Ai for Science (2026)

If you want to understand AI for Science (AI4Science) - how AI is changing biology, chemistry, physics, medicine, materials science and scientific discovery - I'd recommend following a mix of leading researchers, scientific entrepreneurs, and people who explain the field exceptionally well.

Here is the list I'd start with.

1. The people I'd follow first

FOUNDATIONAL THINKERS AND RESEARCH LEADERS

1. Demis Hassabis

Must follow

AI, biology, chemistry, neuroscience and scientific discovery. One of the most important voices on AI's potential to transform science.

X: @demishassabis

2. Pushmeet Kohli

AI for scientific discovery, protein structures, mathematics and scientific reasoning.

X / LinkedIn: Search Pushmeet Kohli

3. Anima Anandkumar

Scientific machine learning, neural operators, physics simulations and foundation models for the physical world.

X: @AnimaAnandkumar

4. Regina Barzilay

AI in medicine, cancer detection, drug discovery and healthcare.

Follow: MIT and personal research updates

5. David Baker

Computational protein design and how AI can create entirely new proteins.

Follow: @bakerlab

6. Andrew White

AI agents for chemistry, molecular design and automating scientific workflows.

X: @andrewwhite01

These recommendations span several major AI4Science research directions. AI4S Learning LibraryTwo Years of Learning About AI: The People and Sources I FollowAI for science: 35 AI researchers and builders

2. People building the next generation of AI scientists

These are particularly interesting if you want to understand where the field is going.

AI Scientists – Tools, Co-authors, or Founders?AI for science: 35 AI researchers and buildersAI4S Learning Library

3. Organizations and research labs to follow

These are arguably just as important as individual researchers.

Google DeepMind

AlphaFold, AI co-scientists, mathematics and scientific discovery.

Research and publications

Microsoft Research AI for Science

Materials discovery, molecular simulation, chemistry and scientific foundation models.

AI for Science research

Arc Institute

AI-enabled biology, cell science and foundational biological research.

Research and projects

FutureHouse

Building AI scientists and agents that can conduct scientific research.

Research and projects

Chan Zuckerberg Biohub

AI, advanced imaging, cell biology and understanding human health.

Research and projects

4. My recommended learning combination

If I were building a high-quality information feed around AI for Science, I'd start with these eight:

1. Demis Hassabis
2. Pushmeet Kohli
3. Anima Anandkumar
4. Regina Barzilay
5. David Baker
6. Andrew White
7. Sam Rodriques
8. Jim Fan

Also follow AK (@_akhaliq) for discovering new AI research papers.

One important distinction: AI for Science is much broader than using ChatGPT for scientific research. It includes learning physical laws, designing molecules, predicting protein structures, accelerating simulations, discovering materials and automating experiments. 5 SCIENCEMicrosoft Research AI for Science

One question that would help me tailor this list

Which aspect of AI for Science interests you most?

To learn about AI for Science (AI4Sci) - the intersection where machine learning accelerates breakthroughs in biology, chemistry, materials science, physics, and climate modeling - you want to follow leaders who build foundational models for the natural sciences, pioneers bridging tech and biomedicine, and institutions pushing the state-of-the-art.

1. Pioneers of AI for Science & Structural Biology

• Demis Hassabis (@demishassabis): Co-founder and CEO of Google DeepMind and Isomorphic Labs. He leads the team behind AlphaFold, which fundamentally transformed structural biology by predicting 3D structures of proteins and molecules, ushering in a new era of computer-aided drug discovery.

• Daphne Koller (@DaphneKoller): Founder and CEO of insitro and former Stanford computer science professor. She is a pioneer in applying machine learning to drug discovery and large-scale biological datasets.

• Yoshua Bengio (@yoshuabengio): Turing Award laureate and founder of Mila (Montreal Institute for Learning Algorithms). His research group heavily focuses on applying generative AI and deep learning to molecular discovery, drug design, and material science.

2. Spatial Intelligence, Healthcare, and Systems Biology

• Fei-Fei Li (@drfeifei): Co-Director of the Stanford Institute for Human-Centered AI (HAI) and co-founder of World Labs. While widely known for computer vision, her work and academic network heavily influence spatial intelligence and AI's integration into healthcare and clinical science.

• Max Welling: A prominent researcher specializing in machine learning for physics and chemistry (such as equivariant neural networks for molecular modeling). He splits his time between University of Amsterdam and Microsoft Research AI4Science.

3. Labs, Institutions, and Organizations to Follow

Instead of just individual accounts, following these specialized research hubs will give you front-row seats to breakthrough papers, datasets, and open-source tools:

• Google DeepMind / Isomorphic Labs: The gold standard for AI for Science breakthroughs (AlphaFold, GraphCast for weather, GNoME for materials discovery).

• Microsoft Research AI4Science: A dedicated division focused on using machine learning to model quantum chemistry, molecular dynamics, and material design.

• Mila (Montreal Institute for Learning Algorithms): Known for driving open-source AI initiatives targeting climate change, drug discovery, and sustainability.

• Broad Institute of MIT and Harvard: Look out for their computational biology and machine learning research groups, which apply AI directly to genomics and biomedicine.

Where to Track Their Work

1. Google Scholar & ArXiv: AI for Science moves too fast for traditional textbooks. Regularly check arXiv categories like cs.LG (Machine Learning), q-bio (Quantitative Biology), and physics.chem-ph (Chemical Physics).

2. Specialized Conferences: Keep an eye on papers published at workshops like AI for Science (AI4Science) at NeurIPS, ICML, and ICLR.

Are you looking to dive into a specific sub-field within AI for Science - such as computational drug discovery, climate modeling, or materials science - so I can point you toward more specialized researchers?

To learn about AI for Science (the intersection of machine learning, physics, biology, chemistry, and automated discovery), you should follow leading frontier lab executives, academic research hubs, and technical writers who focus heavily on how models simulate the physical world rather than just chatbots.

The primary figures, research organizations, and newsletters to follow include:

• Demis Hassabis (Co-founder and CEO of Google DeepMind, @demishassabis): As a recipient of the Nobel Prize in Chemistry for his work on AlphaFold, Hassabis is a foundational voice on how AI transforms structural biology and physical sciences.

• Jim Fan (Senior AI Research Scientist at NVIDIA, @DrJimFan): Frequently shares deep breakdowns on foundation models applied to robotics, physical simulations, and general-purpose agentic systems that interact with the real world.

• Nathan Lambert (Author of Interconnects, formerly at the Allen Institute for AI): Excellent for tracking technical shifts in model training, reinforcement learning, and open-source scientific machine learning dynamics. Best AI Newsletters to Subscribe To in 2026: 16 Picks Ranked | Blog

• Andrew Ng (Founder of DeepLearning.AI and Stanford University adjunct professor): Writes a widely read weekly briefing covering applied AI breakthroughs, including intersections with healthcare and data-heavy scientific verticals. Top 10 AI researchers in the world : r/OpenAIInsights - Reddit

• Academic & Institutional Hubs: Following groups like the I-X Centre for AI in Science at Imperial College London or the AI Futures Lab at Berkeley Institute for Data Science (BIDS) on LinkedIn or Bluesky provides direct access to peer-reviewed directions, developer summits, and cross-disciplinary applications. AI Futures Lab | Berkeley Institute for Data Science (BIDS)I-X Centre for AI in Science - Imperial College London

If you have a specific scientific domain in mind (such as drug discovery, climate modeling, or materials science), let me know and I can tailor this list to researchers specializing in that exact field.

For AI for Science, follow Ethan Mollick (@emollick) for research and real-world applications, and Demis Hassabis for frontier research milestones.(x.com)(uxcel.com)

If you want to learn about AI for Science, the best approach is to follow researchers and leaders who are applying AI to scientific discovery, not just general AI development. Here are some of the most influential figures to keep an eye on:

🌍 Pioneers at the Intersection of AI & Science

• Demis Hassabis (DeepMind CEO, Nobel laureate)

  • Led the creation of AlphaFold , which revolutionized protein structure prediction.

  • Currently driving multimodal AI models (Gemini) with applications in biology, physics, and beyond.Top 10 AI Influencers of 2026 - KDnuggetsTop 50 People in AI to Follow in 2026 (X, LinkedIn, Websites)

• Fei-Fei Li (Stanford, AI4Science advocate)

  • Co-director of Stanford’s Human-Centered AI Institute.

  • Strong voice on using AI responsibly in healthcare, biology, and scientific research.TIME Reveals the 2026 TIME100 AI List of the World’s Most Influential People in Artificial IntelligenceTop AI Leaders of 2026 | The Industry Leaders

• Yoshua Bengio (Mila, Turing Award winner)

  • Focuses on deep learning and AI safety, but also emphasizes AI’s role in advancing scientific understanding.Top 50 People in AI to Follow in 2026 (X, LinkedIn, Websites)Top AI Leaders of 2026 | The Industry Leaders

• Geoffrey Hinton (ex-Google, Turing Award winner)

  • Foundational deep learning researcher, now vocal about AI’s potential in neuroscience and cognitive science.Top 50 People in AI to Follow in 2026 (X, LinkedIn, Websites)

🔬 Researchers Driving AI for Discovery

• Andrew Ng (DeepLearning.AI, Coursera)

  • Promotes AI education and applied AI in medicine, climate science, and engineering.Top 50 People in AI to Follow in 2026 (X, LinkedIn, Websites)Top AI Leaders of 2026 | The Industry Leaders

• Mustafa Suleyman (Inflection AI, ex-DeepMind)

  • Co-founder of DeepMind, involved in early AI-for-science projects like AlphaGo and AlphaFold.Top AI Leaders of 2026 | The Industry Leaders

• Ilya Sutskever (Safe Superintelligence, ex-OpenAI)

  • While focused on safe superintelligence, his research background (sequence-to-sequence learning, reasoning models) is foundational for scientific applications of AI.Top 10 AI Influencers of 2026 - KDnuggetsTIME Reveals the 2026 TIME100 AI List of the World’s Most Influential People in Artificial Intelligence

📚 Where to Follow Them

• X(Twitter) and LinkedIn : Most of these leaders share updates, talks, and papers.

• Podcasts & Interviews: For example, Ilya Sutskever’s interview on The Dwarkesh Podcast is a great entry point into frontier AI thinking.Top 10 AI Influencers of 2026 - KDnuggets

• Academic Labs & Institutes: DeepMind, Mila, Stanford HAI, and OpenAI often publish cutting-edge research directly relevant to science.

👉 If your main interest is AI applied to biology, chemistry, and physics, I’d recommend starting with Demis Hassabis (DeepMind) and Fei-Fei Li (Stanford). They are at the forefront of using AI to accelerate scientific discovery.

Would you like me to curate a list of specific labs and projects (like AlphaFold, AI for climate modeling, or materials discovery) so you can follow both the people and the research outputs?