Companies Making Automated Drug Discovery a Reality
Data is king in modern biopharmaceutical research, and ability to generate massive amounts of quality biomedical data represents a tremendous research and business potential in the artificial intelligence (AI)-driven drug discovery realm. Challenges associated with big biological data, such as poor reproducibility, low accessibility, low standardization, etc, represent a considerable bottleneck for the advent of AI in drug discovery at scale, and the ambitions of the industry leaders to shift from drug discovery as a largely artisanship process to a so called "industrialized" drug discovery.
AI-driven companeis demonstrate progress in drug discovery
There is a growing wave of companies building drug design platforms of new generation -- Recursion Pharmaceuticals (NASDAQ: RXRX), Insitro, Insilico Medicine, Deep Genomics, Valo Health, Relay Therapeutics (NASDAQ: RLAY), among others—companies that create highly integrated and automated AI-driven and data-centric drug design processes from biology modeling and target discovery, all the way to lead generation and optimization (sometimes referred to as “end-to-end” platforms).
These “digital biotechs” are trying to transform traditional drug discovery, a notoriously bespoke, artisan process, into a more streamlined, repeatable, data-driven process—more resembling an industrial conveyor line for drug candidates. Announcements by Exscientia (NASDAQ: EXAI) (here), Deep Genomics (here), Insilico Medicine (here), and other companies point to a situation where the average time for an entire preclinical program—from building disease hypothesis to official nomination of a preclinical drug candidate—have shrunk down to timelines as short as 11-18 months, and at fraction of costs of a typical project of similar nature conducted “traditionally”.
Rapid timelines are achieved in drug repurposing programs with previously known drugs or drug candidates, for example, using advanced multiomics analysis and network biology to derive precision biomarkers for better patient stratification and matching novel indications—as Lantern Pharma (NASDAQ: LTRN) does to rapidly expand their clinical pipeline.
However, a lot of those AI-driven “digital biotechs” are still relying on community-generated data to train machine learning models, and this may come as a limiting factor. While some of the leading players in the new wave, such as Recursion Pharmaceuticals and Insitro, are investing heavily into their own high-throughput lab facilities to get unique biology data at scale, other companies appear to be more focused on algorithms and building AI systems using data from elsewhere, and only having limited in-house capabilities to run experiments. Data generation is a bottleneck in AI-driven drug discovery
A common practice is to use community-generated, publicly available data. But it comes with a caveat: an overwhelming majority of published data may be biased or even poorly reproducible. It also lacks standardization—conditions of the experimentation may differ, leading to a substantial variation in data obtained by different research labs or companies. A lot has been written about it, and a summary of the topic was published in Nature under the title “The reproducibility crisis in the age of digital medicine”.
This brings us to a known bottleneck of “industrializing drug discovery”: the necessity for large amounts of high quality data, highly contextualized, properly annotated biological data that would be representative of the underlying biological processes and properties of cells and tissues.
As one sign of physical automation entering mainstream pipelines, Insilico Medicine unveiled a bipedal humanoid lab robot, ‘Supervisor,’ designed to learn bench skills and perform routine tasks (e.g., pipetting, reagent handling, instrument operation), feeding data back into its AI platform.
Another lab-in-the-loop effort focused on standardization and speed links Ginkgo Bioworks’ Datapoints unit, Inductive Bio, and Tangible Scientific. Their collaboration creates an integrated lab-in-the-loop system that connects AI-based ADMET prediction models with automated compound logistics and assay platforms, enabling direct feedback of experimental data for continuous model retraining and faster small-molecule optimization cycles.
However, in order for a wide-scale industrialization of drug discovery to occur, the crucial thing is the emergence of widely adopted global industrial standards for data generation and validation—and the emergence of the ecosystem of organizations which would be “producing” vast amounts of novel data following such standards.
Then, large drug makers and smaller companies would be able to adopt AI technologies to a much deeper extent. If we take the automotive industry as an example, a component of, say, an engine, developed in one part of the world would often fit into a technological process line in the other part of the world. So, highly integrated processes can be built across geographies and companies, as a “plug-and-play” paradigm.
The same approach is required in the preclinical research in drug discovery: every lab experiment, every data generation process, every dataset generated, all must be “compatible” with all other research processes, such as machine learning pipelines, etc., across the pharmaceutical and biotech communities globally. When this tectonic shift occurs, we will witness a truly exponential change in the performance of the pharmaceutical industry, something I would call “commoditization” of preclinical research.
These companies enable automated drug discovery
There is, luckily, a growing number of companies that are starting to bring about the required change in how preclinical research is done. Companies that build standardized, highly automated, scalable, and increasingly compatible laboratory facilities, guided by AI-based experiment control systems, and supplemented by AI-driven data mining and analytics capabilities. Such “next gen” lab facilities are often available remotely, making preclinical experimentation more accessible to various players in a wider scope of geographies.
In this post, let’s review several such companies, which offer various options, including “plug-and-play” experimentation services to drug discovery and biotech organizations.
Lila Sciences
Launched by Flagship Pioneering in March 2025 with $200 million in seed financing, Lila Sciences is developing what it calls the world’s first scientific superintelligence platform—a fusion of AI, robotics, and autonomous laboratory systems designed to accelerate discovery across biology, chemistry, and materials science.
Lila’s AI Science Factories are robotic, closed-loop laboratories guided by specialized AI models to plan and conduct experiments, generating proprietary datasets across life sciences, chemistry, and materials.
These automated labs enable continuous, instrument-agnostic experimentation under AI control, with the platform intended to couple reasoning models to physical lab systems for end-to-end execution and learning.
The company’s Autonomous Science Platform combines generative AI with scalable robotic lab units capable of designing, executing, and optimizing experiments across multiple domains, reportedly surpassing existing benchmarks in drug design, catalyst development, and materials engineering.
In September 2025, Lila announced a $235M Series A to expand its autonomous research labs. A month later, Lila Sciences has raised an additional $115 million from investors including Nvidia’s venture arm, bringing its valuation to over $1.3 billion and total funding to $550 million; the company plans to expand its AI Science Factories and open its automated research platform to commercial partners across sectors such as energy, semiconductors, and drug development.
Recently, Lila Sciences has partnered with IQHQ to establish a 244,000-square-foot facility at the developer’s Alewife Park life sciences district in Cambridge, Massachusetts. The long-term lease will provide Lila with both move-in-ready and purpose-built laboratory space to house its expanding network of AI-driven autonomous research labs and support the development of its scientific superintelligence platform.
Potato
Seattle startup Potato targets the orchestration layer for automated drug discovery—turning hypotheses and literature into structured protocols and robot scripts, and pairing that with analysis and data outputs.
Their cloud-based “Scientific OS” is designed to do just that. The platform reportedly connects to hundreds of scientific tools, allowing users or autonomous agents to design, execute, and analyze experiments at scale. Its multi-agent system, TATER (Technical AI for Theoretical & Experimental Research), can generate protocols from literature, run computational and wet-lab experiments, and translate research goals into executable robotic scripts.
Potato has raised $4.5 million in seed funding to advance its AI-driven platform for automating laboratory research through integrated robotic systems. The company is building a closed-loop environment where AI agents design, execute, and analyze experiments across connected lab hardware, moving toward fully autonomous lab operation.
The platform is supported by partnerships with Wiley for peer-reviewed protocol integration and Ginkgo Automation for physical lab automation.
Medra
San Francisco–based Medra positions its platform as an on-prem, instrument-agnostic automation layer that can operate general-purpose robots to interact with devices via software and physical controls (e.g., reading screens, pressing buttons), while logging video/metadata for each action. The company claims the platform can automate “up to 70%” of instruments already in labs and uses the resulting data to optimize subsequent runs.
Their recently launched Continuous Science Platform, an AI-driven system that merges robotics and proprietary machine learning models to enable continuous, autonomous scientific experimentation, is designed to address data scarcity in experimental research by creating a self-improving feedback loop between physical automation and AI reasoning, generating structured experimental datasets to accelerate discovery cycles.
At its core, the platform combines Physical AI and Scientific AI; Physical AI automates up to 70% of standard laboratory instruments through general-purpose robots equipped with visual and language understanding, while Scientific AI operates on the Infra-data layer, integrating information from electronic lab notebooks and scientific literature.
Medra reports that the system is already deployed in collaborations with major biotech and pharma companies. In a study with Lila Sciences, the platform fully automated protein characterization workflows on the LabChip GX Touch.
CurifyLabs
Helsinki-based CurifyLabs builds an automated pharmaceutical compounding platform for hospital and community pharmacies.
Compounding System Solution is an end-to-end pharmaceutical compounding platform that replaces manual preparation with a fully integrated system combining hardware, GMP-grade excipient bases, and intelligent software. At its core is the 3D Pharma Printer 1, a CE-marked desktop unit capable of producing up to 1,000 precisely dosed tablets, capsules, suppositories, or liquids per hour, with in-process quality control ensuring mass uniformity and regulatory compliance.
The system uses validated excipient bases compatible with a wide range of active pharmaceutical ingredients (APIs), including raw materials and crushed tablets, to support personalized formulations for diverse patient needs. A cloud-based formulation library provides preprogrammed blueprints, automated batch records, and validated quality dossiers.
This spring, the company announced €6.7M in funding, and now the launch of CurifyLabs Create.
The software platform enhances its 3D printing-based compounding system. It enables pharmacists to design and produce customized medications directly from APIs or crushed tablets, supporting multiple dosage forms through the company’s expanded CURABLEND excipient bases. Integrated with FDA- and GMP-aligned quality controls, CurifyLabs Create streamlines personalized drug formulation, making compounding reportedly up to four times faster than manual preparation.
Culture Biosciences
Founded in 2016, San-Francisco-based Culture Biosciences is a company that is involved in scaling up and optimizing bioreactor experiments which accounts for a substantial overhead for biopharma companies. Culture Biosciences has designed a set of bioreactors specifically suitable for fine-tuning the processes and remote real-time monitoring. Along with a wide range of strain screening and process development capabilities, this would allow a quick transfer from a lab-scale to commercial production both for small biotech companies as well as larger pharmaceutical organizations.
Last disclosed round is $80M Series B in 2021 led by Northpond Ventures.
In 2023, Culture Biosciences announced a shift of its focus towards upstream bioprocess development of new therapeutics in automated drug discovery. The company has appointed a new leadership team with expertise in the biotech and biopharma industries to enhance its capabilities.
Since then, the company has introduced a three-part stack: Nexxys services—now “home to 300+ bioreactors”—for managed upstream runs; Console, a cloud platform for experiment design/monitoring; and Stratyx 250, a cloud-integrated, mobile 250 mL bioreactor launched on April 1, 2025.
Notably, Culture Biosciences has partnered with Google Cloud last year to integrate its cloud-connected bioreactors with Gemini AI and Google’s data tools, aiming to accelerate bioprocess development through automated experiment design, real-time monitoring, and advanced analytics. The collaboration will build a scalable cloud architecture combining Culture’s Console platform with BigQuery and Looker to enable data-driven optimization and visualization of bioprocess workflows.
In parallel, Cytiva and Culture Biosciences have expanded their partnership to include global commercialization and joint development of cloud-connected bioreactor technologies. Under the agreement, Cytiva will exclusively distribute Culture’s Stratyx 250 bioreactor platform and co-develop new formats incorporating Cytiva’s Xcellerex X-Platform, creating an integrated, scalable solution from 250 mL to 2,000 liters for digital bioprocessing and biomanufacturing.
Synthace
In the growing field of automated drug discovery, a user-friendly solution is needed to integrate multiple robotic devices, data collection, and visualization routines from various vendors.
UK-based Synthace, founded in 2011, tackles this challenge with its cloud-based "no-code" Synthace Life Sciences R&D Cloud for experiment automation. This software is suitable for simple sample liquid handling and complex multi-step protocols, using a graphical interface without requiring coding skills.
The device-agnostic protocol builder enables users to create sophisticated experiment routines and transfer them between multiple devices. The in silico simulation feature helps identify potential errors in future workflows before actual experiment runs.
SPT Labtech and Synthace have collaborated to pair Synthace’s DOE-driven experiment platform with the Dragonfly Discovery dispenser for assay optimization. A supporting case study documents a 3,456-run space-filling DOE campaign with automated data structuring.
In another case study, Oxford Biomedica achieved an 83% time reduction in experimental design and execution time and a tenfold increase in vector titre by using Synthace’s automated Design of Experiments (DOE) platform to optimize transfection and transduction processes.
The platform remains device-agnostic, with published integrations across vendors including SPT Labtech, Tecan, and Hamilton.
Synthace has raised $81 million from various investors, with the latest round being a $35M Series C in 2021 co-led by Horizons Ventures and Sofinnova Partners.
Strateos
Strateos, a California-based company, formerly known as Transcriptic and founded in 2012, provides cloud-based automation for daily routine operations of synthetic biology, medicinal chemistry, and a closed cycle of design-synthesis-testing for potential drug candidates.
Having acquired 3Scan, it has expanded its spectrum of operations by robotic tissue slicing and analysis using computer vision technologies. In 2020 they started a collaboration with Eli Lilly where they are using Strateos Robotic Cloud platform at the client’s facilities to increase biology capabilities and implement an automated chemical synthesis loop. Later, Eli Lilly has sold its San Diego-based Life Sciences Studio robotic lab to Oxford- and Boston-headquartered contract research organization Arctoris, transferring the automated research platform to the UK
Strateos has raised a total funding of around $90M with the latest reported funding round being a $56M Series B round in 2021, led by DCVC and Lux Capital.
Recently, Strateos announced it is pivoting its focus to meet the rising demand for on-site, fully automated cloud labs for life science research and automated drug discovery. The company's LodeStar software platform allows companies to manage their on-premises research operations and instruments, enabling remote control, automated drug discovery data generation, and data analytics.
Strateos also reportedly completed multimillion-dollar design programs with two undisclosed top biopharmaceutical companies to accelerate their digital transformation. The strategic shift also involves a reorganization of the company's staffing structure and the establishment of a Project Management Office. This move aims to support the growing demand for laboratory modernization and automated drug discovery solutions.
Emerald Cloud Lab
Another US-based company, the Emerald Cloud Lab founded in 2010, takes a different approach and instead of utilizing a set of predefined workflows provides a broad range of scientific instrumentations and therefore the ability to design fully customizable life science experiments. They are constantly adding new types of operations and machinery offering a wide and flexible set of services to their clients.
The company raised a total of $92.1 million from a number of investors, including Schooner Capital, Founders Fund, and Alumni Ventures.
The company is also active in democratizing academic research. For instance, Carnegie Mellon University has opened the CMU Cloud Lab in Pittsburgh, the first academic remote-controlled laboratory in the U.S. The facility, enabled by Emerald Cloud Labs, will provide students and faculty with remote access to over 200 lab instruments. Researchers can design AI-assisted experiments from any location, while technicians and robots carry out the work on-site.
The Cloud Lab is meant to democratize academic science by allowing researchers from under-resourced institutions to conduct complex experiments with just an internet connection. Centralizing experiments in the cloud lab streamlines the process, reducing costs and the likelihood of errors during experiment replication. Carnegie Mellon is the first academic institution to implement cloud lab technology in collaboration with Emerald Cloud Labs.
