[Revised January 23, 2026]

Dario Amodei, Demis Hassabis & Jensen Huang: Compressing a Century of Biology into a Decade

Major advances at the intersection of artificial intelligence and the life sciences have prompted insightful commentary from leaders and researchers at top AI companies. This report compiles scientific and technical opinions from key figures at OpenAI, Nvidia, Google DeepMind, Anthropic, and others, focusing on the past year’s developments. We highlight how AI is impacting drug discovery, genomics, protein structure prediction, diagnostics, personalized medicine, and more. The emphasis is on substantive insights (from papers, keynotes, blogs, and interviews) rather than marketing hype, to inform an audience of life science PhDs about the latest trends and technologies (e.g. AlphaFold, large language models applied to biology).

Executive Summary

AI’s Growing Role in Life Sciences: Leaders in AI research agree that recent AI breakthroughs are transforming biology and medicine. Notably, DeepMind’s AlphaFold solved the 50-year protein folding challenge, earning Demis Hassabis and John Jumper the 2024 Nobel Prize in Chemistry ^[1]. Such computational tools have “transformed biology” and could “revolutionize drug discovery” ^[2]. In drug development, AI-driven platforms are shortening discovery timelines and suggesting new therapeutic candidates. In healthcare, large AI models are approaching expert-level performance in diagnosis and genomics. Crucially, these AI systems are seen as augmenting scientists and clinicians – offloading data-crunching “legwork” while empowering human experts to ask the right questions ^[3].

Key Trends (2024–2026):

• Generative AI for Biology: AI leaders describe a "generative revolution" in life sciences, as models learn the patterns of biological data (genes, proteins, etc.) and even generate novel biological designs ^[4]. Nvidia's BioNeMo platform has expanded significantly, now hosting over 25 foundation models including the groundbreaking Evo 2 genome model—the largest publicly accessible AI model for biology, trained on 9.3 trillion nucleotides from 128,000 species ^[5]. The ability to "digitize" biology means if a phenomenon can be turned into data (DNA sequences, protein structures, cellular images), AI can learn from it and create new hypotheses or molecules. By January 2026, Eli Lilly and NVIDIA announced a $1 billion co-innovation lab to accelerate AI-driven drug discovery ^[6].

• Foundation Models in Healthcare: Large language models (LLMs) and multimodal models are being specialized for medical and biological tasks. OpenAI's ChatGPT Enterprise is now deployed across major pharmaceutical companies including Moderna, Pfizer, Merck, and Sanofi. Google DeepMind released MedGemma at I/O 2025—their most capable open model for multimodal medical text and image comprehension—and introduced upgraded AMIE (AI Medical Interview Engine) that now handles both conversations and visual medical data research.google. In November 2025, Google launched Gemini 3, billed as their most capable AI model yet, with enhanced healthcare tools including MedLM for X-ray analysis and new Medical Records APIs.

• Accelerating Drug Discovery: AI drug discovery investment rebounded sharply to $3.8 billion in 2024, with projections reaching $15.2 billion by 2030 ^[7]. Isomorphic Labs (Google DeepMind's drug discovery spin-off) secured a $600 million Series A in March 2025 and expanded its partnerships with Eli Lilly and Novartis to nearly $3 billion in total deal value. By late 2026, several AI-designed drug candidates are expected to enter Phase I clinical trials. Insilico Medicine now operates 30 drug programs with 10 compounds having received IND clearance, including Rentosertib showing favorable Phase IIa results for idiopathic pulmonary fibrosis.

• AI for Genomics and Personalized Medicine: AI models are improving our ability to interpret genomic data and personalize treatment. NVIDIA's Evo 2 achieves 90% accuracy in BRCA1 mutation predictions for cancer risk assessment ^[5]. New models announced in January 2026 include RNAPro for RNA structure prediction and ReaSyn v2 for ensuring AI-designed drugs are practical to synthesize. Google also released C2S-Scale, a 27 billion parameter foundation model for single-cell analysis in collaboration with Yale, which has already generated novel hypotheses about cancer cellular behavior. Leaders envision AI enabling "biological freedom" – where people control their health via AI-driven insights, curing diseases like cancer and Alzheimer's and extending healthy lifespan ^[8].

The table below summarizes key companies, their AI life-science projects, notable figures, and their perspectives:

AI Models and Specialization in Medicine. Google DeepMind's Med-Gemini models build on the general-purpose Gemini AI by adding medical-domain specialization (via additional self-supervised training on medical data, web-powered knowledge retrieval, fine-tuning with clinical datasets, and chain-of-reasoning prompts). This approach combines Gemini's inherited capabilities (advanced reasoning, multimodal understanding, long-context processing) with medical expertise to tackle complex tasks in healthcare research.google. At Google I/O 2025, the company announced MedGemma—based on Gemma 3—as their most capable open model for multimodal medical text and image comprehension, designed as a starting point for developers building health applications. In January 2026, Google released MedGemma 1.5 with enhanced medical image interpretation and MedASR for medical speech-to-text blog.google. The result is an AI ecosystem that can ingest diverse medical inputs (text, imaging, even genomics) and produce useful outputs like diagnostic reports or treatment recommendations with expert-level accuracy.

OpenAI: Large Language Models Accelerating Drug Discovery and Medical Research

OpenAI’s leadership views general-purpose AI models as powerful tools to boost productivity in the life sciences. In 2024, OpenAI made headlines by partnering with pharmaceutical companies to apply GPT-4’s capabilities to drug R&D. For example, Sanofi announced a collaboration with OpenAI and startup Formation Bio to develop AI-powered software for drug development, spanning tasks from target identification to clinical trial design ^{[14] [15]}. “This unique collaboration is the next significant step in our journey to becoming a pharmaceutical company powered by AI,” said Sanofi CEO Paul Hudson ^[16]. From OpenAI’s side, COO Brad Lightcap underscored the high expectations: “There is massive potential for AI to accelerate drug development. We are excited to collaborate…to help patients and their families by bringing new medicines to market.” ^[17] This statement reflects a common belief that large AI models (like GPT) can analyze pharma data in ways that uncover novel drug candidates or streamline trial processes.

OpenAI is also exploring how its models can assist regulatory science and biomedical knowledge synthesis. The FDA has shown increasing interest in AI-assisted drug evaluation, with the agency completing its first AI-assisted product review. "Why does it take over 10 years for a new drug to come to market? Why are we not modernized with AI?" asked FDA commissioner Dr. Marty Makary. OpenAI's collaborations aim to answer these questions by using GPT-based tools to sift through clinical trial data and scientific literature more efficiently, potentially cutting years off development timelines.

In October 2025, Thermo Fisher Scientific announced integration of OpenAI APIs into its Accelerator Drug Development platform and PPD clinical research business, aiming to shorten clinical trial timelines and identify lower-probability therapies earlier in development ^[18]. Lundbeck also began deploying ChatGPT across its global workforce to drive R&D and commercial innovation. In January 2026, OpenAI launched ChatGPT Health, a dedicated healthcare feature, highlighting growing competition with Google and Anthropic in the medical AI space.

Beyond these new partnerships, OpenAI's ChatGPT Enterprise continues to see broad uptake in biomedical settings. Moderna deploys it to thousands of employees, helping scientists quickly query datasets, summarize research, and brainstorm experimental designs ^[19]. Moderna's CEO Stéphane Bancel stated, "ChatGPT and what OpenAI is doing is going to change the world… we're looking at every business process — from legal, to research, to manufacturing — and thinking about how to redesign them with AI." Pfizer has developed marketing GPT tools, Merck uses its GPTeal platform, and Sanofi maintains an active R&D partnership with OpenAI. OpenAI is also working with Moderna to accelerate the development of personalized cancer vaccines.

Importantly, OpenAI’s Sam Altman emphasizes a long-term vision of AI tackling humanity’s hardest problems. In his 2024 essay “The Intelligence Age,” Altman describes a future where AI “helps us accomplish much more than we ever could without AI”, including better healthcare outcomes for all ^{[20] [21]}. He imagines each person having a personal AI expert in various domains, potentially offering personalized medical advice or tutoring researchers in new scientific techniques. While optimistic, Altman and OpenAI also recognize the need for responsible use – e.g. ensuring models are accurate and safe in medical recommendations. Nonetheless, the company’s engagement with life-science problems (from drug discovery partnerships to advising health agencies) shows its commitment to translating general AI advances into biological impact.

Google DeepMind: From AlphaFold to Gemini – AI Transforming Biology

Google DeepMind (now part of Alphabet’s Google DeepMind unit) has been at the forefront of applying AI to fundamental biology. CEO Demis Hassabis often cites AlphaFold as a prime example of AI’s potential to solve scientific grand challenges. “The reason I’ve worked on AI my whole life is that I’m passionate about science… If we could build AI in the right way, it could be the ultimate tool to help scientists,” Hassabis said, adding “I hope AlphaFold is a first example of that.” ^[22] AlphaFold’s success in predicting 3D protein structures at atomic accuracy validated this vision, effectively providing structural biologists with an AI assistant that can determine a protein’s shape from its amino acid sequence ^[1]. The impact on life sciences has been profound – tens of thousands of new protein structures have been solved in silico, and researchers are using them to study diseases and design drugs. The achievement was recognized by science’s highest honors (the 2024 Nobel Prize in Chemistry went to Hassabis and colleague John Jumper for AlphaFold ^[1]), underscoring that AI is now a central tool in modern biology.

Hassabis stresses that AI will augment rather than replace scientists. In a late-2024 interview, he explained that tools like AlphaFold let individual scientists “do so much more” by handling data analysis and pattern-finding, but “they can’t figure out the right question to ask…that’s got to come from the human.” ^[3] The best outcomes, in his view, arise from “the best scientists paired with these kinds of tools”, enabling even small research teams to achieve breakthroughs that used to require huge efforts ^[3]. This philosophy guides Google DeepMind’s projects: building AI systems that empower experts in biology, genomics, and medicine.

After AlphaFold, DeepMind and Google have expanded into many areas of life science AI:

• Drug Discovery (Isomorphic Labs): Alphabet spun out Isomorphic Labs as a dedicated AI-first drug discovery company. Its mission is to "reimagine the entire drug discovery process from first principles with an AI-first approach" ^[23]. In January 2024, Isomorphic signed landmark partnerships with Eli Lilly ($45M upfront, $1.7B in milestones) and Novartis ($37.5M upfront, $1.2B in milestones)—deals totaling nearly $3 billion in potential value ^[24]. In February 2025, Novartis expanded its collaboration, adding up to three additional research programs focused on "undruggable" targets that traditional methods cannot address. In March 2025, Isomorphic raised a massive $600 million Series A, signaling strong investor confidence ^[25]. The company is now "staffing up" for its first human clinical trials, with several lead candidates for oncology and immune-mediated disorders in IND-enabling phase. Experts predict the first AI-designed molecules from these partnerships could enter Phase I trials by late 2026.

• AlphaFold 3 and Beyond: In May 2024, DeepMind released AlphaFold 3, which predicts the structure and interactions of virtually all biomolecules—including proteins, DNA, RNA, ligands, and ions. AlphaFold 3 achieves 76.4% full-atom docking accuracy, 1.8x better than RoseTTAFold All-Atom and superior to classic docking tools like Vina and Gold blog.google. The AlphaFold Protein Structure Database received a major redesign for 2025, now providing open access to hundreds of millions of high-accuracy predictions used by over 3 million researchers in 190+ countries ^[26]. Scientists have also released OpenFold3, an open-source alternative approaching AlphaFold 3's performance ^[27].

• MedGemma and Gemini 3: Google's medical AI has advanced rapidly. Gemini 3, launched November 18, 2025, is billed as Google's most capable AI model yet, with enhanced healthcare tools including MedLM for X-ray analysis ^[28]. At I/O 2025, Google announced MedGemma (based on Gemma 3) as their most capable open model for multimodal medical text and image comprehension deepmind.google. The upgraded AMIE (AI Medical Interview Engine) now handles both conversations and visual medical data, helping clinicians piece together diagnostic puzzles more accurately. In January 2026, Google released MedGemma 1.5 with enhanced medical image interpretation, MedASR for medical speech-to-text, and C2S-Scale—a 27 billion parameter foundation model for single-cell analysis that has already generated novel hypotheses about cancer cellular behavior research.google.

• Genomics and Rare Diseases: AlphaMissense continues to classify missense mutations as benign or pathogenic, aiding personalized medicine by interpreting patient genomes far faster than traditional lab assays. The combination of AlphaFold predictions with genomic variant analysis enables researchers to flag dangerous mutations in cancer genes or rare disease genes with unprecedented speed and accuracy.

Hassabis has called this moment a “watershed” for AI in science – after decades of using AI in games and internet applications, we are now “applying it to real-world problems including scientific discovery itself” ^[29]. The past year’s developments at Google DeepMind reflect a broad trend of AI as a co-pilot for scientists. From modeling protein structures and genetic variants to analyzing clinical data, the company’s work suggests that general AI models (like Gemini) plus domain fine-tuning can tackle many problems once thought to require human intellect or decades of experimentation. Still, Hassabis and colleagues caution that these tools must be used with scientific rigor. They advocate publishing in peer-reviewed journals and open-sourcing tools like AlphaFold, so that the community can validate and build upon these AI-driven insights ^[30]. The consensus from DeepMind’s leadership is that AI will amplify human scientific creativity, not supplant it – enabling discoveries in biology at an unprecedented speed, while humans remain in charge of hypothesis and validation.

NVIDIA: GPU-Powered Generative Models for Drug Discovery and Genomics

Chipmaker NVIDIA, known for its AI hardware, has become a central player in AI software for life sciences. CEO Jensen Huang often highlights healthcare as a domain where accelerated computing and AI models are catalyzing a "revolution". In his March 2024 keynote at NVIDIA's GTC conference, Huang declared: "Proteins and genes and brainwaves – anything you can digitize, so long as there's structure, we can probably learn some patterns from it. And if we can learn the patterns, we can understand its meaning. If we can understand it, we might be able to generate it as well. And so therefore, the generative revolution is here." ^[13] This encapsulates NVIDIA's perspective that biological data has become ripe for AI.

GTC 2025 and Beyond: Major Expansions

At GTC 2025, NVIDIA announced significant expansions to its life sciences AI capabilities ^[5]:

• Evo 2: In collaboration with the Arc Institute, NVIDIA released Evo 2—the "largest publicly accessible AI model for biology to date." Trained on more than 9.3 trillion nucleotides from 128,000+ species across the tree of life using 2,000 NVIDIA H100 GPUs, Evo 2 provides both predictive and generative capabilities. It achieves 90% accuracy in BRCA1 mutation predictions for cancer risk assessment and can predict protein shapes and functions across species.

• New BioNeMo NIM Microservices: The platform now offers GenMol (molecule generation), DiffDock (docking), Evo2 (genomic generative AI), and numerous other optimized checkpoints for biomolecular tasks.

• Cadence Partnership Expansion: Cadence announced the integration of NVIDIA BioNeMo NIM microservices with Orion, its cloud-native molecular design platform, accelerating drug discovery with de novo protein structure prediction, small molecule generative AI, and antibody property prediction.

• Sapio Sciences Integration: BioNeMo was integrated into the Sapio Lab Informatics Platform, bringing computational drug discovery directly into electronic lab notebooks with access to AlphaFold2 NIM, MolMIM NIM, and DiffDock NIM.

January 2026: $1 Billion Eli Lilly Partnership

At the J.P. Morgan Healthcare Conference in January 2026, NVIDIA announced a major expansion of BioNeMo along with transformative partnerships ^[6]:

• Eli Lilly & NVIDIA Co-Innovation Lab: A $1 billion, five-year partnership to tackle drug discovery challenges. This follows Lilly's October 2025 announcement of a 1,016 Blackwell Ultra GPU system rated at over 9 exaflops of AI performance.

• Thermo Fisher Autonomous Labs: NVIDIA is collaborating with Thermo Fisher to build autonomous lab infrastructure for scalable scientific discovery.

• New Models: RNAPro for RNA structure prediction and ReaSyn v2 for ensuring AI-designed drugs are practical to synthesize. BioNeMo Recipes were introduced to easily accelerate biological foundation model training, customization, and deployment.

• Ecosystem Growth: Partners including Chai Discovery, Basecamp Research, Boltz, Natera, Apheris, Dyno Therapeutics, OpenFold, and Terray Therapeutics are building on the BioNeMo platform.

AI Scientists and Autonomous Discovery

A growing ecosystem of "AI scientist" companies is developing on NVIDIA open models. Edison Scientific released Kosmos, an AI scientist for autonomous discovery that reportedly can "do six months of work overnight." This represents the frontier of AI-driven drug discovery: fully autonomous systems that can generate hypotheses, design experiments, and analyze results with minimal human intervention.

NVIDIA's efforts have attracted over 100 biotechnology and pharma organizations to use BioNeMo Cloud. Insilico Medicine—which NVIDIA supported since its early days—now operates 30 drug programs with 10 compounds having received IND clearance, including Rentosertib showing favorable Phase IIa results. Looking ahead, NVIDIA's Rubin CPX architecture (announced 2025) promises 128 GB GPU RAM and massive multi-GPU scaling, well-suited for whole-genome AI models and eventual whole-cell simulations.

Anthropic: AI Assistants for Scientific Research and “Compressing” Progress

Anthropic has emerged as a significant force advocating for AI in the service of science, particularly through its large language model Claude. Co-founder and CEO Dario Amodei, a former OpenAI research director, is known for focusing on AI safety – but he is also highly optimistic about AI's upside in areas like biology. In his October 2024 essay titled "Machines of Loving Grace," Amodei outlines a future where AI helps solve fundamental scientific problems and vastly accelerates discovery ^[8]. He introduces the idea of a "compressed 21st century": "My basic prediction is that AI-enabled biology and medicine will allow us to compress the progress that human biologists would have achieved over the next 50–100 years into 5–10 years." At the 2025 World Economic Forum in Davos, Amodei declared that AI breakthroughs could radically accelerate advancements in biology—potentially matching 100 years of progress in just 5–10 years—and predicted systems "broadly better than all humans at almost all things" by 2026 or 2027 dataphoenix.info.

This vision includes achieving what Amodei calls "biological freedom" – humanity gaining control over biology to "eliminate diseases like cancer and Alzheimer's and even radically extend life expectancy." He predicts: reliable prevention and treatment of nearly all natural infectious disease; elimination of most cancer; effective cures for genetic disease; prevention of Alzheimer's; biological freedom encompassing advancements in birth control, fertility, weight management, and appearance; and potentially doubling the human lifespan to about 150 years. Amodei states that "biology is probably the area where scientific progress has the greatest potential to directly and unambiguously improve the quality of human life."

Claude for Life Sciences (October 2025)

Anthropic made its formal entry into the life sciences sector with the launch of Claude for Life Sciences in October 2025, months after hiring longtime industry executive Eric Kauderer-Abrams as head of biology and life sciences ^[31]. This product is designed to help researchers at each step of the drug discovery process—reading studies, generating hypotheses, analyzing data, and even preparing regulatory submissions.

Key integrations announced include:

• Benchling (lab informatics)
• BioRender (scientific visualization)
• PubMed (literature search)
• Scholar Gateway (developed by Wiley)
• Synapse.org (collaborative research)
• 10x Genomics (single-cell and spatial analysis in natural language)

Anthropic reports that analyses that previously required "days" of validating and compiling information can now be completed in minutes ^[32].

Real-World Adoption

The impact is already visible in major pharmaceutical companies:

• Novo Nordisk cut clinical study documentation from over 10 weeks to 10 minutes using Claude
• Sanofi reports the majority of its employees use Claude every day
• Biomni, an agentic AI platform from Stanford University, uses Claude to navigate hundreds of tools, packages, and datasets across more than 25 biological subfields, forming hypotheses and designing experimental protocols in plain English ^[33]

Claude for Healthcare (2026)

In 2026, Anthropic expanded its offerings with Claude for Healthcare—a complementary set of tools and resources allowing healthcare providers, payers, and consumers to use Claude for medical purposes through HIPAA-ready products^[34]. New capabilities for life sciences include additional scientific platform connections and support for clinical trial management and regulatory operations.

Model Capabilities

Anthropic has invested heavily in making Claude the most capable model for scientific work. Claude Opus 4.5 shows significant improvements in figure interpretation, computational biology, and protein understanding benchmarks. It is described as "an incredible model and a great choice for computational biology" ^[35], excelling at coding, reasoning about biology, and understanding scientific figures.

Despite the rapid progress, Amodei and Anthropic remain mindful of accuracy and safety. Claude is designed with constitutional AI principles to reduce the chance of incorrect or harmful suggestions—crucial in medicine where errors can cost lives. They encourage users to treat AI outputs as assisting tools and verify critical findings experimentally. As Amodei noted, addressing AI risks is "the only thing standing between us and [a] fundamentally positive future" for science.

Conclusion and Outlook

As we enter 2026, the dialogue around AI in life sciences has evolved from speculative to transformational. The predictions made by AI leaders are beginning to materialize into real-world impact:

• AI breakthroughs are delivering clinical-stage results: AlphaFold 3 now predicts interactions of virtually all biomolecules with unprecedented accuracy. The 2024 Nobel Prize in Chemistry recognized AI's "game-changing" impact on biology. More importantly, AI-designed drug candidates are now entering human clinical trials—Insilico Medicine has 10 compounds with IND clearance, and Isomorphic Labs expects first Phase I trials by late 2026. AI drug discovery investment rebounded to $3.8 billion in 2024, projected to reach $15.2 billion by 2030 ^[7].

• Billion-dollar partnerships signal industry transformation: The $1 billion NVIDIA-Eli Lilly co-innovation lab (January 2026), $3 billion Isomorphic Labs partnerships with Eli Lilly and Novartis, and $1.2 billion Sanofi-Insilico collaboration represent unprecedented investment in AI-driven drug discovery. Major pharma companies including Moderna, Pfizer, Merck, and Novartis have deployed AI assistants enterprise-wide.

• AI tools are now integrated into scientific workflows: Claude for Life Sciences connects to Benchling, PubMed, and 10x Genomics. NVIDIA BioNeMo serves 100+ biotech organizations. MedGemma provides open multimodal medical AI. Biomni from Stanford enables AI agents to navigate 25+ biological subfields. Researchers report tasks that took weeks now complete in minutes.

• The open-source movement is accelerating: OpenFold3 approaches AlphaFold 3's performance as open-source. Evo 2 (9.3 trillion nucleotides) is the largest publicly accessible biology AI model. This democratization ensures AI tools reach researchers globally, not just at well-funded institutions.

• Autonomous AI scientists are emerging: Edison Scientific's Kosmos reportedly does "six months of work overnight." This represents a frontier where AI systems can autonomously generate hypotheses, design experiments, and analyze results—a vision that seemed distant just two years ago.

Challenges remain: Medical AI must be rigorously tested for bias and errors. Privacy of patient data requires careful handling. The transition from computational predictions to successful clinical outcomes is not guaranteed—proteins that dock perfectly in silico may still fail in human trials. Regulatory frameworks are still adapting to AI-designed therapeutics.

However, the momentum is undeniable. As Dario Amodei predicted, we appear to be entering a "compressed 21st century" for biology. The first AI-designed drugs approved for human use may arrive within the next few years. Claude for Healthcare is HIPAA-ready. Google's C2S-Scale is generating novel cancer hypotheses. The vision these AI pioneers articulated—AI as an amplifier of human scientific creativity, compressing a century of biology into a decade—is no longer speculative. It is happening now.

For life science professionals, the imperative is clear: engage with these AI tools, form partnerships with AI experts, and prepare for a pace of discovery that will feel bewildering compared to traditional R&D timelines. The goal remains what Demis Hassabis articulated—not to replace scientists, but to let scientists armed with AI achieve breakthroughs once thought impossible. If Amodei's predictions hold, we may see the elimination of major diseases and extension of healthy human lifespan within our lifetimes. The coming years will test these extraordinary claims against clinical reality.

Sources:

• Nobel Prize Committee, "The Nobel Prize in Chemistry 2024" (recognizing AlphaFold's impact) ^[11]

• Demis Hassabis, interview during Nobel Week (AI as a tool for scientists) ^[11]

• Jensen Huang, GTC 2024 and GTC 2025 Keynotes (NVIDIA BioNeMo and generative AI in biology) ^{[13] [5]}

• NVIDIA Investor Relations – "BioNeMo Platform Adopted by Life Sciences Leaders" (January 2026 announcements) ^[6]

• OpenAI–Sanofi Press Release (AI partnership for drug development) ^[9]

• Thermo Fisher–OpenAI Partnership Announcement (October 2025) ^[18]

• Google Research Blog – "Advancing medical AI with Med-Gemini" research.google

• Google DeepMind – MedGemma model page deepmind.google

• Google Blog – "AlphaFold 3 predicts the structure and interactions of all of life's molecules" blog.google

• Dario Amodei, "Machines of Loving Grace" essay ^[8]

• Anthropic – "Claude for Life Sciences" announcement ^{[32] [31]}

• Anthropic – "Claude for Healthcare" announcement ^[34]

• Anthropic – "How scientists are using Claude to accelerate research" ^[35]

• Isomorphic Labs – Partnerships page ^[12]

• BioPharma Dive – "Isomorphic raises $600M" ^[25]

• Pharmaphorum – "Isomorphic signs Lilly, Novartis for $3bn AI drug hunt" ^[24]

• Fortune – "Inside Big Pharma's big bet on AI" (January 2026) ^[7]

• Sam Altman, "The Intelligence Age" essay ^[10]

• Bloomberg News – "OpenAI, Thrive Capital Back AI Drug Discovery Startup" bnnbloomberg.ca

• Nature – "Open-source protein structure AI aims to match AlphaFold" ^[27]

• AlphaFold Protein Structure Database 2025 update ^[26]

Sources

• ^[11]
• genengnews.com – GTC 2024
• genengnews.com – GTC 2025
• ^[6]
• research.google
• deepmind.google
• blog.google
• ^[9]
• ^[18]
• ^[8]
• ^[32]
• ^[31]
• ^[12]
• ^[25]
• ^[24]
• ^[7]
• ^[10]
• bnnbloomberg.ca
• ^[27]
• ^[26]
• ^[19]
• health.google