These 20 Companies and Startups are leading AI Drug Discovery and Development

If drug discovery is your bread and butter, you might already know that drug development often takes a decade of research and billions in investment before the drug reaches the market. In other words, the drug development process is complicated, difficult, and a rather costly business. But all hope’s not lost yet.

To detangle the process of drug discovery. Artificial Intelligence and machine learning have come as a ray of hope for the pharmaceutical industry. AI solutions enable researchers to design novel drugs with the desired properties rapidly. Many companies are working on designing novel drug molecules using these advanced technologies.

Talking about novel drugs, every year, patents on these novel drugs expire. As monitoring these drug patents is important to many pharma companies, we listed over 200 drug patents that are expiring between 2025 and 2030.

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Who are these companies, and how are they using the power of AI and ML?

To find out, we delved into this technology and looked into companies offering unique solutions. We found some startups using innovative approaches and filing patents to solve problems faced during the drug discovery process. Many companies are collaborating with and acquiring different start-ups to become future leaders in this domain. In this article, we will focus on the start-ups leading and shaping this industry by harnessing AI to discover cures for serious human diseases.

1. Exscientia

Founded in 2012, Exscientia developed an AI-powered design platform known as Centaur Chemist, which not only identifies potential new drug targets but also builds those drugs and sends them to clinical trials.

With around 14 patents filed, they’ve got a considerable IP portfolio. 7 of these 14 patents relate to the AI domain itself. One of its recent patents, US20200013486A1, describes an in silico system that helps solve real industry problems, namely lead optimization and the identification of drug effectiveness. Now, if I take the first step in the drug discovery process- to select a chemical moiety and optimize its effectiveness, performing this task manually can be a tedious and lengthy process. But this patent proves an innovative way of lead molecule optimization via –

1. True lead optimization involves assessing large numbers of hypothetical molecules and converging on a smaller set of potentially active molecules, thereby reducing the number of compounds that must be synthesized and tested during a project.
2. Prediction of drug candidates having desired biological properties and activities.

It simplifies the de novo drug design process and reduces the costs of lead optimization, thereby directly impacting drug discovery.

The company has recently announced its first AI-designed molecule for treating cancer, co-developed with Evotec, that uses the body’s own immune system. It will be entering human clinical trials soon. This happened following its 2020 announcement of bringing an AI-designed molecule for treating obsessive-compulsive disorder (OCD) to a phase 1 clinical trial in partnership with Japanese collaborator Sumitomo Dainippon Pharma.

Acquisition/Mergers and Collaborations

Exscientia has acquired Allcyte, an Austrian company developing an AI platform to predict how well cancer treatments will work in individual patients. Allcyte’s technology relies on deep learning to analyze the effects of various drugs on live samples of an individual patient’s tissue, rather than on artificial or animal models. By using allcyte’s platforms, Exscientia will be able to take a precision medicine approach to design drug molecules, ensuring that they’re even more effective at targeting tumor tissues than those designed with Exscientia’s technology alone.

Recently, the company also entered into a joint venture with GT Apeiron Therapeutics (Apeiron), a Shanghai-based company, to provide its AI-driven drug identification and design capabilities to accelerate the discovery of a therapeutic drug.

EQRx and Exscientia also teamed up on discovery-through commercialization to bring cheaper medicines to patients faster. As part of the deal, Exscientia will assume discovery responsibilities, while EQRx will handle development and commercialization.

Where is the startup receiving its funding from?

In March 2021, a Bill Gates-backed Oxford biotech raised $100M to discover new drugs using AI (source).

In April 2021, Softbank led a $525m round in British drug developer Exscientia (source).

2. Standigm

South Korean start-up Standigm, founded in 2015, discovers drugs using AI, saving time and costs compared to traditional methods. Their AI platform, Standigm BEST, intends to explore latent chemical space to generate novel compounds with desired properties. In addition to chemical data, they analyze biomedical literature to accelerate de novo drug design.

Once candidates are identified, Standigm Insights provides biological interpretations to discover pathways, therapeutic patterns, and prioritize potential targets. The startup’s solutions eliminate uncertainty in the drug discovery process, saving time and costs during development.

In a nutshell, Standigm has automated the entire drug discovery process using its AI platforms, including Standigm ASK™ for target discovery, Standigm BEST™ for lead design, and Standigm Insight™ for drug repurposing. To date, Standigm has run 22 in-house or collaborative drug pipelines using ‌workflow AI technology.

The startup has filed a total of 10 patents. Given below are some of the interesting patents related to their innovative technology-

1. KR2018022537A – This patent primarily focuses on a method that uses machine learning to predict the effects of a drug combination. This method uses a computer algorithm to collect various data given below. All this collected data enables the prediction of the therapeutic efficacy of a new combination drug when synergistic effects are expected based on the drug combinations. This method helps identify which targets or proteins are affected by which drugs. And how they interact when used in combination for treatment purposes.
   • cell-related data
   • drug-related data and,
   • drug/cell correlation-related data
2. KR2020145835A – This patent talks about a thioridazine composition used to treat or prevent metabolic liver disease, steatosis, inflammation, reduce fibrosis, non-alcoholic fatty liver disease, non-alcoholic steatohepatitis, etc. Further, the thioridazine molecule is selected using an artificial intelligence (AI) deep-learning screening method after confirming its therapeutic efficacy.
3. WO2021080295A1 – This patent describes a method and apparatus for generating an effective compound structure by using the learning data of existing biologically active structures. The method identifies a candidate group of effective compounds through artificial intelligence based on existing compound data. Using this data, the AI system can derive a new compound with properties related to the target effective molecule and simulate it. In simple terms, this AI-based molecular structure design method automatically designs the molecular structure of a new drug candidate by selectively modifying only part of a given material structure, using existing compound data.

Where is the startup receiving its funding from?

In July 2021, Standigm secured $10M in funding from Pavilion Capital to accelerate its global competitiveness. With this funding, Standigm is ready to make more trade opportunities for its AI-driven drug assets in the global market (source).

In March 2019, Standigm raised US $11.5 million in a series B round of funding to advance its AI-powered drug pipelines toward license-out. This investment includes participants such as Kakao Ventures, Atinum Investment, DSC Investment, LB Investment, Wonik Investment Partners, Mirae Asset Venture Investment, and Mirae Asset Capital. Kakao Ventures, one of the leading early-stage VCs in Asia, has continued to invest in Standigm since its seed round (Source).

Collaborations

Since July 2019, Standigm and SK Chemicals Co., Ltd. have been in an innovation partnership, working hand in hand on the drug discovery process. Recently, this combo announced that they have successfully found a new rheumatoid arthritis indication for an FDA-approved drug and have filed a patent (Source).

The research collaboration aims to identify novel lead compounds and repurpose existing drugs for rheumatoid arthritis and nonalcoholic steatohepatitis, leveraging Standigm’s AI-powered drug discovery platforms: Standigm BEST™, Standigm Insight™, and Standigm ASK™. In this, SK Chemicals has shared its expertise in these diseases and validated the predicted targets and compounds through in vitro and in vivo studies.

Further, in 2020, Standigm Inc., along with SK Holdings C&C Co., launched its AI-based target identification platform, iCLUE&ASK™, to the public on a trial basis. The platform offers to prioritize protein targets for a given disease and presents the results with supporting evidence through an interactive user interface (source).In 2020, India-based Excelra, a global data science and data analytics company, announced its collaboration with Standigm Inc. In this collaboration, Excelra will provide its small molecule medicinal chemistry intelligence platform, GOSTAR, to Standigm Inc.

GOSTAR provides comprehensive information encompassing over 8 million compounds, manually curated from 3 million patents and 200,000 journal articles. The database contains over 28 million SAR-associated data points. A well-structured relational database can be utilized for diverse applications across different stages of the drug discovery and development lifecycle, aiding in target validation, hit identification, early lead identification, and optimization (source).

In 2017, CrystalGenomics, Inc. and Standigm, Inc. announced a collaboration to apply Artificial Intelligence (AI) to the research and development of novel drugs. In this agreement, both parties plan to work together by combining the power of Standigm’s AI technology with CrystalGenomics’ pharmaceutical expertise to discover and develop novel drugs in the therapeutic areas of cancer, rheumatoid arthritis, and liver-related diseases (source).

3. Genesis Therapeutics

Genesis Therapeutics is a USA-based start-up, founded in 2019. It unifies AI and biotech to accelerate the discovery of new medicines. The company uses neural networks, biophysical simulations, and a scalable computing platform for drug design and development.

Usually, deep learning software represents molecules as images and classifies them — like, say, this is a cat picture, or this is not a cat picture. But Genesis Therapeutics’ AI software represents molecules more naturally. A set of nodes or vertices, atoms, and things that connect them, bonds. They don’t just represent them as a bond or no bond. But there are multiple types of contacts between atoms, spatial distances, and more complex features.

The resulting representation is richer and more complex. A more complete picture of a molecule than you’d get from its chemical formula, or a stick diagram showing the different structures and bonds. Because in the world of biochemistry, nothing is as simple as a diagram. Every molecule exists in a complex, shifting 3D conformation. Important aspects, such as the distance between two carbon formations or bonding sites, are subject to many factors. Genesis attempts to model as many of those factors as it can.

Representation is the first step; the next question is, how does one leverage that representation to learn a function that takes an input and outputs a number? Like binding affinity or solubility, or a vector that predicts multiple properties at once?

The startup works at the intersection of modern deep neural network approaches and biophysical simulation — conformational changes in ligands and proteins.

Their Dynamic PotentialNet technology helps in protein structure prediction. It leverages 3D structural information of proteins and computational protein folding. Also, their AI platform helps in understanding the absorption, distribution, metabolism, elimination, and toxicity (ADMET) properties of drug candidates. (Source)

So far, the startup has only one patent. Nevertheless, an interesting one.

US20020046054A1 – This patent mainly focuses on a method for identifying individuals for clinical trials. This method comprises identifying and recruiting donors whose demographic characteristics, genomic and proteomic profiles, and medical histories make them attractive candidates for clinical trials, drug target identification, and pharmacogenomic studies.

All in all, this method enables efficient identification of research subjects and, hence, allows the biopharmaceutical industry to access a large and varied population of individuals with detailed, fully consented medical histories/data as subjects for clinical trials required for drug development and as sources of research materials.

Where is the startup receiving its funding from?

In December 2020, Genesis Therapeutics secured a $52M Series A to further accelerate AI innovation and launch a drug discovery & development pipeline. (Rock Springs Capital, T. Rowe Price Associates, Inc., Seed-round lead investor Andreessen Horowitz, Menlo Ventures, and Radical Ventures – source).

In November 2019, Genesis Therapeutics, Unifying AI and Biotech, raised $4.1M in Seed Funding led by Andreessen Horowitz to accelerate and Optimize Drug Discovery/Development. (source).

Collaborations

In 2020, Genesis Therapeutics entered into a multi-target collaboration agreement with Genentech, a member of the Roche Group. The collaboration leverages Genesis’ graph machine learning and drug discovery expertise to identify innovative drug candidates for therapeutic targets in multiple disease areas (source).

4. Data2Discovery

USA-based Data2Discovery was founded in 2012. It is the application of AI to find hidden connections and new insights in diverse, linked datasets. Their healthcare data analytics platform is intended to derive insights from large volumes of complex, heterogeneous data. The company’s platform uses an advanced stack of scalable graph technologies, public and proprietary data sources, AI and machine learning, graph mining capabilities, and extensive experience in linking and mapping data to address problems, enabling users to get results efficiently and effectively.

This startup has only one patent, US20190130290A1. This patent discloses a method for semantic analysis of disparate (different or diverse) data in an environment having a plurality of datasets with distinct information fields.

Further, this method involves creating graphs that relate specified information to information fields from multiple datasets as nodes, thereby improving the accuracy of machine learning on digital computers.

This technology is basically a data-driven process. There is vast data available on drug discovery processes, including exploratory data on chemical moieties, clinical trial data on drugs, early-stage chemical molecules, and their effectiveness.

All this data comprises effective drug molecules that can be further explored and may become potential drug substances for treating various conditions. Data2Discovery is using its platform and software to explore and screen all available data related to specific drug discovery processes and to identify the required molecule or the step where researchers need to spend more time to obtain the required chemical moiety. Their platform uses all this data, along with AI/ML technology, to plot various scalable graphs, perform graph mining, and link and map data to address the problems and provide the required results efficiently and effectively.

Where is the startup receiving its funding from?

In March 2017, Data2Discovery was awarded a $750,000 grant from the National Science Foundation (NSF) via the highly competitive Small Business Innovation Research (SBIR) Phase II program.

The grant will support Data2Discovery’s efforts to support translational and phenotypic research on vast interlinked datasets, which will include applications in Drug Repurposing, Toxicology and Safety, and Phenotypic Analysis (source).

Collaborations

In April 2017, the Open PHACTS Foundation announced the collaboration with Data2Discovery to form a Strategic Partnership. The goals of the partnership will support and build on the Open PHACTS vision of creating a sustainable, open, interoperable information infrastructure for applied life science research and development, while advancing science for the public benefit through shared knowledge and data in life science and biomedical research.

Data2Discovery Inc claims to bring extensive experience in pharmaceutical semantic linked data. The startup partners with pharmaceutical companies to develop full-stack semantic and graph capabilities to venture into real scientific problems (source).

In February 2017, Data2Discovery Inc. identified 14 potential drug repurposing opportunities for Tuberculosis (TB) using its P3 graph-based association-finding approach. This is done in an innovative partnership with the NIH National Center for Advancing Translational Sciences (NCATS) and OpenPHACTS. This small-scale project successfully demonstrated the feasibility of combining two key data resources – EU OpenPHACTS Open Pharmacological Space (OPS) and NCATS Phenotypic Drug Discovery Resource (PDDR) – with state-of-the-art graph mining tools from Data2Discovery.

The capabilities demonstrated in this project open up many opportunities for public impact in rare and neglected diseases, as well as complex disease areas being pursued by pharmaceutical companies (source).

In May 2021, Data2Discovery, along with Indiana University Crisis Technologies Innovation Lab and two partner companies, Disaster Tech and OPS, was awarded a $2.3m contract from the US Army Telemedicine and Advanced Technology Research Center (TATRC) to create a Technology in Disaster Environments Learning Accelerator (TLA).

The TLA will employ advanced data and performance science tools to identify best practices for patient care in disaster and infrastructure-degraded environments as part of the National Emergency Tele-Critical Care Network (NETCCN). Data2Discovery will use its proprietary graph technology stack, along with deep expertise in working with medical and biomedical data, to pilot capabilities that will enable insights to be gained from multiple data streams not found elsewhere (source).

5. Unlearn.AI

Founded in 2017, Unlearn.AI is a platform designed to make computational clinical trials. The company’s platform accelerates clinical trials by supplementing control groups with synthetic patient data generated using AI, which helps reduce the time to develop new medicines, enabling healthcare companies to provide patients in need with life-saving therapies sooner.

Technology – Unlearn is the only company using AI to create Digital Twins, which helps accelerate clinical trials and improve results. Unlearn’s platform uses historical datasets and disease-specific machine-learning models to generate virtual placebo patients from actual patient baseline data in clinical studies. This novel approach increases trial power and confidence, accelerates trial timelines, and enables patient-level insights. The whole process involves the following steps –

Step 01 [Creating a dataset]

The first step involves preparing a highly curated dataset so that the machine learning model can learn from the relationships.

Step 02 [digenesis-generating-our-machine-learning-model]

After preparing the dataset, it needs to be separated into two groups, one for training and the other for testing. The machine learning model builds an internal network of connections and starts generating Digital Twins.

Step 03 [digital-twin-procova]

Once the trial has started, the platform maintains records of patients’ Digital Twins. This model uses baseline data to create a complete record that predicts how the patient would have responded had they not received the experimental treatment. Next, the use of PROCOVA™ (prognostic covariate adjustment) – a statistical method that incorporates Digital Twins into statistical analysis plans to provide a more precise estimate of the treatment effect.

Step 04 [Randomized controlled trials using Digital Twins and PROCOVA™]

The prepared digital twins are incorporated into clinical trials, enabling smaller, more efficient trials. Each patient from the trial was paired with their AI-generated predicted placebo outcome or with a Digital Twin. Digital Twins maintain randomization and blinding while increasing certainty without introducing bias.

The startup has filed three patent applications. Let’s discuss each of them in brief.

1. CA3088204A1 – This patent describes a method to train an artificial intelligence system or an artificial neural network that can provide a probability of results based on provided inputs/data. This trained AI model probability identifier can be utilized in various fields such as health informatics, image/audio processing, marketing, sociology, and lab research.
2. WO2021041128A1 – This patent discloses a method for determining the treatment effects of randomized control trials (RCT). The method includes steps for receiving data from an RCT, generating results using different models (e.g., a Conditional Restricted Boltzmann Machine or a recurrent neural network), and determining treatment effects for the RCT based on the generated results. This method estimates quantities with high accuracy and precision and determines decision rules for declaring treatments effective with low error rates.
3. US20210117842A1 – This patent describes a method for training generative models (machine learning models that learn to sample from observed data) using summary statistics, so that the model-generated data satisfy specified population-level summary statistics. Furthermore, these generative models are used across a variety of fields, including economic forecasting, climate modeling, and medical research.

All these patents describe technologies that can be used to speed up the highly time-consuming process of clinical trials. Unlearn’s patents disclose the use of machine learning and artificial neural networks, such as the restricted Boltzmann machine (RBM) and recurrent neural networks, in the clinical trial process and its data to achieve effective results.

Further, their technology uses machine learning to create Digital Twins, which helps accelerate clinical trials and assess the effects of specific drugs before testing in humans. This technology is a breakthrough in drug discovery as it can simplify the whole clinical trial process and will provide its outcome early and accurately. Conducting clinical trials with such an advanced solution will definitely reduce the time and cost of drug discovery.

Also, for its breakthrough AI innovation, Unlearn.AI won the “Predictive Analytics Solution of the Year” at the 2021 BioTech Breakthrough Awards Program (Source).

Where is the startup receiving its funding from?

In Nov. 2020, Unlearn.AI announced a series of extensions, including new investments from Epic Ventures, alumni venture groups, and global pharma company Eisai (source).

In Apr. 2020, Unlearn.AI closed a $12m series to advance the use of digital twins in clinical trials. Led by 8VC, this financing will accelerate the application of Unlearn’s innovative machine-learning technology to improve clinical trial efficiency and increase confidence in results (source).

6. Insilico Medicine

Founded in 2012 and headquartered in Massachusetts, Insilico Medicine is a global clinical-stage biotechnology company that uses generative AI to connect biology, chemistry, medicine, and scientific research for end-to-end drug discovery.​

Their proprietary platform, Pharma.AI, incorporates large language models (LLMs), Nach01 (a multimodal foundation model for nature and chemical languages), and Dora, a multi-agent generative research assistant. 

A landmark achievement for Insilico is its idiopathic pulmonary fibrosis (IPF) drug, INS018_055 — the first AI-designed drug candidate to enter human clinical trials, which it did in 2021.

Insilico has reduced the average time to preclinical candidate nomination to just 12–18 months, compared to the traditional 2.5–4 years, while synthesizing and testing only 60–200 molecules per program — a fraction of the conventional requirement.​

Collaborations

Insilico has built an expanding portfolio of collaborative drug programs with pharma partners across oncology, fibrosis, CNS diseases, infectious diseases, autoimmune disorders, and aging-related conditions.​

Fundings

• In March 2025, Insilico Medicine secured $110 million in an oversubscribed Series E funding round led by Value Partners Group, with participation from new and existing investors.​ (Source)
• In June 2021, the company raised $255 million in Series C financing.​ (Source)
• In December 2025, Insilico listed on the Hong Kong Stock Exchange (IPO), with approximately 48% of net proceeds allocated to clinical R&D.​ (Source)

7. Numerion Labs (Formerly Atomwise)

Founded in 2012 and based in San Francisco, Numerion Labs applies deep learning to structure-based drug design, enabling rapid identification of small-molecule drug candidates.​

Their flagship technology, AtomNet, is a deep-learning platform for ultra-high-throughput virtual screening that predicts how small molecules interact with protein targets. 

In April 2024, the company published results from a landmark 318-target study highlighting AtomNet’s performance across a broad range of disease targets. To date, the company has completed over 750 research collaborations covering more than 600 unique disease targets and virtually screened over 16 billion novel small molecules for potential drug-protein interactions.

Collaborations

Numerion Labs works with an extensive portfolio of corporate and academic partners, including Eli Lilly and Company, Bayer, Hansoh Pharmaceuticals, and Bridge Biotherapeutics. 

It has 285 active drug discovery partnerships with researchers at top research institutions worldwide, including 15 collaborations focused on exploring COVID-19 therapies. (Source)

Fundings

• In August 2020, Atomwise raised $123 million in a funding round led by B Capital Group and Sanabil Investments, bringing its total raised to nearly $175 million.​ (Source)
• In Jan 2024, it raised $45 million in a Series C round.

8. Isomorphic Labs

Spun out of Google DeepMind in 2021, Isomorphic Labs is reimagining drug discovery from first principles using an AI-first approach to model biology and chemistry at the molecular level.​

The company’s core mission is to build machine learning models that accurately mirror the fundamentals of biology and chemistry. Their platform leverages AlphaFold, the revolutionary protein structure prediction system developed at DeepMind, as a cornerstone. 

By conducting drug discovery in silico, Isomorphic is working to drastically reduce the amount of wet-lab experimentation needed, enabling faster and more rational design of novel medicines.

“We’re building generalizable AI models capable of learning from the entire universe of protein and chemical interactions. This fundamentally breaks from the target-specific, siloed approach of conventional drug development.” — Max Jaderberg, Chief AI Officer.​

Collaborations

In 2024, Isomorphic Labs signed landmark multi-target drug discovery agreements with Eli Lilly (worth up to $1.7 billion) and Novartis (worth up to $1.2 billion), marking two of the largest AI-drug discovery deals ever announced.​ (Source)

Fundings

Isomorphic Labs is funded by Alphabet (Google’s parent company), which has backed the venture since its founding. The company raised $600 million in Series A funding in 2024, reaching a valuation of over $3 billion.​ (Source)

9. insitro

Founded in 2018 by Daphne Koller and headquartered in South San Francisco, insitro applies machine learning and functional genomics to transform drug discovery and development.​

The company builds predictive models that combine high-throughput, functional genomic datasets with patient data to accelerate target identification and therapeutic design. 

At the core of their platform is the insitro Human (ISH) platform, designed to create disease models using ML, human genetics, and functional genomics — providing insights into disease progression and predicting patient responses to potential therapies. Insitro also acquired Haystack Sciences, a high-throughput chemistry platform, to enable ML-driven molecular design.

Collaborations

Insitro entered into a $1 billion+ collaboration with Gilead Sciences focused on NASH (non-alcoholic steatohepatitis), in which Insitro’s ISH platform will help discover and validate up to 5 novel targets.​ (Source)

With Bristol Myers Squibb, the startup launched a multi-year partnership in 2021 for ALS drug discovery using ML programs.​ (Source)

Fundings

In March 2021, insitro raised $400 million in a Series C round led by Canada Pension Plan Investment Board (CPP Investments), with backing from Andreessen Horowitz, ARCH Venture Partners, GV (Google Ventures), T. Rowe Price, BlackRock, and others.​ (Source)

10. Generate:Biomedicines

Massachusetts-based Generate:Biomedicines is pioneering a new category it calls Generative Biology — using AI to programmatically design therapeutic proteins from scratch.​

The company’s flagship AI model, Chroma, is engineered to design proteins with precise biophysical, biological, and therapeutic properties. Rather than screening existing molecules, Generate:Biomedicines uses generative AI to directly design new drug candidates tailored to specific disease targets, fundamentally shifting drug discovery from a probabilistic to a deterministic process. 

The company has also compressed traditional decision cycles — embedding design logic into generative AI workflows so that candidates are evaluated and prioritized computationally, with scientists reviewing recommendations rather than driving every step.

Collaborations

Generate:Biomedicines has established partnerships with major pharma players to advance its protein-based therapeutics pipeline in oncology and immunology.​ (Source)

Fundings

Generate:Biomedicines raised $273 million in Series B funding in 2023.​ (Source)

11. Owkin

Owkin is an AI precision medicine company that applies machine learning to accelerate drug discovery and clinical development.​ 

Owkin’s platform enables doctors, medical researchers, and pharma companies to identify new drug candidates and enhance clinical trials by drawing on health data from usually siloed sources, including hospitals across the US and Europe. 

Their AI predicts biomarkers for diseases such as breast cancer and estimates treatment effectiveness through covariate adjustment, thereby increasing a trial’s statistical power without expanding its sample size. Owkin also runs in-house drug discovery to develop its own intellectual property, which it then brings to pharma partners for commercialization.​

In March 2026, Owkin spun out Waiv (formerly Owkin Dx), an independent AI precision testing company, backed by $33 million in financing led by OTB Ventures and Alpha Intelligence Capital. 

Waiv develops AI-powered tests to better identify and stratify patients in clinical settings and trials, with products like RlapsRisk BC for cancer risk profiling and MSIntuit, developed in collaboration with MSD since 2023. 

Leveraging a decade of Owkin’s foundational medical AI research and its patient data network, Waiv will remain a key strategic partner within Owkin’s broader patient validation ecosystem, which also includes organoid testing and the INVOKE clinical trial. (Source)

Collaborations

Bristol Myers Squibb (BMS) led an $80 million Series B-1 round in 2022 and partnered with Owkin on clinical trials to accelerate drug development using its AI platform.​

Another pharma giant, Sanofi, is in a collaboration focused on applying Owkin’s AI to drug discovery workflows.​ (Source)

Fundings

Owkin has raised $304.1m USD from investors such as Bpifrance Large Ventures, GV, NJF Capital, Cathay Innovation, F-Prime, Otium Capital, Eight Roads Ventures, Plug and Play, Mubadala, and MACSF (French Pension Fund for Clinicians), as well as corporate institutional partners such as Sanofi and Bristol-Myers Squibb. (Source)

12. XtalPi

Founded in 2014 and headquartered in Shenzhen, China (with global offices), XtalPi is an AI drug discovery company that uniquely combines artificial intelligence, quantum physics, and robotics to accelerate pharmaceutical research.​

XtalPi’s hybrid engine integrates AI with quantum mechanics (QM) to deliver highly accurate predictions on molecular properties — including crystal structure prediction, drug formulation, and solid-state behavior of drug compounds. This addresses one of the most underrated but critical challenges in pharma: ensuring drug compounds can be manufactured in a stable, reproducible crystalline form. 

By combining quantum-informed AI predictions with experimental data, XtalPi helps companies shorten development timelines and improve reproducibility, thereby reducing late-stage attrition caused by crystallization or formulation issues.​

Collaborations

XtalPi has rapidly gained adoption across Asia-Pacific pharma manufacturing and works with partners seeking to reduce the risk of late-stage drug failures due to solid-state issues. 

The company has been building its AI and robotics-powered R&D portfolio, inking high-value partnerships with companies including DoveTree Medicines (valued at up to $6B), Eli Lilly ($345M), and VISEN Pharmaceuticals. (Source)

Fundings

XtalPi has raised over $1.9 billion in funding, with investors including Google, Tencent, Sequoia China, SoftBank Vision Fund, and several top-tier pharma companies.​ (Source)

13. Recursion

Founded in 2013, Recursion Pharmaceuticals is a leading drug discovery startup that combines experimental biology, bioinformatics, and artificial intelligence to identify cellular-level treatments for diseases. They aim to accelerate drug discovery by combining biological insights with cutting-edge machine learning to target both rare and common diseases. 

Based in Salt Lake City, Utah, Recursion has developed an AI-powered platform to accelerate the identification of therapeutic candidates. Notably, they’ve already progressed a candidate into Phase 1 clinical development for a rare hereditary stroke syndrome using machine learning, a remarkable departure from the traditional drug development timeline. 

Collaborations

1. On July 12, 2023, Recursion announced a collaboration with NVIDIA and a $50 million investment to accelerate the development of foundation models for AI-enabled drug discovery. The companies collaborated on software for biotech and pharmaceutical companies to accelerate the development of improved treatments for patients.
2. In December 2021, the startup initiated a multi-year collaboration with Roche and Genentech, focusing on neuroscience and oncology. This partnership leverages Recursion’s OS and Biology Maps, along with Roche and Genentech’s single-cell perturbation screening data, to swiftly uncover new biological connections for innovative therapeutic initiatives. The alliance aims to launch potentially 40 programs over a decade or more.
3. In August 2020, Recursion formed a strategic, multi-year partnership with Bayer focused on fibrosis. This was extended in December 2021 to incorporate their advanced inferential search capabilities. In this enhanced collaboration, the companies are working on more than a dozen initiatives to discover new therapies for challenging fibrotic conditions affecting organs such as the lungs, liver, and heart. 

Acquisitions

On May 8, 2023, Recursion entered into agreements to acquire Cyclica (Decentralized Drug Discovery) for $40 million and Valence (Drug Design Company) for $47.5 million.

Fundings

Recursion Pharmaceuticals secured a total of $665.4 million through 18 funding rounds. Their most recent funding was obtained via a Post-IPO Equity round on July 12, 2023.

14. BenevolentAI

Headquartered in London, with research centers in Cambridge (UK) and New York, BenevolentAI was founded in 2013 with the vision of leveraging AI to revolutionize scientific discovery. 

BenevolentAI is a prominent player in AI-driven drug discovery and development, traded on the Euronext Amsterdam stock exchange. By combining its advanced AI platform, scientific acumen, and lab capabilities, the company aims to deliver innovative drug candidates with a higher likelihood of clinical success compared to traditional approaches. 

The startup has a growing portfolio of 13 named drug programs and over 10 exploratory initiatives, powered by the Benevolent Platform™.

Collaborations

1. In 2016, BenevolentAI secured an exclusive license to a portfolio of clinical-stage drug candidates from Janssen Pharmaceutica, a subsidiary of the Janssen Pharmaceutical Companies within Johnson & Johnson.
2. In 2019, BenevolentAI collaborated with AstraZeneca to use AI and machine learning to develop new therapies for chronic kidney disease (CKD) and idiopathic pulmonary fibrosis (IPF).
3. Further, in 2022, BenevolentAI and AstraZeneca decided to extend their collaborative AI-driven drug discovery initiative. This expansion encompassed systemic lupus erythematosus (SLE) and heart failure (HF), in addition to the previously included disease areas. By harnessing the capabilities of the Benevolent Platform and the biomedical Knowledge Graph, scientists and technologists from both organizations stand to gain deeper insights into fundamental disease mechanisms and uncover novel targets for therapeutic intervention.
4. In 2023, 9xchange joined forces with BenevolentAI to integrate its Ai technology into its innovative Biopharma Marketplace. This collaboration will help promote information sharing and collaboration across the biopharma industry to accelerate new drug discovery and development.

Acquisitions

In December 2021, BenevolentAI agreed to merge with Odyssey, a special-purpose acquisition company listed on Euronext Amsterdam with a €300 million valuation. Odyssey fosters growth across European healthcare, technology, media, and telecommunications (TMT) companies.

Fundings

BenevolentAI secured a total of $292 million through three funding rounds. Their most recent funding was acquired through a Private Equity round on September 17, 2019.

15. Xbiome

Xbiome stands as China’s first AI pharmaceutical firm dedicated to intestinal micro-ecology. With a foundation in this field, it employs AI technology to precisely and individually oversee the gut health of the nation’s 1.3 billion individuals. Employing artificial intelligence, Xbiome analyzes both patients’ and donors’ intestinal flora to expedite the development of impactful pharmaceutical solutions.

Collaborations

In January 2022, Aurealis Therapeutics, a synthetic biology company developing groundbreaking four-in-one cell and gene therapies and Xbiome, announced that the two companies have entered into an exclusive license and collaboration agreement to advance the clinical development and commercialization of Aurealis’ investigational Diabetic Foot Ulcer (DFU) therapy, AUP-16, as well as its application for other chronic wounds and inflammatory diseases in Greater China.

As per the terms of the agreement, Xbiome secured exclusive rights to develop and commercialize AUP-16 in the clinical stage, specifically for Diabetic Foot Ulcer, in Mainland China, Hong Kong, Macao, and Taiwan. Xbiome undertook clinical development, regulatory submissions, and commercialization of these licensed products across the designated territories.

Acquisitions

In April 2022, Xbiome announced the acquisition of the clinical-stage M201 program from Assembly Biosciences, Inc. This biotechnology company is known for developing innovative therapeutics targeting the hepatitis B virus and other viral diseases.

Fundings

Xbiome secured $124.2 million across 4 funding rounds. Their most recent financing was obtained during a Series B round on December 22, 2021.

16. Xilis

Founded in 2019, this biotech firm is dedicated to advancing precision oncology through a unique platform. Xilis’s platform assists oncologists in making informed treatment choices to enhance patient outcomes in cancer care. Additionally, it aids pharmaceutical companies in drug discovery and development.

At the core of their approach lies the utilization of patient biopsies to cultivate miniature replicas of their distinct organs and tumors. Medical professionals analyze these tissue samples to determine the disease stage and assess the tissue’s response to various drugs and therapies. This enables swift determination of the optimal treatment path.

Collaborations

In February 2023, the University of Texas MD Anderson Cancer Center formed a strategic partnership with biotech firm Xilis to accelerate the development of cutting-edge technologies and expedite the development of novel cancer therapies.

This collaboration involves integrating Xilis’ MicroOrganoSphere (MOS) platform with MD Anderson’s specialized expertise to advance therapeutic development and cancer research.

Within this agreement, the joint focus of MD Anderson and Xilis is to advance drug development and research initiatives through the innovative MOS technology.

Fundings

Xilis has secured $92 million in funding across five rounds. Their most recent financing was obtained through a Series A round on July 13, 2022.

17. Atavistik Bio

Founded in 2021, Atavistik Bio is a recently established, privately held biopharmaceutical company. The company is dedicated to advancing novel therapeutic solutions for metabolic diseases and cancer. 

Their innovative strategy involves screening libraries of metabolites and proteins to identify important binding sites. This approach empowers them to create and advance transformative therapies. Their efforts primarily focus on rectifying protein functions to address innate metabolic errors. 

Additionally, they employ metabolites to enhance the development of cancer treatments. Their technology and analytics platforms also enable them to expand their reach across different disease domains.

Collaborations

In January 2023, Atavistik Bio announced a collaboration with Plex Research to enhance its AMPS Platform’s informatics capabilities. This collaboration aims to speed up the identification of innovative small-molecule therapeutics.

Fundings

In August 2021, the startup secured a $60 million Series A financing. The funds acquired were dedicated to progressing genetically validated targets in metabolic diseases and cancer.

18. Polaris Quantum Biotech 

Established in 2020, Polaris Quantum Biotech is based in Durham, North Carolina. Polaris Quantum Biotech is transforming drug design by integrating quantum computing, artificial intelligence, and precision medicine. By merging these technologies, the company can navigate extensive chemical libraries to pinpoint potential drugs that specifically target proteins involved in disease modulation. 

Collaborations

During February 2022, Allosteric Bioscience, a company that enhances treatments for Aging and Longevity by combining Quantum Computing, Artificial Intelligence, and Biomedical sciences, entered into a collaboration agreement with Polaris Quantum Biotech. Together, they are harnessing advances in Quantum Computing and Artificial Intelligence to innovate in pharmaceutical development.

Fundings

Polaris Quantum Biotech secured $2.1 million in funding through a single Seed round on August 10, 2021.

19. AMPLY Discovery 

AMPLY Discovery is an innovative enterprise specializing in developing novel biogenic molecules with potential anti-infective applications for both animal and human health.

By leveraging advanced machine learning techniques and synthetic biology methodologies, AMPLY Discovery delves into expansive biological datasets to identify novel drug and nutraceutical candidates. Their proprietary in silico and in vitro hybrid platform facilitates this process, allowing them to identify superior molecular entities.

Their pursuits extend to addressing significant challenges such as cancer, multidrug-resistant infections, and metabolic disorders.

Fundings

AMPLY Discovery has secured £900,000 in funding across 2 rounds. They acquired their most recent funding on September 21, 2022, through a Seed round.

20. Absci Corporation

Absci Corporation is a clinical-stage generative AI drug creation company that combines deep learning with high-throughput synthetic biology to design and validate novel biologics from scratch.

At the core of their work is the Integrated Drug Creation (IDC) platform, which pairs proprietary zero-shot generative AI algorithms with a synthetic biology data engine to design, optimize, and wet-lab validate antibody drug candidates. 

In January 2026, Absci published Origin-1, a generative AI platform for de novo antibody design that integrates epitope-conditioned all-atom structure generation, and made the model available as open source. Unlike conventional drug discovery, which screens existing molecular libraries, Absci’s zero-shot approach designs antibodies de novo to bind to specific target sites without relying on pre-existing binding data, enabling a feedback loop that can take AI-designed candidates to wet-lab validation in as few as six weeks. Their automated labs screen billions of cells per week, generating proprietary data to continuously improve their AI models.

“Instead of searching for a needle in a haystack, we are creating drug candidates that possess all the desired attributes.” — Sean McClain, Founder & CEO. (Source)​

Collaborations

• Almirall: A dermatology-focused partnership launched in 2023 to develop AI-designed therapeutics for chronic skin diseases, with Absci eligible for up to $650 million in upfront, R&D, and milestone payments across two programs. (Source)​
• Caltech: An ongoing collaboration targeting the HIV gp120 protein’s “caldera” region, for which no previously identified antibody binders exist. (Source)​
• Oracle Cloud Infrastructure & AMD: A 2025 infrastructure partnership to accelerate biologics design cycles and scale AI model training using AMD Instinct MI355X GPUs. (Source)​

Fundings

• In September 2025, Absci secured $64 million in equity financing, extending its operational runway into mid-2028 to advance pipeline candidates ABS-101 and ABS-201. (Source)​

AMD made a strategic investment in Absci in January 2025 to support AI chip adoption and expand its cloud infrastructure capabilities. (Source)​

What other technologies are companies bringing into drug research?

Apart from these technological advancements, other companies are also using various technologies to accelerate drug discovery. For example, an Australian-German start-up, Quantum Brilliance, is using ultra-efficient quantum computers to reveal previously unknown compounds and uses quantum accelerators in the drug discovery process. Big pharmaceutical companies such as Boehringer Ingelheim and Merck are collaborating with this start-up to expedite drug discovery.

Also, Secondcell Bio and Alliance Care Technologies International announced a new strategic partnership to use Chromovert® Technology to accelerate drug discovery for rare genetic diseases. This technology uses a cell-based discovery platform to screen cells and select the best responders to the molecules involved in the drug discovery process.

Future Outlook

Artificial intelligence (AI) and machine learning (ML) are advanced technologies that will shape the future. Using these technologies in the drug-discovery process will help quickly identify new targets and develop cures for incurable diseases.

With the predicted market growth and the increasing number of companies and startups in this domain, we can confidently state that the future of drug discovery appears extremely promising. But the market is still new, and drug giants need to have an edge in the industry. It is important that they figure out startups that align with their goals and collaborate with them.

Learn how GreyB can help you find your desired startups and scale-ups, or reach out to us directly:

Authored By: Ganesh Solanke and Nikhil Gupta, Search Team.