World’s First Fully Automated Medicine Lab: All Robots No Humans

Japan has unveiled a world-first fully automated medicine laboratory, operating entirely without human researchers. Located at the Institute of Science Tokyo’s Yushima campus, this facility is staffed exclusively by humanoid robots and autonomous machines, marking a radical shift in medical research.

The facility developed by the Institute of Science Tokyo operates with 10 robots, including the humanoid Maholo LabDroid, and no on-site human staff.

Key Highlights

• Location: Yushima campus, Institute of Science Tokyo
• Robots in operation: 10 autonomous machines, including Maholo LabDroid with dual robotic arms
• Functions: Automated cell culture, reagent transfer, temperature-controlled experiments, and repetitive lab tasks
• Expansion goal: 2,000 robots by 2040, aiming to automate the entire medical research pipeline
• No human staff: Operates 24/7 without on-site researchers

Inside the Robot-Run Lab

• Maholo LabDroid: A humanoid robot capable of performing delicate tasks such as stem cell culture and drug testing.
• Other autonomous systems: Handle repetitive lab work with precision, ensuring consistency across experiments.
• Current scale: 10 robots are already in operation.
• Future vision: Expansion to 2,000 robots by 2040, creating a fully autonomous medical research ecosystem.

Maholo LabDroid was among the earliest humanoid robots specifically built for biomedical research. It was first unveiled in 2017 by Japan’s Robotic Biology Institute (RBI), marking the debut of a humanoid laboratory robot designed to automate complex

biological experiments.  

Why It Matters

• 24/7 operation: Robots can run experiments continuously, accelerating discovery.
• Error reduction: Automation minimizes human mistakes in sensitive procedures.
• Drug development speed: Timelines for testing and discovery are compressed significantly.
• AI integration: Plans include combining robotics with AI for hypothesis generation and experimental validation.

Human vs. Robot Labs

Feature	Human-Staffed Labs	Fully Automated Robot Lab
Staffing	Scientists, technicians	10 robots (scaling to 2,000)
Operation Hours	Limited (8–12 hrs/day)	24/7 continuous
Error Risk	Human error possible	Minimized via automation
Scalability	Workforce-dependent	Expandable via robotics
Research Speed	Weeks/months	Compressed timelines

Challenges Ahead

• Ethical oversight: With no humans on site, accountability for results becomes complex.
• Technical reliability: Robots must adapt to unexpected experimental variables.
• Cost barrier: High investment may limit adoption outside elite institutions.
• Human role shift: Researchers may transition into supervisory, AI-integration, and oversight positions.

This lab represents a paradigm shift in medical research, where robots not only assist but fully replace human presence. If successful, it could redefine the future of drug discovery and biomedical innovation worldwide.

Amazon's New AI Tool Turns Years of Drug Research Into Weeks

Amazon Web Services has launched Amazon Bio Discovery, an AI-powered platform that compresses early-stage drug discovery timelines from 18 months into just weeks, enabling rapid molecule design, antibody generation, and lab-in-the-loop testing.

Key Highlights

• Launch Date: April 2026
• Purpose: Accelerate early-stage drug discovery using AI-driven workflows
• Compression of Timelines: From 12–18 months to weeks
• No Coding Required: Scientists can run workflows without programming
• AI Foundation Models: Specialized biological models generate and evaluate molecules
• Lab-in-the-Loop: Candidates synthesized and tested with results fed back

Achievements & Adoption

Feature	Details
Antibody Generation	300,000 novel antibodies generated; 100,000 tested
Industry Adoption	Used by Bayer, Broad Institute, and 19 of top 20 pharma companies
Research Partners	Memorial Sloan Kettering reported sub-nanomolar affinity antibodies
Free Trial	AWS offers trial access for researchers

How It Works

AI agent helps scientists set up and run AI-powered drug discovery workflows.

• Model Selection: AI agents guide researchers
• Candidate Screening: Millions narrowed to 100–1,000 top candidates
• Optimization: AI refines therapeutic properties
• Integration: Results loop back into system knowledge

Risks & Considerations

• Validation Needed: Wet-lab testing required
• Regulatory Oversight: Compressed timelines challenge frameworks
• Data Dependence: Accuracy depends on training datasets
• Not a Replacement: Supports researchers, not replaces them

Impact for India & Global Pharma

With India’s growing biotech and pharma sector, platforms like Amazon Bio Discovery could accelerate vaccine and therapeutic development, especially in oncology, infectious diseases, and rare disorders.

Globally, this represents a paradigm shift in R&D efficiency, reducing costs and speeding access to life-saving drugs.

Emergence of AI based Drug Discovery

More than 200 AI-designed drugs are now in clinical development, with Phase I success rates of 80–90%, nearly double traditional averages. Big Pharma Adoption: In early 2026, Eli Lilly, GSK, and Pfizer signed major deals with AI drug discovery platforms, signaling mainstream adoption.

Several AI-driven platforms besides Amazon Bio Discovery are reshaping drug discovery in 2026, including Insilico Medicine, Recursion Pharmaceuticals, and Exscientia, all of which are delivering clinical candidates and accelerating pharma R&D.

IAN Alpha Fund Backs Peptris in ₹70 Crore Series A to Advance AI Drug Discovery

IAN Alpha Fund, the 2nd in the series of IAN Group’s VC funds, along with Speciale Invest, has co-led a ₹70 crore Series A funding round in Peptris, a Bengaluru-based AI-powered drug discovery company. The round also saw participation from Tenacity Ventures, BYT Ventures, and other investors. The investment strengthens Peptris’ proposition as one of the leading Indian AI drug discovery platforms progressing beyond molecule generation towards clinical-ready assets. It is an opportune time for such an investment, as companies like Peptris not only help drug companies globally reduce development costs amid high failure rates but also assist with life-cycle management of their patents.

Founded in 2019, Peptris has built an AI-led discovery engine that addresses one of the most persistent problems in healthcare: despite scientific advances, drug discovery remains slow, expensive, and failure-prone, leaving large unmet medical needs. This challenge is most acute in the pre-clinical stage, where time, capital, and scientific uncertainty often derail promising programs much before drugs reach the clinical stage.

Peptris’ differentiator is its proprietary AI models, which not only generate novel molecules but also help predict critical drug development parameters early. This enables faster and better-informed decision-making and has already resulted in the discovery of Novel Chemical Entities (NCEs) as well as drug repurposing and rescue opportunities, with multiple programs now advancing towards clinical development.

This fresh capital infusion will be used to advance its existing programs toward clinical readiness and to significantly expand its pipeline over the next 24 months. The company plans to initiate several new NCE programs, alongside multiple drug repurposing and rescue programs (shelved clinical stage drugs of other companies). The company will also expand teams across biology, chemistry, data science, and AI, while building dedicated business development capabilities in the US and Europe to deepen partnerships with global pharmaceutical and biotech companies.

Business Model: Peptris follows a B2B engagement model, working closely with pharma, biotech, and select FMCG partners to license assets and co-develop programs. Several of its discovery programs each address multi-billion-dollar global market opportunities, underlining the company’s value potential.

Leadership Team: Peptris is led by a founding team comprising Narayanan Venkatasubramanian (CEO), Shridhar Narayanan (CSO), Anand Budni (CTO), and Amit Mahajan (Chief Data Scientist), bringing together deep expertise across AI drug discovery and computational science. The team combines strong academic grounding with hands-on experience in building technology-led platforms and translating complex biological problems into data-driven solutions.

CEO Statement: Narayanan Venkatasubramanian, Co-founder & CEO, Peptris, said, “Our mission has been to harness AI to address meaningful healthcare challenges that enhance quality of life, not merely extend lifespan. The true reward lies in seeing this work translate into hope for patients, relief for caregivers, and a more accessible future for those with limited or unaffordable treatment options. Peptris is especially encouraged to have IAN Alpha Fund share this conviction as an early backer, reinforcing our commitment to building impactful, patient-centered innovation.”

Investor Statement: Rajnish Kapur, Managing Partner, IAN Alpha Fund, said, “Drug discovery is becoming economically unsustainable, with expenses nearly doubling every nine years and pre-clinical development swallowing disproportionate time and capital. The use of AI for molecule discovery, which re-engineers the economics of early drug discovery by enabling faster decisions, reducing costs, and significantly accelerating go-to-market timelines, really excited us. This is driven by a team with scientific depth and computational rigor. This innovation could absolutely help change the drug discovery ecosystem not only in India but globally. At its core, this investment is about serving humanity better.”

Therapeutic Focus: Peptris focuses on therapeutic areas including rare diseases, inflammation, oncology, and women’s health to deliver a durable impact for patients, caregivers, and healthcare systems globally.

About Peptris

Peptris is an AI-powered pre-clinical drug discovery company based in Bengaluru. Peptris has created AI models to generate novel molecules and predict varied parameters that are critical to reduce failures in drug development. The approach has led to the discovery of Novel Chemical Entities (NCE), drug repurposing and rescue opportunities. With novel solutions and a healthy pipeline of molecules, Peptris is one of the few nimble AI drug discovery companies to be entering clinical development and has successfully licensed its first program in Duchenne Muscular Dystrophy.

About IAN Alpha Fund

IAN Alpha Fund, a $100 Mn SEBI-registered Category II AIF VC Fund, is the 2nd fund in IAN Group’s series of funds. The Fund explores opportunities in diverse sectors such as healthtech, cleantech, deep tech, agritech, medtech, hardware and electronics, manufacturing, Web 3.0, Metaverse, Industry 4.0, SaaS, and other sectors where innovation is transformational. The Fund invests in innovative startups solving real problems for India and the world, with sustainable business models enabling scale by leveraging technology.

About IAN Group

IAN Group is India’s largest horizontal platform for early-stage investments, comprising the IAN Angel Fund, BioAngels, and a series of SEBI-registered Venture Capital Funds, the latest being a US$100mn VC Fund, IAN Alpha Fund. IAN enables entrepreneurs to raise from Rs. 50 lakhs to Rs. 50 crores, supported by high-quality mentoring by successful entrepreneurs, enabling access to global markets. Forbes has recognised IAN as one of the most iconic business and economic developments of Independent India over the last 75 years, alongside institutions such as LIC, NASSCOM, the RBI, and Naukri.com.

Google DeepMind Unveils AlphaGenome: AI Tool to Decode DNA Mutations and Advance Precision Medicine

Google DeepMind has unveiled AlphaGenome, a breakthrough AI tool designed to predict the molecular impact of DNA mutations and accelerate biological discoveries. Published in Nature on January 28, 2026, AlphaGenome uses deep learning to analyze complex genetic sequences, helping scientists identify disease-causing mutations and understand how genetic changes influence health.

What AlphaGenome Does

• Interpret DNA mutations by analyzing millions of genetic letters and predicting their molecular consequences.
• Identify disease-causing genes, including those linked to cancer, heart disease, autoimmune disorders, and mental health conditions.
• Accelerate drug discovery by highlighting potential therapeutic targets.
• Enable faster biological research by reducing the time needed to analyze genetic data compared to traditional methods.

Why It Matters

• Healthcare Impact: AlphaGenome could transform precision medicine by helping clinicians understand which mutations are benign and which are pathogenic.
• Drug Development: By predicting how mutations affect proteins and cellular processes, it provides insights for pharmaceutical companies to design targeted therapies.
• Scientific Discovery: It offers researchers a powerful tool to decode the genome and uncover new biological mechanisms.

Comparison with Other AI in Genomics

Tool	Developer	Focus	Key Advantage
AlphaFold	DeepMind	Protein structure prediction	Revolutionized structural biology
AlphaGenome	DeepMind	DNA mutation impact	Predicts functional consequences of genetic variants
Enformer	Google Research	Gene expression prediction	Specialized in regulatory DNA regions
EVE (Evolutionary Model)	Harvard/Google	Variant pathogenicity	Uses evolutionary data for predictions

Risks & Challenges

• Clinical Validation: Predictions must be rigorously tested before being applied in patient care.
• Data Bias: AI models depend on training data; incomplete or biased datasets could affect accuracy.
• Ethical Concerns: Widespread use raises questions about genetic privacy and responsible application in healthcare.

Looking Ahead

AlphaGenome is available via API, allowing researchers worldwide to integrate it into their workflows. Its release signals a new era where AI becomes central to genomics and precision medicine, potentially reshaping how we diagnose, treat, and prevent genetic diseases.

Alphabet Spinout SandboxAQ, Backed by Nvidia, Unveils Synthetic Molecule Megaset to Revolutionize Drug Discovery

SandboxAQ, an AI startup spun out of Google parent Alphabet and backed by Nvidia, has announced that it has released a massive dataset of 5.2 million synthetic 3D molecules to accelerate drug discovery. These molecules don’t exist in nature—they were generated using Nvidia’s chips and grounded in real-world experimental data to simulate how drugs bind to proteins, a critical step in developing effective treatments.

This synthetic dataset, called the Structurally Augmented IC50 Repository (SAIR), is publicly available and designed to train AI models that can predict drug-protein interactions far faster than traditional lab methods. The goal? To virtually replicate lab results with high accuracy, potentially compressing months of research into a single AI-driven prediction.

It’s a bold move that blends physics-based modeling with machine learning—an approach that could reshape how we think about early-stage pharmaceutical R&D. And with SandboxAQ planning to monetize its own trained models, it’s also a glimpse into the future of AI-powered biotech platforms.

While the dataset is public, SandboxAQ plans to monetize its proprietary AI models trained on it—essentially offering virtual labs as a service.

Headquartered in Palo Alto, California, right in the heart of Silicon Valley, SandboxAQ is a fascinating fusion of quantum tech and AI muscle. It emerged from Alphabet’s moonshot factory and is backed by Nvidia, with nearly $1 billion in venture capital fueling its ambitions.

SandboxAQ's signature tech is Large Quantitative Models, the AI systems grounded in the laws of physics, chemistry, and biology. These aren’t just data-driven models; they simulate the real world with scientific rigor. Think of it as a tireless digital researcher. This tool autonomously explores millions of chemical pathways, helping discover novel molecules far beyond human capacity.

Beyond drug discovery, SandboxAQ is tackling – 1) Cybersecurity: Detecting cryptographic vulnerabilities; 2) Navigation: Enhancing precision in GPS-denied environments. 3) Medical Diagnostics: Using AI to analyze cardiac signals. 4)Materials Science: Predicting atomic-level properties for breakthrough materials.

SandboxAQ was founded by Jack Hidary, a tech entrepreneur with a deep background in quantum computing and neuroscience. He previously led a quantum initiative at Alphabet before spinning it out as SandboxAQ in 2022. Hidary studied philosophy and neuroscience at Columbia University and has authored a well-regarded book on quantum computing.

While Hidary is the primary founder and CEO, the company’s early development was also shaped by influential figures like Eric Schmidt, former CEO of Google, who serves as Chairman of the Board.

Accenture Invests in Sam Altman and Y Combinator Backed BioTech Startup 1910 Genetics

Accenture has invested in 1910 Genetics, a biotechnology startup backed by OpenAI CEO Sam Altman and Y Combinator. This investment aims to revolutionize drug discovery using Al-driven solutions.

Terms of the investment were not disclosed.

For an Al-driven drug discovery, 1910 Genetics uses its proprietory multimodal Al platform called Input-Transform-Output (ITOTM) to enhance drug discovery for both small and large molecule therapies. This platform integrates hundreds of Al models and employs federated learning to optimize molecule design and target identification.

The Accenture–1910 collaboration aims to provide biopharma clients with a powerful Al platform that streamlines the drug discovery process, reduces costs, and improves patient outcomes.

Launched in 2021, the biotech startup, 1910 Genetics, is backed by leading investors including M12 - Microsoft’s Venture Fund, Playground Global, Sam Altman, Y Combinator, FoundersX Ventures, and Scientia Ventures.

The biotech company recently launched CANDID-CNS™, an AI model for predicting blood-brain barrier permeability, which outperforms the industry standard[

The goal of the investment by Accenture is to collaborate with 1910 Genetics wherein Accenture brings its AI and enterprise scalability expertise to the table, while the biotech startup contributes its comprehensive AI platform. Together, they aim to drive innovation in drug discovery and therapeutic development.

Key figures from both companies will join advisory boards, and 1910 Genetics will join Accenture Ventures’ Project Spotlight for emerging technologies. Dr. Petra Jantzer and Tom Lounibos will join the Business Advisory Board of 1910, and Dr. Kailash Swarna, a managing director in Life Sciences at Accenture, and Dr. Cecil Lynch, Biomedical Informatics lead at Accenture, will join the Technology Advisory Board of 1910.

This partnership is expected to significantly impact the biopharma industry by enhancing efficiency and accelerating the development of new therapies.

The global drug discovery market is projected to reach USD 76.5 billion by 2033, growing at a compound annual growth rate (CAGR) of 14.5% from 2023 to 2033. North America currently dominates the market, but Asia-Pacific is expected to see significant growth due to increasing investments in R&D and the expansion of the pharmaceutical industry.

It may be recalled that, in middle of this year, a CNBC report said that the AI-powered healthcare field is on a path that will see medicines completely generated by Artificial Intelligence (AI) in the near future.

In addition to 1910 Genetics, Accenture has made several other strategic investments and collaborations in the drug discovery space. In August, Accenture announced it has invested in Ocean Genomics to accelerate Al-driven drug discovery and the development of personalized medicines. Ocean Genomics uses advanced computational platforms to assist biopharma companies in discovering and developing more effective diagnostics and Therapeutics.

Accenture has partnered with Exscientia, a leading AI-driven drug discovery company, to leverage its AI platform for accelerating the discovery of new therapeutic compounds. Further, Accenture also collaborated with Schrödinger to enhance its computational platform for drug discovery. Schrödinger's platform integrates physics-based computational software to design novel molecules with improved accuracy.

Accenture Invests in Turbine, That is Building a Platform for Interpreting Human Biology Using AI

Accenture has made a strategic investment through Accenture Ventures in Turbine, a company that specializes in predictive simulation. This investment is aimed at accelerating the use of AI-powered cell simulations in biopharma research and development.

Turbine's core technology, the Simulated Cell™ platform, leverages machine learning to model molecular interactions within and around cells. This allows for virtual experiments that can reveal mechanisms driving diseases and responses to therapies. The platform is designed to perform virtual experiments at a scale nearly impossible in the physical world, thus providing valuable insights into biological systems.

This collaboration through the investment is expected to help global biopharma companies uncover hidden biological insights, which could guide and accelerate key drug development workstreams. It's particularly focused on uncovering promising drug targets, selecting patient populations most likely to benefit from therapy, and identifying combination therapy regimens.

The investment reflects Accenture's commitment to supporting technology and digital capabilities that drive innovation in AI-based drug discovery, with the ultimate goal of enhancing patient care and providing more effective treatments.

Turbine is the latest company to join Accenture Ventures’ Project Spotlight, an engagement and investment program focused on working with companies that create or apply disruptive enterprise technologies. Project Spotlight offers extensive access to Accenture’s domain expertise and its enterprise clients, helping startups harness creativity and deliver on the promise of their technology.

Earlier in March this year, Accenture, through Accenture Ventures, had invested in Sanctuary AI, a company specializing in the development of AI-powered humanoid robots.

Besides Turbine, other digital simulation companies in Project Spotlight include QuantHealth, Virtonomy and Ocean Genomics.

“Since most unsolved complex diseases are heterogenous, progress in utilizing AI for drug discovery is hindered by ethical and technological limitations in sourcing deep and diverse ground truth data. To bridge this gap, we need a toolkit that can learn fundamental rules of biology on in vitro experiments and apply to patients that it has never seen before. Turbine's Simulated Cells can be engineered at scale to represent the heterogeneity of complex human disease better than currently available wet lab experimental models, which are inherently biased towards representing certain disease types and hardly scalable,” said Szabolcs Nagy, co-founder and CEO of Turbine.

“By tapping into Accenture’s expertise, we hope to expand our market reach and augment our simulation platform in order to benefit the whole biopharma industry by ensuring that the next experiment is always the correct one", Nagy added.

Founded in 2015 by Kristof Szalay, Ph.D., Daniel Veres, M.D., Ph.D., Szabolcs Nagy and Ivan Fekete, M.D., Turbine is based in London, UK, with offices in Budapest, Hungary and Cambridge, UK. The team’s vision is to overcome the current limitations in identifying oncology treatments with true patient benefit by combining molecular biology and interpretable machine learning.

Turbine’s latest investment round (Series A) was closed in June 2023 which was €5.5 million from MassMutual Ventures. Prior to this, in 2022, Turbine raised finding which was co-led by MSD (Merck & Co., Inc., Rahway NJ, USA) Global Health Innovation Fund (GHIF) and Mercia Asset Management, who were joined by both new and existing investors Delin Ventures, Day One Capital, Accel, Atlantic Labs, XTX Ventures, o2h Ventures and Boston Millenia Partners.

Future Medicines To Be Completely Designed by AI

Generative Al will be designing new drugs all on its own in the near future

Generative AI is making significant strides in the field of drug discovery. It's being used to design new drugs by analyzing vast datasets and generating novel molecular structures that could potentially be strong drug candidates. For instance, scientists at pharmaceutical giant, Eli Lilly, have been surprised by the unique molecules that AI
has produced, which could not have been envisioned by human researchers.

Citing executives working at the industry of Al & healthcare cross-over, a CNBC report said that the AI-powered healthcare field is on a path that will see medicines completely generated by Artificial Intelligence (AI) in the near future.

Moreover, AI is expected to not only conceive new drugs but also create ones that humans might not be able to, thus expanding the horizons of medical science. The technology is advancing rapidly, and experts believe that within a few years, it will become a norm in drug discovery. This could significantly reduce the time and cost associated with developing new medications, leading to faster and more efficient healthcare solutions.

According to some, within a few years at most it will become a norm in drug discovery. Experts at Eli Lilly and NVIDIA say that within a few years, Al will not only think up new drugs, but ones that humans could not create.

Generative Al is rapidly accelerating its applicability to the developments and discovery of new medications, in a move that will reshape not only the pharmaceutical industry but ground-level ideas that have been built into the scientific method for centuries.

AlphaFold

A major precedent for AI-generated breakthroughs in biology was set in 2021 when Google’s DeepMind AI, came up with a novel protein called AlphaFold.

Google DeepMind and EMBL-EBI (European Bioinformatics Institute) have partnered to create the AlphaFold Protein Structure Database. This database provides open access to over 200 million protein structure predictions generated by AlphaFold.

Exscientia

Exscientia is a leading pharmatech company that uses Al to design and optimize molecular properties of drugs for patients, revolutionizing drug discovery. Exscientia combines advanced Al design with exacting experimental validation to rapidly progress a pipeline of drug discovery assets. The pharmatech company is spearheading the drug discovery revolution by creating the first Al-designed molecules to reach clinical trials.

Similarly, Insilico Medicine, a company with headquarters in Hong Kong and New York, has used AI to develop an experimental drug for idiopathic pulmonary fibrosis, an incurable lung disease. The treatment is in mid-stage trials in the US and China with some results expected early 2025.

However, the success of any AI-designed drugs will ultimately be tested by the traditional final step in drug development —performance in human trials.

Nvidia Unveils BioNeMo, A GenAI Platform Designed for Drug Discovery and Healthcare Innovations

NVIDIA has recently unveiled — BioNeMo, a generative AI platform designed for drug discovery. It offers a range of tools to simplify and accelerate the training of models using your own data, and it facilitates the scaling of model deployment for drug discovery applications. The platform provides state-of-the-art biomolecular models for tasks such as 3D protein structure prediction, de novo protein and small molecule generation, property predictions, and molecular docking.

NVIDIA says that its latest BioNeMo foundation models can analyze DNA sequences, predict how proteins will change shape in response to a drug molecule, and determine a cell’s function based on its RNA.

BioNeMo offers pretrained models, such as MegaMolBART and MoFlow for chemistry, and ESM-1 and ESM-2 for protein embeddings. BioNeMo also allows researchers to customize and deploy generative and predictive biomolecular AI models at scale.

This generative AI platform also simplifies and speeds up training models using your own data. For example, generative AI can analyze and visualize large amounts of data to identify emerging trends, key players, and potential opportunities. This can help R&D teams and innovation-driven companies gain insights that can inform research priorities and investment strategies.

BioNeMo also features a turnkey solution accessible through a web interface or cloud APIs, allowing users to access pretrained models for interactive inference, visualization, and experimentation. It's designed to provide seamless and scalable AI services, with the flexibility to experiment and build enterprise-grade generative AI workflows.

For those interested in the technical development and deployment of AI models, BioNeMo offers training services optimized for drug discovery, with support for various models, data loaders, training workflows, and validation processes. The platform can be accessed through the NVIDIA Base Command Platform for NVIDIA DGX Cloud.

 

Resources for BioNeMo Framework

Additionally, BioNeMo's ecosystem includes documentation, tutorials, and community support to help users explore its capabilities further.

NVIDIA's BioNeMo platform is at the forefront of a collaboration with Cognizant to enhance drug discovery for pharmaceutical clients. This partnership leverages generative AI to tackle complex challenges in the life sciences industry, aiming to improve productivity and speed up the development of new treatments.

Cognizant brings its AI and industry expertise to the table, utilizing NVIDIA's pretrained, industry-specific generative AI models to provide clients with a suite of model-making services. These services are designed to reduce manual intervention for data analysis and eliminate the need for extensive coding and infrastructure maintenance.

Furthermore, NVIDIA and Microsoft have expanded their collaboration in healthcare innovation, which includes the application of generative AI technology with the NVIDIA BioNeMo platform to address drug discovery challenges. This collaboration seeks to enhance productivity in development processes and hasten the delivery of new treatments to the market.

Cognizant also plans to establish an NVIDIA AI Center of Excellence this year to continue innovating with NVIDIA technologies across various domains, including manufacturing and automotive engineering. This center will focus on leveraging generative AI to bolster productivity, streamline costs, and expedite innovation.

Besides, BioNeMo models will soon be accessible on AWS HealthOmics, a purpose-built service that helps healthcare and life sciences organizations store, query and analyze biological data including DNA and RNA.

NVIDIA says that Cadence and Iambic Therapeutics are among more than 100 companies adopting NVIDIA AI to propel computer-aided drug discovery and generative AI.

Biotech Pioneer Genentech and NVIDIA Join Hands for Drug Discovery Using Generative AI

• Genentech and NVIDIA Enter Into Strategic AI Research Collaboration to Accelerate Drug Discovery and Development
• Collaboration leverages expertise in artificial intelligence from Genentech and NVIDIA to unlock scientific innovation and empower R&D at a scale previously unattainable in pursuit of novel therapies.
• Multi-year research effort aims to optimize and accelerate each company’s platforms and create innovations that could be applied in healthcare and beyond

Genentech, a member of the Roche Group (SIX: RO, ROG; OTCQX: RHHBY), on Tuesday, announced a multi-year strategic research collaboration with NVIDIA that couples Genentech’s artificial intelligence (AI) capabilities, extensive biological and molecular datasets, and research expertise with NVIDIA’s world-leading accelerated computing capabilities and AI to speed up drug discovery and development. The collaboration is designed to significantly enhance Genentech’s advanced AI research programs by transforming its generative AI models and algorithms into a next-generation AI platform, expediting the discovery and delivery of novel therapies and medicines to people.

The companies will join forces to accelerate and optimize Genentech’s proprietary machine learning (ML) algorithms and models on NVIDIA DGX Cloud, which provides a training-as-a-service platform built on dedicated NVIDIA AI supercomputing and software, including NVIDIA BioNemo for generative AI applications in drug discovery.

NVIDIA will share its computing expertise with Genentech’s teams of computational scientists with the goal of optimizing and scaling Genentech’s models, and in that process, may improve or enhance NVIDIA’s platforms.

“By harnessing the power of AI models and algorithms, with our unique data and experiments, we're unlocking scientific discoveries with incredible speed and generating insights at an unprecedented scale,” said Aviv Regev, EVP and head of Genentech Research and Early Development (gRED). “Bringing science and technology together has always been a foundation of biomedical breakthroughs at Genentech. We are thrilled to join forces with NVIDIA to further optimize our drug discovery and development to deliver treatments that transform people’s lives.”

“The greatest impact of generative AI is to revolutionize the life science and healthcare industry,” said Jensen Huang, founder and CEO of NVIDIA. “Our collaboration to create Genentech’s next-generation AI platform will dramatically accelerate the pace of drug discovery and development.”

The collaboration with NVIDIA complements Genentech’s AI/ML teams, which are developing and leveraging AI and ML foundational models across numerous research areas including diverse therapeutic modalities. Genentech scientists aim to glean new insights for target and drug discovery and to answer fundamental questions about human biology and disease.

The collaboration will also help accelerate Genentech’s “lab in a loop”, where extensive experimental data feeds computational models that uncover patterns and make new, experimentally testable predictions. Scientists quickly assess these predictions in the lab and the results are fed back into the models to improve the underlying computational model, allowing for iterative development of better therapies.

Activities will leverage publicly available and Genentech-proprietary data. Genentech will control the sharing of its proprietary data and NVIDIA will not have direct access to Genentech’s proprietary data unless granted by Genentech for use in a particular project during the term of that project.

Drug discovery and development is currently a lengthy, complicated, and uncertain process. Targets for novel medicines are difficult to predict, as is successfully developing a molecule as a potential therapeutic. Genentech believes that AI plays an invaluable role in revolutionizing drug development, in combination with people, science and technology, helping make it more predictable and cost-effective, boosting the success rate of R&D over the long term, and ultimately helping discover and design therapeutics to improve people’s lives.

About Genentech

Founded more than 45 years ago, Genentech is a leading biotechnology company that discovers, develops, manufactures and commercializes medicines to treat patients with serious and life-threatening medical conditions. The company, a member of the Roche Group, has headquarters in South San Francisco, California. For additional information about the company, please visit http://www.gene.com.