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Artificial intelligence (AI) has taken the biotech industry by storm, allowing companies to speed up the drug discovery process while also making it more cost-effective. With so many companies in the industry now embracing the technology, we take a look at 12 AI drug discovery companies.
The COVID-19 pandemic revealed AI to be an essential tool in helping to find treatments and vaccines with greater speed and precision. Since then, there have been several drug discovery breakthroughs for AI within the biopharma industry, from helping to quickly and efficiently discover a new antibiotic called abaucin to combat a multi-drug resistant bacteria, to fully discovering and designing a drug that has entered clinical trials.
Here are 12 AI drug discovery companies currently making great strides with their technology.
Table of contents
Anima Biotech
Technology: mRNA biology modulators
Disease areas: Immunology, oncology and neuroscience
Recent news: Announced promising preclinical data for lead pulmonary fibrosis candidate
Anima Biotech’s AI drug discovery technology is built around its mRNA Lightning.AI platform, which images hundreds of cellular pathways in both healthy and diseased cells to train disease-specific AI models, making use of neural networks to help these models distinguish between healthy and diseased cells and identify dysregulated pathways. These pathways are subsequently analyzed to uncover novel targets backed by experimental validation.
Anima currently has 20 preclinical candidates being evaluated for immunology, oncology, and neuroscience indications, with its most advanced candidate indicated for the treatment of lung fibrosis. The company announced in February 2024 that this candidate had shown promising preclinical results and could open up new avenues for treating patients with idiopathic pulmonary fibrosis.
The AI drug discovery company also has ongoing collaborations with several pharma giants. After initially partnering with Eli Lilly in 2018 and Takeda in 2021, its most recent partnership was formed with AbbVie in 2023 for the discovery and development of mRNA biology modulators against oncology and immunology targets.
Atomwise
Technology: TYK2 inhibitor
Disease area: Autoimmune and autoinflammatory diseases
Recent news: Published results showcasing AtomNet’s ability for drug discovery
Atomwise is leveraging the power of AI in an attempt to revolutionize small molecule drug discovery. The company wants to tackle the most challenging, seemingly impossible targets and streamline the drug discovery process to give drug developers more shots on goal.
Atomwise’s approach to drug discovery shifts the mode of drug discovery away from serendipitous discovery and toward search based on structure, making the drug discovery process more rational, effective, and efficient. The company’s AtomNet platform incorporates deep learning for structure-based drug design, enabling the rapid, AI-powered search of its proprietary library of more than three trillion synthesizable compounds.
In April 2024, the AI drug discovery company published results from a 318-target study highlighting AtomNet as a viable alternative to high-throughput screening, with the AI platform identifying structurally novel hits for 235 of the 318 targets evaluated in the study.
In one of Atomwise’s biggest deals, the AI drug discovery company signed a strategic multi-target research collaboration with pharma giant Sanofi, which leverages its AtomNet platform for computational discovery and research of up to five drug targets.
Meanwhile, in October 2023, the company also announced the nomination of its first AI-driven development candidate – an orally bioavailable and allosteric TYK2 inhibitor. TYK2 is a key mediator in cytokine signaling pathways linked to a broad range of immune-mediated inflammatory conditions. By modulating the TYK2 pathway, the candidate has the potential to treat a wide range of autoimmune and autoinflammatory diseases, including inflammatory bowel disease, systemic lupus erythematosus, psoriasis, and psoriatic arthritis.
A top priority for Atomwise now is to get this candidate into human testing.
BPGbio
Technology: Drug-lipid conjugate nanodispersion
Disease areas: Oncology, neurology and rare diseases
Recent news: Formed 5-year partnership with the University of Oxford for novel protein degradation technologies
Named as the 2024 “BioTech AI Company of the Year” by BioTech Breakthrough Awards, BPGbio has an AI platform called NAi Interrogative Biology, which leverages one of the world’s largest non-governmental biobanks with more than 100,000 clinically annotated and multi-omics annotated patient samples. Powered by causal AI and the world’s fastest supercomputer, Frontier, at Oak Ridge National Laboratory, the platform accelerates the identification of novel drug targets and biomarkers, enabling BPGbio to advance treatments for critical diseases.
After acquiring all of Boston-based biotech BERG’s assets in 2023, BPGbio now has a pipeline of late-stage clinical assets in oncology, rare diseases, and neurology. Its lead asset, BPM31510, is a drug-lipid conjugate nanodispersion containing ubidecarenone (CoQ10) that is being tested as topical, intravenous, and oral formulations. CoQ10 is a vitamin-like fat-soluble substance found in the mitochondria of human cells. Validated by BPGbio’s AI platform, BPM31510 has been found to induce a hallmark shift in the tumor microenvironment (TME) by modulating mitochondrial oxidative phosphorylation in aggressive tumors, leading to cancer cell death. Furthermore, in many mitochondrial diseases, restoring CoQ10 levels can overcome the effect of mutations in genes that lead to mitochondrial dysfunction.
The intravenous formulation of BPM31510 is in phase 2 trials for glioblastoma multiforme (GBM) and pancreatic cancer. It has received orphan drug designation from the U.S. Food and Drug Administration (FDA) for both of these indications. Meanwhile, the topical formulation has also recently received orphan drug designation for epidermolysis bullosa, as well as rare pediatric disease designation for primary CoQ10 deficiency.
In October 2024, BPGbio entered into a 5-year collaboration with the University of Oxford in the U.K. focused on advancing novel protein degradation technologies, particularly in oncology and central nervous system (CNS) diseases, with the goal of unlocking new therapeutic pathways for conditions with limited treatment options. This is one of many partnerships the AI drug discovery company has with academic institutions, as well as healthcare organizations. Plus, it also has partnerships with the likes of AstraZeneca and Boehringer Ingelheim formed through BERG.
Cradle Bio
Focus areas: Therapeutics, diagnostics, food, chemicals, and agriculture
Major partnerships: Novo Nordisk, Johnson & Johnson, Grifols and Twist Biosciences
Recent news: Raised $73 million in series B funding
Cradle Bio uses generative AI to help biologists design improved proteins and accelerate research and development, making it easier, quicker, and more cost-effective to create bio-based products for therapeutics, diagnostics, food, chemicals, and agriculture. The company’s AI models are trained on billions of protein sequences, as well as data generated in Cradle’s own wet lab.
Cradle operates as a platform provider, working with biotech and pharmaceutical companies to accelerate protein engineering. The company has secured partnerships with major industry players, including Novo Nordisk, Johnson & Johnson, Grifols, and Twist Biosciences, helping them refine and optimize proteins for various applications. It is working on multiple projects focused on engineering a wide range of protein modalities, including enzymes, vaccines, peptides, and antibodies across a broad spectrum of desired protein properties, such as stability, expression, activity, binding affinity, and specificity.
In November 2024, Cradle raised $73 million in series B funding to help the company accelerate the adoption of AI-powered protein engineering. This funding took the total raised by the company to date to more than $100 million.
Iktos
Technology: Small molecules
Disease areas: Inflammatory and autoimmune diseases, oncology and obesity
Recent news: Secured a €2.5 million ($2.7 million) grant from the EIC Accelerator
Based in Paris, Iktos is making use of AI and robotics synthesis automation technology for drug discovery and design, using it to rapidly identify small molecules that can become clinical candidates. By using AI, Iktos aims to speed up the drug discovery process while increasing the probability of success of drug candidates reaching clinical development. This approach has already been validated by Iktos through more than 50 academic and industrial collaborations, with pharmaceutical and biotech companies such as Janssen, Merck, Pfizer, Servier, Ono, and Teijin.
The company has multiple AI technologies to help with the discovery process. These are: Makya, a generative AI that generates optimal molecules in silico; Spaya, a retrosynthesis AI platform that processes the molecules in order to identify synthesis routes compatible with Iktos’ robots; and Ilaka, an “orchestration” AI platform that takes over to manage the entire workflow, from ordering raw materials to overseeing synthesis campaigns and directing the company’s robots to perform the chemistry. From this, the company currently has a pipeline focused on delivering preclinical candidates for inflammatory and autoimmune diseases, oncology, and obesity.
In March 2023, the company announced that it had closed a €15.5 million ($16.4 million) series A financing round, enabling it to further develop its AI and drug discovery capabilities, expand its existing SaaS software offering, as well as launch Iktos Robotics – an end-to-end, drug discovery platform that combines AI and automation of chemical synthesis to significantly accelerate drug discovery timelines.
More recently, in February 2025, Iktos secured a €2.5 million ($2.7 million) grant from the European Innovation Council (EIC) Accelerator to advance its AI and robotics technology. Plus, in January, the company signed a small molecule AI drug discovery collaboration with Cube Biotech. This partnership will leverage Iktos’s AI platform and Cube Biotech’s advanced protein technologies to develop novel agonists of the Amylin Receptor.
Insilico Medicine
Technology: Small molecule inhibitor
Disease area: Fibrosis
Recent news: Raised $110 million in series E round
Insilico Medicine is intent on using AI for every step of pharmaceutical research and development, in an effort to significantly reduce the time and cost associated with bringing life-saving medicines to patients. To achieve this, the company connects biology, chemistry, and clinical trial analysis using next-generation AI systems. Its fully integrated drug discovery suite, Pharma.AI, consists of PandaOmics (to discover and prioritize novel targets), Chemistry42 (to generate novel molecules), and InClinico (to design and predict clinical trials).
In June 2023, Insilico’s small molecule inhibitor drug candidate, INS018_055, for the treatment of idiopathic pulmonary fibrosis, became the first entirely AI-discovered and AI-designed drug to enter a phase 2 clinical trial, representing an important milestone for the industry. The company then announced in June 2024 that it had completed patient enrollment in a phase 2a study of the candidate in China. The AI company also has two more drugs in clinical stages that have been partially generated by AI. One is for COVID-19, and the other is for solid tumors.
It is worth mentioning, too, that in November 2022, Insilico signed a major collaboration deal with Sanofi, worth up to $1.2 billion. The agreement stated that Sanofi would leverage Insilico’s Pharma.AI to identify disease targets, generate new molecular data, and predict clinical trial results to advance drug candidates for up to six new targets.
Meanwhile, earlier this month, Insilico secured $110 million in series E financing, which will go toward advancing its drug development pipeline and AI platform developments.
insitro
Disease areas: Neuroscience and metabolic diseases
Recent funding: Raised $400 million in series C round
Recent news: Formed collaboration with Eli Lilly to advance novel treatments for metabolic diseases, including MASLD
By generating high-throughput, functional genomic data sets that align with patient data, and interpreting those data via novel machine learning methods, insitro builds predictive models that can accelerate target selection and the design of effective therapeutics. This AI-assisted drug discovery has built the foundation of insitro’s pipeline, which includes candidates in neuroscience and metabolic diseases that are being advanced both internally and through strategic partnerships.
Speaking of strategic partnerships, insitro has so far established several of them. Shortly after launching in 2018, it formed a partnership with Gilead to discover and develop novel therapies for nonalcoholic steatohepatitis (NASH). Then, in 2020, the company signed a deal with Bristol Myers Squibb (BMS) to discover new therapies for amyotrophic lateral sclerosis (ALS) – insitro received a $25 million milestone payment as part of this agreement in December last year for the achievement of discovery milestones and the selection of the first novel target for ALS. And, most recently, insitro teamed up with Eli Lilly to advance novel treatments for metabolic diseases, including metabolic dysfunction-associated steatotic liver disease (MASLD), based on targets identified by insitro using the company’s AI/ML-based platform.
In terms of funding outside of these collaborations, insitro also managed to bring in an impressive $400 million in series C financing back in 2021, which went toward further expanding the company’s platform capabilities and pipeline.
Isomorphic Labs
Drug discovery collaborations: Eli Lilly and Novartis
Recent achievement: Co-developed AlphaFold3 with Google DeepMind
Recent news: Expanded the scope of small molecule drug discovery agreement with Novartis
As the sister company of the prominent AI research laboratory Google Deepmind, Isomorphic Labs is working to develop cutting-edge computational techniques in fields like deep learning, reinforcement learning, active learning, representation learning, and more, to solve some of the toughest challenges in drug discovery, and some of the most stubborn scientific problems in biology, chemistry and medical research today.
The company helped Google DeepMind to develop the acclaimed AI model AlphaFold3, which can accurately predict the 3D structure of proteins. The technology has been heralded as a game-changer for drug discovery, as it has the potential to allow researchers to identify promising drug candidates much more quickly and precisely than current methods, especially since the companies decided to open-source it in November last year. Isomorphic Labs is now using AlphaFold3 internally to speed up its own drug discovery efforts.
Over the last year or so, Isomorphic Labs has formed a couple of high-profile partnerships with big pharma companies. It kicked off 2024 by announcing two strategic collaborations with Eli Lilly and Novartis that have the potential to be worth nearly $3 billion to Isomorphic Labs, excluding any royalties that may result from future drug sales. Last month, the company said that it had also expanded the scope of its small molecule drug discovery agreement with Novartis, adding up to three additional research programs.
Generate Biomedicines
Lead asset technology: Anti-TSLP antibody
Disease areas: Immunology, infectious disease and immuno-oncology
Recent news: Entered into collaboration with Novartis to discover and develop protein therapeutics
After emerging from stealth in 2020, Generate Biomedicines has made notable progress as a leader in the field of generative biology, using AI to discover and develop innovative new drug candidates. The company’s AI technology is called the Generate Platform, which works as a continuous loop to generate protein sequences to answer a specific therapeutic question, build computationally generated sequences as real proteins at scale, measure critical molecular characteristics and functions of generated proteins, and learn in order to drive improvement in the company’s engine and every molecule that is generated.
Since its inception, Generate has expanded the Generate Platform into new modalities, including into bispecifics, enzymes, T‑cell engagers, and cell therapies. The company now has a robust pipeline across immunology, infectious disease, and immuno-oncology. Its lead asset is an anti-TSLP antibody called GB-0895 for the treatment of severe asthma that is currently in phase 1 studies.
The AI drug discovery company is considered to be a “biotech unicorn” and has raised a substantial amount of money since its emergence from stealth. When it bagged $273 million in series C financing in September 2023 – in the largest private biotech fundraising round of that month – it brought its total capital raised since 2020 to nearly $700 million.
Additionally, Generate has formed some high-profile partnerships with big pharma. It partnered with Amgen in 2022 to discover and create protein therapeutics for five clinical targets across several therapeutic areas and multiple modalities. More recently, in September last year, Generate entered into an agreement with Novartis in one of the biggest biotech deals of that month. The two companies are collaborating to discover and develop protein therapeutics across multiple disease areas with the help of Generate’s generative AI platform.
Latent Labs
Technology: Designing therapeutic proteins
Business model: Partnership-driven
Recent news: Raised $40 million in series A financing
Just last month, Latent Labs stepped into the spotlight after raising $40 million in series A financing. It just so happens that it was also founded by Simon Kohl, an important figure in the development of award-winning AI model AlphaFold. But, as AlphaFold was not intended to create novel proteins from scratch, Latent Labs wants to build upon its foundation by actually creating entirely new therapeutic proteins that do not exist in nature.
This approach allows for the development of proteins that can be tailored to specific functions and could expand drug targets, as many diseases remain untreatable due to the fact that natural proteins do not provide the right molecular tools to treat them.
It is worth noting that Latent Labs operates on a partnership-driven model instead of focusing on developing its own proprietary drug candidates. By not restricting its technology to in-house projects or exclusive licensing deals, it makes its AI tools more accessible to a broader range of partners, enabling smaller biotech companies and academic institutions to utilize its technology without the need for substantial upfront investments or exclusive agreements.
Relay Therapeutics
Technology: PI3Kα inhibitor
Significant M&A activity: Acquired ZebiAI and its machine learning-DEL technology
Recent news: Secured $30 million in a PIPE financing round
Relay Therapeutics’ Dynamo platform integrates an array of computational and experimental approaches designed to drug protein targets that have previously been intractable or inadequately addressed. To accompany Relay’s own technology, the biotech also acquired ZebiAI and its machine learning-DEL (ML-DEL) approach, which can predict potential small molecules that bind to a protein of interest, in 2021.
Relay’s primary focus is on enhancing small molecule therapeutic discovery in targeted oncology and genetic disease indications. It has recently made significant progress with its breast cancer candidate, a phosphoinositide 3-kinase alpha (PI3Kα) inhibitor called RLY-2608 that is designed to target and inhibit only the mutant form of PI3Kα so that it spares the normal enzyme in the hope of minimizing off-target effects and reducing toxicity.
In September last year, Relay reported interim data from a phase 2 trial, showing that its candidate had beaten its survival goal. The study, which is testing the candidate in combination with Pfizer’s Faslodex (fulvestrant), demonstrated a median progression-free survival of 9.2 months in patients with heavily pre-treated PI3Kα-mutated, HR+/HER2- metastatic breast cancer. Relay used a subgroup of AstraZeneca’s Truqap study as its benchmark for the trial, and these results mean that RLY-2608 could eventually challenge AstraZeneca’s product if approved.
Relay secured $30 million in a private investment in public equity (PIPE) financing round in January 2024.
Recursion
Lead asset technology: Small molecule superoxide scavenger
Significant M&A activity: Merged with AI drug discovery company Exscientia
Recent news: Announced encouraging phase 2 topline data for lead asset, REC-994, in the treatment of CCM
Central to the mission of the AI drug discovery company Recursion is the Recursion Operating System (OS), a platform built across diverse technologies that continuously expands one of the world’s largest proprietary biological, chemical, and patient-centric datasets.
Meanwhile, the company has a large language model (LLM) called LOWE, which enables scientists and technologists to directly query the RecursionOS, identifying novel targets, generating novel compounds, and even scheduling compounds for synthesis and experimentation. In June 2024, Recursion announced that Bayer would be the first external beta-user of LOWE for drug discovery and development.
Last year, Recursion also combined with fellow AI drug discovery company Exscientia, in turn creating one company that, at the time of the merger, had 10 clinical readouts to look forward to over the course of 18 months. The merger also combined multiple high-profile partnerships; as well as its collaboration with Bayer, Recursion had formed deals with Genentech in neuroscience and gastrointestinal oncology, and Exscientia had formed partnerships with Sanofi and Merck in immunology and cancer.
Recursion currently has clinical candidates for oncology and rare disease indications, among others. Its most advanced asset, REC-994, is an orally bioavailable small molecule superoxide scavenger being developed for the treatment of cerebral cavernous malformation (CCM), a neurovascular disease that impacts around 360,000 symptomatic patients in the U.S., U.K., France, Germany, Italy, and Spain. Recursion announced encouraging topline results from a phase 2 trial of the candidate in September 2024, as REC-994 met its primary endpoint of safety and tolerability, plus magnetic resonance imaging-based secondary efficacy endpoints showed a trend towards reduced lesion volume and hemosiderin ring size in patients at the highest dose (400mg) as compared to placebo.
AI in drug discovery: A rocky road in recent times, but potential still remains clear
Despite the sheer amount of companies choosing to work in the AI drug discovery field, there have been questions raised regarding whether we are in an AI bubble that could be about to burst as the initial wave of optimism surrounding the technology is being tempered by the realities of complex biological systems and the challenges of translating AI-driven discoveries into clinical successes.
However, as Niven R. Narain, the president and chief executive officer (CEO) of BPGbio, mentioned in an article for Forbes, perhaps the problem is not AI itself, but the approach that certain companies have taken to utilizing it. Narain said: “The underlying issue for pharma companies using AI lies in the quality of the inputs and how AI models are used. Think of AI as the teamwork and coaching that enable players to reach their full potential.”
As can be seen from the successes of the companies mentioned in this article, there is clearly still enormous potential for AI in drug discovery if the technology is used in the right way.
According to Grand View Research, the global AI in drug discovery market size was valued at $1.1 billion in 2022, and is expected to expand at a compound annual growth rate (CAGR) of 29.6% from 2023 to 2030. The report states that the growing demand for the discovery and development of novel drug therapies and increasing manufacturing capacities of the life science industry are driving the demand for AI-empowered solutions in the drug discovery processes. As this report suggests, AI for drug discovery is clearly still a growing field within the biopharma industry. Inevitably, as it grows even larger, we will see more companies come to the forefront of the field, hoping to change the face of drug discovery – and also the biopharma industry as a whole – so that the entire drug development process can become faster, more consistent, more accurate, and more scalable. Whether this will culminate in a wide array of clinical success, we will just have to wait and see.
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