of patients who may respond favorably to an existing drug.

Recursion Pharmaceuticals

Uses AI to: Conduct experimental biology at scale by testing thousands of compounds on hundreds of cellular disease models in parallel. Allows researchers to: Rapidly identify new indications for many known drugs and shelved assets.


Uses AI to: Interpret how drug compounds would interact with people in the real world. Allows researchers to: Predict new indications for existing drugs (current focus).

Generate Novel Drug Candidates

A2A Pharmaceuticals

Uses AI to: Iterate small molecules to find candidates with optimal properties for a target. Allows researchers to: Design novel treatments for diseases including cancer, bacterial infection, and muscular dystrophy.


Uses AI to: Predict synergistic plant mixes. Allows researchers to: Develop plant-based compounds that counteract bacterial infection and restore microbiota equilibrium.


Uses AI to: Predict antibody-antigen binding. Allows researchers to: Generate antibody drug candidates in one day.

Arbor Biotechnologies

Uses AI to: Curate and mine gene variants. Allows researchers to: Accelerate discovery of proteins for improving human health.


Uses AI to: Predict drug candidates by leveraging a convolutional neural network trained on a huge amount of organic chemistry data. Allows researchers to: Generate novel drug candidates faster (with a number of candidates already in development with partners).


Uses AI to: Generate insights from molecular data for a deep understanding of disease biology and patient subtypes. Allows researchers to: Discover compounds designed to most effectively address significant unmet medical needs for clinically meaningful disease subtypes.


Uses AI to: Analyze data from its "Totally Integrated Medicines Engine" platform. Allows researchers to: Perform and screen billions of assays in a single day.


Uses AI to: Ingest scientific research data sets, then form and qualify hypotheses and generate novel insights. Allows researchers to: Identify novel drug candidates (via life science-focused subsidiary BenevolentBio).


Uses AI to: Analyze data from patient samples in both healthy and diseased states to generate novel biomarkers and therapeutic targets. Allows researchers to: Generate therapeutic targets from biological data in an unbiased way, and implement personalized medicine at scale.

BioAge Labs

Uses AI to: Analyze omics data related to aging. Allows researchers to: Develop biomarkers and drugs that impact human aging.

BlackThorn Therapeutics

Uses AI to: Generate novel insights into neurobehavioral health from prorietary data sources including brain imaging and functional assessment tools. Allows researchers to: Develop new drugs targeting receptors in dysregulated brain circuits.

Celsius Therapeutics

Uses AI to: Analyze data from single-cell RNA sequencing. Allows researchers to: Understand genes in specific cells that trigger disease, then develop precision treatments for those diseases along with companion biomarker-based diagnostic tools.


Uses AI to: Generate molecular structures based on user-defined criteria. Allows researchers to: Design novel small organic molecules and scaffolds.

Cloud Pharmaceuticals

Uses AI to: Search a virtual chemical space, predict binding affinity and allow filtering for drug-like properties, safety, and synthesizability. Allows researchers to: Speed drug development with a higher success rate and better targeting of hard-to-drug indications.

Clover Therapeutics

Uses AI to: Analyze clinical and genomic data from consenting participants who are members of insurer Clover Health. Allows researchers to: Discover strategies or interventions that can form the basis of new therapies for pharma partners.

Cotinga Pharmaceuticals

Uses AI to: Predict biological activity from molecular structures. Allows researchers to: Intervene in pathways that cancer cells use to escape cell death.


Uses AI to: Integrate clinical trial data with real-world evidence and public datasets to eliminate silos of health information. Allows researchers to: Reduce the cost of drug development, and improve the time-to-market and likelihood of success for new drugs.

Deep Genomics

Uses AI to: Search 69 billion molecules with the goal of generating a library of 1,000 compounds to manipulate cell biology. Allows researchers to: Unlock new classes of antisense oligonucleotide therapies.


Uses AI to: Analyze RNA data from patients to identify new biomarkers and drug targets. Allows researchers to: Accelerate discovery of RNA therapeutics.

Engine Biosciences

Uses AI to: Uncover gene interactions and biological networks underlying diseases, and test therapies that target them. Allows researchers to: Make analyses and predictions for precision medicine applications.


Uses AI to: Analyze complex networks of molecular interactions in cells. Allows researchers to: Acquire or in-license drug candidates.


Uses AI to: Elucidate novel tumor biology and innovative strategies that shut down key cancer pathways. Allows researchers to: Focus on the most promising strategies to tackle essential oncogenes, accelerating development of oncology drug candidates.


Uses AI to: Identify suitable targets for vaccines and antibodies in complex immunological data. Allows researchers to: Program the immune system to fight off diseases from cancers to hard-to-treat infections.


Uses AI to: Learn best-practices from drug discovery data and experienced drug hunters. Allows researchers to: Generate drug candidates in roughly one-quarter the time of traditional approaches.


Uses AI to: Generate novel insights and predictions from biological data, chemical data, and curated databases of approved drugs. Allows researchers to: Leverage existing data to develop therapies (currently focused on infectious disease diagnostics and treatments).

Gritstone Oncology

Uses AI to: Predict immune targets for cancer immunotherapy using a model trained on extensive human tumor data. Allows researchers to: Develop new approaches to drive potent, tumor-specific immune responses that provide therapeutic benefit to a large number of cancer patients.


Uses AI to: Simulate, filter, and search for molecules with "Generative Tensorial Networks." Allows researchers to: Discover molecules otherwise hidden from view.


Uses AI to: Design novel compounds that optimize for specific objectives. Allows researchers to: Improve the success rate of in silico to in vitro translation.

Insilico Medicine

Uses AI to: Predict pharmacological properties of drugs and supplements, and identify novel biomarkers. Allows researchers to: Generate novel therapeutic candidates, with a focus on aging and age-related diseases.


Uses AI to: Generate models from large, high-quality datasets. Allows researchers to: Address key problems in the drug discovery and development process. (Not clear on their positioning at this point, but recent partnerships suggest they aim to generate drug candidates.)

LAM Therapeutics

Uses AI to: Analyze data from next-gen sequencing, genome editing, chemical genomics, and combinational drug screening to find the most appropriate patients to treat with novel therapeutics. Allows researchers to: Develop precision therapeutics for cancer and rare diseases.


Uses AI to: Shorten discovery and screening, lead optimization, and ADMET studies. Allows researchers to: Create "build-to-buy" partnerships, forming startups around new drug discovery programs that pharmaceutical companies can then acquire if successful.

Mind the Byte

Uses AI to: Analyze data in a SaaS-based bioinformatics platform for computational drug discovery. Allows researchers to: Leverage big data and machine learning for every stage of the drug discovery process, from target-identification to post-marketing activities, with no need for their own hardware infrastructure.


Uses AI to: Discover connections between drugs and diseases at a systems level by analyzing hundreds of millions of raw human, biological, pharmacological, and clinical data points. Allows researchers to: Find drug candidates and biomarkers predictive of efficacy for diseases.


Uses AI to: Predict the therapeutic potential of food-derived bioactive peptides. Allows researchers to: Cost-effectively develop highly targeted treatments for specific diseases from natural food sources.


Uses AI to: Design protein drugs through reinforcement learning. Allows researchers to: Target a wider array of binding sites, target diseases with high specificity, and create compounds that are easier to synthesize and test.

Quantitative Medicine

Uses AI to: Analyze many drug discovery factors simultaneously, such as effects, side effects, and toxicity. Allows researchers to: Solve complex drug discovery optimization problems.

Relay Therapeutics

Uses AI to: Understand protein motion—how protein shape influences health and disease. Allows researchers to: Design compounds that can target and stabilize proteins in a normal shape.

Resonant Therapeutics

Uses AI to: Assess and prioritize a library of drug candidates derived from analyzing tumor microenvironments. Allows researchers to: Simultaneously discover novel targets and functional antibodies for cancer.


Uses AI to: Analyze metabolomic and bioassay datasets to uncover insights into human health and disease, as well as potential new chemical scaffolds. Allows researchers to: Find new therapeutic targets and biomarkers for diseases and aging, as well as novel compounds that can provide the foundation for new drugs.

Spring Discovery

Uses AI to: Accelerate experimentation for discovering therapies for aging and related diseases. Allows researchers to: Uncover new therapies for diseases of aging by targeting the biological processes of aging itself.

Systems Oncology

Uses AI to: Aggregate and mine disparate biomedical datasets to reveal novel vulnerabilities in cancer. Allows researchers to: Exploit novel cancer vulnerabilities with innovative treatments.


Uses AI to: Screen compound libraries for efficacy against a disease, identify new drug candidates from a public library, and identify biologic targets. Allows researchers to: Speed and reduce costs for drug discovery.


Uses AI to: Perform de novo molecular design and molecular simulation. Allows researchers to: Automate design of small molecule drugs.


Uses AI to: Analyze data from the world's largest database of microbiomes in the world. Allows researchers to: Develop new treatments inspired by novel insights into human-microbe relationships.

Verge Genomics

Uses AI to: Map hundreds of genes that cause a disease, then find drugs that target all at once. Allows researchers to: Find cures for complex diseases—especialy brain diseases—that involve a network of genes.


Uses AI to: Analyze concepts in unstructured documents and perform advanced chemistry analysis. Allows researchers to: Mine data in electronic laboratory notebooks, find unexpected relationships between mechanisms of action and disease, and generate novel compounds optimized for specific variables.

Validate and Optimize Drug Candidates


Uses AI to: Predict protein-ligand binding. Allows researchers to: Select better drug candidates, exclude toxic or reactive molecules, and improve ADMET profiles.


Uses AI to: Better predict how molecules will behave in the body. Allows researchers to: Accelerate development of more effective therapies.


Uses AI to: Predict the impact of ligands on protein-protein interactions. Allows researchers to: Increase the success rate of small molecule drugs targeting protein-protein interactions.

InVivo AI

Uses AI to: Integrate structural, target, and pathway-based descriptors to generate toxicological profiles of small molecule drugs in silico. Allows researchers to: Reduce the time and cost of preclinical decision-making while increasing the likelihood of success for compounds selected for clinical trials.


Uses AI to: Understand how patient subpopulations respond to treatments. Allows researchers to: Match drugs to patients, with the potential to extract information from failing or failed clinical trials and revive the prospects for at-risk drugs.

Reverie Labs

Uses AI to: Predict potency and pharmacokinetic properties of small molecules, and conceive new molecules to optimize for them. Allows researchers to: Accelerate preclinical drug development by generating and improving leads.


Uses AI to: Simulate experiments. Allows researchers to: Gain insights into how a drug works from the preclinical phase through phases I, II, and III.


Uses AI to: Predict pharmacology of potential new drug candidates through pharmacokinetic and pharmacodynamic modeling and simulation. Allows researchers to: Narrow the number of drug candidates that offer anticipated effects for specified diseases.


Uses AI to: Predict the crystalized form a drug will take. Allows researchers to: Understand the potential safety, stability, and efficacy of drug candidates.

Design Drugs


Uses AI to: Consider the polypharmacology, pharmacokinetics, and structural pharmacogenomics of molecules. Allows researchers to: Explain mechanisms of action, and de novo design or optimize multi-targeted drug-like molecules with preferred pharmacokinetic properties while minimizing off-target interactions.

Fetch Biosciences

Uses AI to: Understand structure-to-function relationships underpinning existing biomolecules. Allows researchers to: Engineer novel biomolecules with optimized functions.


Uses AI to: Predict antibody binding, similarity, and cross-reactivity. Allows researchers to: Characterize, find substitutes for, and identify potential off-target effects of a specified antibody.


Uses AI to: Design structurally new small molecules for validated biological targets. Allows researchers to: Collaborate with each other (using what they call "Human Collective Intelligence") and AI to jointly develop safer and more effective therapeutics.


Uses AI to: Analyze public and private data, with a claim to work with less data, noisier data, and more biased data than alternative approaches. Allows researchers to: Predict how a potential drug will behave in the lab and the body, with a focus on neurodegeneration, cardiovascular disease, and oncology.


Uses AI to: Predict protein features and characteristics. Allows researchers to: Reduce complexity in protein design, detect production and characterisation issues, and discover novel protein features.


Uses AI to: Design peptides based on a target's solved crystal structure. Allows researchers to: Speed development of peptide drugs, which have high selectivity and low toxicity.

Remedium AI

Uses AI to: Identify minimum functional units of protein drug candidates by building specific predictive models from customized data sets. Allows researchers to: Reduce dependence on mass screenings, both virtual and chemical, through rapid selection of small molecule agonists, antagonists, or functional mimics of protein drug candidates.


Uses AI to: Make and modify DNA. Allows researchers to: Prototype and edit recombinant molecules for vaccines and biologic medicines.


Uses AI to: Optimize synthetic biotherapies that are easy to manufacture, shelf stable, and outperform known antibodies. Allows researchers to: Mimic proven monoclonal antibodies with safer, more effective biologic alternatives.

Design Preclinical Experiments


Uses AI to: Decode open- and closed-access data on reagents such as antibodies and present published figures with actionable insights. Allows researchers to: Reduce time, money, and uncertainty in planning experiments.

Desktop Genetics

Uses AI to: Determine biological variables influencing CRISPR guide design. Allows researchers to: Improve activity and reduce experimental bias in the selection of guides for CRISPR libraries.

Run Preclinical Experiments


Uses AI to: Generate novel insights from high-quality cancer research data gathered by a robotic system. Allows researchers to: Configure experiments remotely and have them executed in a fully automated cloud laboratory.

Berkeley Lights

Uses AI to: Automate selection, manipulation, and analysis of cells. Allows researchers to: Expedite development of cell lines and automate manufacturing of cellular therapeutics.

Emerald Cloud Lab

Uses AI to: Conduct experiments in an automated lab exactly as specified. Allows researchers to: Run experiments in a central lab from anywhere in the world.


Uses AI to: Make sense of data from testing drug candidates on their bioartificial human heart constructs. Allows researchers to: More accurately evaluate a drug candidate's cardiac safety and effectiveness.


Uses AI to: Build models to understand complex biological systems within Antha, its language and software platform for biology experiments. Allows researchers to: Optimize, reproduce, automate, and scale experiment workflows.


Uses AI to: Automate sample analysis with a robotic cloud laboratory. Allows researchers to: Generate needed data quickly and reliably with an outsourced, on-demand, automated lab.

Design Clinical Trials

BullFrog AI

Uses AI to: Predict which patients will respond to therapies in development. Allows researchers to: Advance therapies that fail phase 3 studies.

GNS Healthcare

Uses AI to: Transform diverse streams of biomedical and healthcare data into computer models representative of individual patients. Allows researchers to: Deliver personalized medicine at scale, by revealing optimal health interventions for individual patients.


Uses AI to: Estimate the risk of clinical trials, and interpret the multitude of factors that contribute to that risk. Allows researchers to: De-risk drug development at the clinical trial stage.

Keen Eye

Uses AI to: Empower pathologists to access new insights in biomedical data. Allows researchers to: Increase diagnostics sensitivity and find predictive signatures of drug response.


Uses AI to: Improve pathology analysis. Allows researchers to: Identify patients that would benefit from novel therapies.


Uses AI to: Automate histopathology. Allows researchers to: Stratify patients for clinical trials.

Uses AI to: Optimize clinical trial study design. Allows researchers to: Make it easier for patients to enroll and engage in clinical trials, eliminate unnecessary clinical operations burdens, and gain real-time insight into study health.

Recruit for Clinical Trials


Uses AI to: Make sense of unorganized and unstructured data about clinical trials. Allows researchers to: Enrol more patients in appropriate trials.


Uses AI to: Transform unstructured clinical notes into rich structured data. Allows researchers to: Increase the speed of screening eligible subjects for clinical trials against trial-specific inclusion and exclusion criteria.


Uses AI to: Screen clinical trial sites and investigators at scale. Allows researchers to: Select appropriate sites to improve patient recruitment rates and accelerate clinical trials.

Deep 6 AI

Uses AI to: Analyze medical records to find patients for clinical trials. Allows researchers to: Accelerate patient recruitment to complete clinical trials faster.

Deep Lens

Uses AI to: Classify digital pathology images. Allows researchers to: Identify and triage patients for clinical trials at the time of diagnosis.

Massive Bio

Uses AI to: Match cancer patients with active clinical trials. Allows researchers to: Facilitate oncology clinical trial enrollment.

Uses AI to: Automate matching cancer patients to clinical trials through personal medical history and genetic analysis. Allows researchers to: Expedite clinical trial enrollment for cancer treatments.

Notable Labs

Uses AI to: Automate evaluating the impact of drug combinations on cancer cells. Allows researchers to: Prioritize drugs for patients with cancer, including to ensure they're enrolled in the most appropriate clinical trials.

Optimize Clinical Trials


Uses AI to: Visually confirm medication ingestion via smartphone. Allows researchers to: Improve medication adherence in clinical trials.


Uses AI to: Analyze cancer biomarkers in 60 seconds from a drop of blood using an at-home device slightly bigger than an Amazon Echo. Allows researchers to: Optimize oncology drug development with a biomarker monitoring platform and millions of patient datapoints.

Brite Health

Uses AI to: Analyze structured and unstructured clinical trial participant data. Allows researchers to: Reduce clinical trial dropout rates through personalized communication.


Uses AI to: Analyze radiological images to produce clinically actionable information. Allows researchers to: Predict a patient's disease progression and treatment response, for clinical trial stratification and companion diagnostics.

nQ Medical

Uses AI to: Find hidden health signals in data from personal devices such as laptops and smartphones. Allows researchers to: Optimize clinical trials for neurological diseases, including through better, faster identification of ideal study participants, less in-clinic observation, improved compliance, and earlier measure of drug impact.


Uses AI to: Analyze multiple data types from multiple systems related to clinical trials. (Note: Technically not a startup, but is increasingly using AI in its offering.) Allows researchers to: Optimize study planning and study conduct, and gain more real-world and commercial insights.

WinterLight Labs

Uses AI to: Assess and monitor cognitive health by analyzing a short speech sample. Allows researchers to: Identify patients, screen patients, and evaluate response to therapy for clinical trials of mental health treatments.

Publish Data

Deep Intelligent Pharma

Uses AI to: Make the medical writing process more efficient and creative (amongst other things). Allows researchers to: Save time producing documents, including regulatory documents, while ensuring content quality and data accuracy.


Uses AI to: Write a draft scientific manuscript based on provided data. Allows researchers to: Get a "head start" when writing a scientific manuscript to submit for publishing.

Analyze Real World Evidence


Uses AI to: Analyze medical and pharmacy claims data. Allows researchers to: Understand which treatments work best for which patients at what times.

Concerto HealthAI

Uses AI to: Extract insights from data on oncology patients' experience with drugs. Allows researchers to: Accelerate the generation of evidence for new therapeutic approaches.

So there you have it. What did I miss? Did I get anything wrong in the descriptions? Please let me know in the comments.

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