Yue Xu

3 months ago

🚀 Excited to announce the launch of my lab at the Children’s Mercy Research Institute in Kansas City! We're engineering next-gen ionizable lipid nanoparticles to revolutionize nucleic acid delivery for pediatric cancer and genetic diseases. Check out our work and website: https://t.co/1coLzxrXqZ

Yuexulab's tweet photo. 🚀 Excited to announce the launch of my lab at the Children’s Mercy Research Institute in Kansas City! We're engineering next-gen ionizable lipid nanoparticles to revolutionize nucleic acid delivery for pediatric cancer and genetic diseases. Check out our work and website: https://t.co/1coLzxrXqZ

Yuexulab retweeted

5 months ago

mRNA cancer vaccines are not just a vaccine problem. They are also an AI systems design problem. For an mRNA cancer vaccine to work, we need to solve at least two brutal design problems: 1. Delivery Can we get the mRNA into the right cells, at the right dose, with the right immune activation? 2. Design Can we choose the right tumor antigens and encode them in a way that produces a real anti-tumor immune response? This is where AI can change the game. On the delivery side, lipid nanoparticles (LNPs) are not just packaging. They are programmable materials. The mRNA has to survive, enter the right cells, escape the endosome, translate efficiently, and activate the immune system without overwhelming toxicity. In our AGILE work, we used deep learning + combinatorial chemistry to accelerate ionizable lipid discovery for mRNA delivery, showing that LNPs can be tailored for different cell types rather than treated as one-size-fits-all carriers. (paper: https://t.co/1ApLpdnPQF) In our new LUMI-lab work, we pushed this further: a foundation model-driven self-driving lab that combines molecular pretraining, active learning, and robotics to autonomously discover ionizable lipids for mRNA delivery. The platform synthesized and screened >1,700 LNPs and found brominated lipids as an unexpected feature that improves mRNA delivery into human cells. (paper: https://t.co/GiDnnLs7HX) This is the future of mRNA delivery: design → synthesize → formulate → test → learn → redesign But delivery is only half the story. For cancer vaccines, the payload also has to be designed. Which tumor mutations should become vaccine targets? AI can help across the full stack: --Neoantigen prediction From tumor sequencing, models can predict which mutations generate peptides likely to be processed, presented by HLA, and recognized by T cells. -- HLA binding + immunogenicity Not every binder is immunogenic. Better models can learn from peptide–MHC structure, TCR recognition, antigen abundance, and tumor context. -- mRNA construct optimization AI can optimize codon usage, UTRs, RNA stability, translation efficiency, secondary structure, and manufacturability. -- Tumor immune microenvironment modeling A great antigen may fail if the tumor is immunologically cold. Multimodal models can integrate genomics, transcriptomics, pathology, and immune profiling to predict response. -- Combination therapy design AI can help identify when vaccines should be paired with checkpoint blockade, cytokines, targeted therapy, or innate immune agonists. The future of personalized cancer vaccines is not just “sequence tumor → pick mutations → inject mRNA.” It is an end-to-end learning system: tumor genome → immune model → antigen ranking → RNA design → clinical response → model improvement This is where AI can make mRNA cancer vaccines faster, more precise, and ultimately more effective.

BoWang87's tweet photo. mRNA cancer vaccines are not just a vaccine problem.
They are also an AI systems design problem.

For an mRNA cancer vaccine to work, we need to solve at least two brutal design problems:

1. Delivery
Can we get the mRNA into the right cells, at the right dose, with the right immune activation?

2. Design
Can we choose the right tumor antigens and encode them in a way that produces a real anti-tumor immune response?

This is where AI can change the game.
On the delivery side, lipid nanoparticles (LNPs) are not just packaging. They are programmable materials.

The mRNA has to survive, enter the right cells, escape the endosome, translate efficiently, and activate the immune system without overwhelming toxicity.

In our AGILE work, we used deep learning + combinatorial chemistry to accelerate ionizable lipid discovery for mRNA delivery, showing that LNPs can be tailored for different cell types rather than treated as one-size-fits-all carriers. (paper: https://t.co/1ApLpdnPQF)

In our new LUMI-lab work, we pushed this further: a foundation model-driven self-driving lab that combines molecular pretraining, active learning, and robotics to autonomously discover ionizable lipids for mRNA delivery. The platform synthesized and screened >1,700 LNPs and found brominated lipids as an unexpected feature that improves mRNA delivery into human cells. (paper: https://t.co/GiDnnLs7HX)

This is the future of mRNA delivery:
design → synthesize → formulate → test → learn → redesign

But delivery is only half the story.

For cancer vaccines, the payload also has to be designed.

Which tumor mutations should become vaccine targets?

AI can help across the full stack:

--Neoantigen prediction
From tumor sequencing, models can predict which mutations generate peptides likely to be processed, presented by HLA, and recognized by T cells.
-- HLA binding + immunogenicity
Not every binder is immunogenic. Better models can learn from peptide–MHC structure, TCR recognition, antigen abundance, and tumor context.
-- mRNA construct optimization
AI can optimize codon usage, UTRs, RNA stability, translation efficiency, secondary structure, and manufacturability.
-- Tumor immune microenvironment modeling
A great antigen may fail if the tumor is immunologically cold. Multimodal models can integrate genomics, transcriptomics, pathology, and immune profiling to predict response.
-- Combination therapy design
AI can help identify when vaccines should be paired with checkpoint blockade, cytokines, targeted therapy, or innate immune agonists.

The future of personalized cancer vaccines is not just “sequence tumor → pick mutations → inject mRNA.”

It is an end-to-end learning system:
tumor genome → immune model → antigen ranking → RNA design → clinical response → model improvement

This is where AI can make mRNA cancer vaccines faster, more precise, and ultimately more effective.

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Just an Ugly, Grumpy Old Vet! I'm an Atheist. I live paycheck to paycheck. Not looking for a wife, girlfriend or anything permanent.

5 months ago

@hansenhe7 Congrats!

Yuexulab retweeted

Moderna

@moderna_tx

5 months ago

We announced today that we will present data from a Phase 1/2 study of mRNA-4359, an investigational cancer antigen therapy, at the American Association for Cancer Research (AACR) Annual Meeting in San Diego, CA, on April 17-22, 2026. The U.S. FDA has granted Fast Track designation for mRNA-4359 in combination with pembrolizumab for the treatment of checkpoint inhibitor refractory unresectable or metastatic melanoma with PD-L1+ (TPS≥1%). Read more: https://t.co/W46C3269nA

$moderna_tx's tweet photo. We announced today that we will present data from a Phase 1/2 study of mRNA-4359, an investigational cancer antigen therapy, at the American Association for Cancer Research (AACR) Annual Meeting in San Diego, CA, on April 17-22, 2026. The U.S. FDA has granted Fast Track designation for mRNA-4359 in combination with pembrolizumab for the treatment of checkpoint inhibitor refractory unresectable or metastatic melanoma with PD-L1+ (TPS≥1%). Read more: https://t.co/W46C3269nA$

Who to follow

Old man

@mdamann1961

Tahoe

@tahoe_ai

Mapping how chemistry perturbs biology to build a virtual model of the human cell. Formerly known as Vevo Therapeutics.

Haotian Cui

@HAOTIANCUI1

Building virtual cell. Large models for drug discovery. CS Ph.D. @UofT.

Yuexulab retweeted

UHN Research @UHN_Research

6 months ago

New research from @PMResearch_UHN introduces LUMI‑lab—a self‑driving lab that uses #AI & #robotics to discover new molecules. The technology could accelerate gene editing, mRNA vaccines, & other therapies. 🔗 Read more: https://t.co/ADcW75G4I1

UHN_Research's tweet photo. New research from @PMResearch_UHN introduces LUMI‑lab—a self‑driving lab that uses #AI & #robotics to discover new molecules. The technology could accelerate gene editing, mRNA vaccines, & other therapies.

🔗 Read more: https://t.co/ADcW75G4I1 https://t.co/1KuR25VdpL

Yuexulab retweeted

Le Cong@Stanford, AI+Bio+Gene-Editing

7 months ago

Everyone is talking about personalized mRNA cancer vaccines. I want to share two recent Nature papers that cut through the excitement and reveal something the viral posts aren't telling you: the approach works — but only in patients whose immune system actually responds to the vaccine. In the PDAC trial, that was half. Papers: — TNBC-MERIT trial (Nature 2026): https://t.co/pCpKdgtWbw — PDAC 3-year follow-up (Nature 2025): https://t.co/1oJxjJSPhS Here's the exact number that explains why. The PDAC trial: at 3.2 years median follow-up, vaccine responders had median recurrence-free survival that was never reached. Non-responders: 13.4 months. HR = 0.14. The T cell memory is real — some clones are projected to persist for over a decade. The TNBC trial: 10 of 14 patients remained relapse-free at 5 years. One patient has been in remission for over 6 years, with neoantigen-specific T cells still circulating at ~2% of her CD8 repertoire. So what separates responders from non-responders? Across both trials: only 41 of 251 neoantigens actually triggered a T cell response. That's 16%. Each vaccine encodes up to 20 neoantigens — the algorithm's best guess at which tumor mutations will be immunogenic. Most don't work. Half the PDAC patients didn't respond — not because they couldn't mount an immune response (they responded fine to concurrent COVID vaccines) — but because their selected neoantigens happened to miss. This is the core unsolved problem: predicting, from sequence alone, which mutations will produce peptides that a specific patient's immune system will actually recognize. It sounds like an MHC binding problem. It isn't. Tools like NetMHCpan handle binding affinity reasonably well. What they miss is the full causal chain: 1. Proteasomal processing — will the protein actually be cleaved into this exact peptide? 2. TAP transport — will it reach the ER for MHC loading? 3. HLA-peptide stability — across the patient's specific HLA alleles (10,000+ variants in the population) 4. T cell repertoire availability — has central tolerance already deleted the clones that would recognize it? 5. Tumor clonal architecture — is this mutation in every tumor cell, or just 30%? Targeting subclonal neoantigens leaves most of the tumor untouched. Every step is a filter. Current prediction stops at step one. Compounding everything: average manufacturing time in the TNBC trial was 69 days (range: 34–125) from sample to vaccine release. For pancreatic cancer, where non-responders recur at 13.4 months post-surgery, that's not a footnote. It's a window closing. The good news: the T cell biology is sound. The mRNA platform works. The immunology is spectacular — when it works. The bottleneck is the first step: choosing which 20 neoantigens go in the vaccine. Get that prediction right, and the responder rate moves. This is where AI in cancer immunotherapy has to go next. Not mRNA design. Not LNP formulation. Immunogenicity prediction — integrating mutation calling, HLA typing, T cell repertoire sequencing, and single-cell tumor expression simultaneously, as a causal inference problem, not a binding affinity lookup. We don't have a model that does this well. That's the gap.

BoWang87's tweet photo. Everyone is talking about personalized mRNA cancer vaccines.

I want to share two recent Nature papers that cut through the excitement and reveal something the viral posts aren't telling you: the approach works — but only in patients whose immune system actually responds to the vaccine. In the PDAC trial, that was half.

Papers:
— TNBC-MERIT trial (Nature 2026): https://t.co/pCpKdgtWbw
— PDAC 3-year follow-up (Nature 2025): https://t.co/1oJxjJSPhS

Here's the exact number that explains why.

The PDAC trial: at 3.2 years median follow-up, vaccine responders had median recurrence-free survival that was never reached. Non-responders: 13.4 months. HR = 0.14. The T cell memory is real — some clones are projected to persist for over a decade.

The TNBC trial: 10 of 14 patients remained relapse-free at 5 years. One patient has been in remission for over 6 years, with neoantigen-specific T cells still circulating at ~2% of her CD8 repertoire.

So what separates responders from non-responders?

Across both trials: only 41 of 251 neoantigens actually triggered a T cell response. That's 16%.

Each vaccine encodes up to 20 neoantigens — the algorithm's best guess at which tumor mutations will be immunogenic. Most don't work. Half the PDAC patients didn't respond — not because they couldn't mount an immune response (they responded fine to concurrent COVID vaccines) — but because their selected neoantigens happened to miss.

This is the core unsolved problem: predicting, from sequence alone, which mutations will produce peptides that a specific patient's immune system will actually recognize.

It sounds like an MHC binding problem. It isn't. Tools like NetMHCpan handle binding affinity reasonably well. What they miss is the full causal chain:

1. Proteasomal processing — will the protein actually be cleaved into this exact peptide?
2. TAP transport — will it reach the ER for MHC loading?
3. HLA-peptide stability — across the patient's specific HLA alleles (10,000+ variants in the population)
4. T cell repertoire availability — has central tolerance already deleted the clones that would recognize it?
5. Tumor clonal architecture — is this mutation in every tumor cell, or just 30%? Targeting subclonal neoantigens leaves most of the tumor untouched.

Every step is a filter. Current prediction stops at step one.

Compounding everything: average manufacturing time in the TNBC trial was 69 days (range: 34–125) from sample to vaccine release. For pancreatic cancer, where non-responders recur at 13.4 months post-surgery, that's not a footnote. It's a window closing.

The good news: the T cell biology is sound. The mRNA platform works. The immunology is spectacular — when it works.

The bottleneck is the first step: choosing which 20 neoantigens go in the vaccine. Get that prediction right, and the responder rate moves.

This is where AI in cancer immunotherapy has to go next. Not mRNA design. Not LNP formulation. Immunogenicity prediction — integrating mutation calling, HLA typing, T cell repertoire sequencing, and single-cell tumor expression simultaneously, as a causal inference problem, not a binding affinity lookup.

We don't have a model that does this well. That's the gap.

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Yuexulab retweeted

@lecong

7 months ago

Wet lab hasn't changed in 50 years - we are changing that. As written by legendary @denibechard on @sciam LabOS is powering agentic lab via smart glasses, multimodal AI, and collaborating robotics, real-time guiding human scientists and training junior scientists to expert-level in 1 week for complex gene-editing experiments. #ai4science in action!🔬 With LabOS and the sister project MedOS, we hope to turn every lab and clinic into AI-perceivable, AI-operable environment. Not to replace humans — to make us better! https://t.co/ih2Nicef7g

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Yuexulab retweeted

Nav Toor

@heynavtoor

7 months ago

https://t.co/z4kQwYt7EA

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Yuexulab retweeted

Cell @CellCellPress

7 months ago

Now online! LUMI-lab: A foundation model-driven autonomous platform enabling discovery of ionizable lipid designs for mRNA delivery https://t.co/AXWLtz40LJ

Yuexulab retweeted

Yingcheng Charles Wu

@Charles_Y_Wu

7 months ago

《Cell》具身AI科学家正式上岗！ LUMI-lab诞生！ 28百万分子预训练的大模型当“大脑”，全机器人闭环当“身体”，10轮迭代就自主合成+测试1700+个离子化脂质！它完全自主发现了人类文献从未报道过的“溴化脂质尾巴”！顶级分子直接把小鼠肺上皮CRISPR基因编辑效率干到20.3%，刷新历史纪录！🔥 暴论时间： 2026年了，还让研究生和实验员天天手工移液、洗板、做重复实验的实验室，真的该大规模优化了！把聪明大脑从96孔板里解放出来吧！让他们去指挥AI科学家军团，干真正战略级的颠覆性创新！ LUMI-lab不是工具，是实验室自动化的未来范式。从“人工农场”到“AI分子工厂”，革命已经打响第一枪！你的实验室2026年准备好拥抱具身AI科学家了吗？还是继续熬夜当“高级移液工”？🤖 #具身智能 #AI科学家 #实验室自动化 #人工智能 #大模型 #AGI #AI革命 #未来科技 #科技前沿 #机器人 #AI科研 #科学智能 #EmbodiedAI #AIScientist #LabAutomation #SelfDrivingLab #AIRevolution #FutureOfScience #AIforScience #AutonomousDiscovery

519

7 months ago

@BowenLi_Lab @CellCellPress Huge thanks for your leadership!

Yuexulab retweeted

Bowen LI

@BowenLi_Lab

7 months ago

LUMI-lab is out today in @CellCellPress! 🚀We built a self-driving lab that closes the loop between an AI foundation model + robotics to accelerate lipid nanoparticle (LNP) discovery for mRNA delivery. Free access to the manuscript: https://t.co/HTAaQPrvSe Code available on GitHub: https://t.co/OgLJ4OamGa Check the video here: https://t.co/t3sffZw53i LUMI-lab (Large-scale Unsupervised Modeling followed by Iterative experiments) is a self-driving laboratory that tightly closes the loop between an AI foundation model and automated robotics to accelerate LNP discovery for mRNA delivery. To tackle data scarcity in emerging mRNA delivery domains, we pretrained the model on 28M+ molecular structures, then iteratively improved it with closed-loop experimental data. This is the kind of workflow we believe can meaningfully expand the accessible chemical space for next-generation RNA medicines. In this work, across ten active-learning cycles, LUMI-lab synthesized and evaluated 1,700+ new LNPs and unexpectedly identified a new design feature for efficient delivery: brominated lipid tails. These brominated-tail ionizable lipids delivered mRNA into human lung cells more efficiently than approved benchmarks, despite representing only a small fraction of the initial chemical space explored. Huge thanks to our team @YueXu1995, @HAOTIANCUI1, Kuan Pang, Reagan Li, and collaborator @BoWang87 at @UofT and @PMResearch_UHN, and to @acceleration_c @CIHR_IRSC @NSERC_CRSNG @InnovationCA @GSK for supporting this platform. #mRNA #LNP #AI #SelfDrivingLab @bradwouters @EricTopol @elonmusk

$BowenLi_Lab's tweet photo. LUMI-lab is out today in @CellCellPress! 🚀We built a self-driving lab that closes the loop between an AI foundation model + robotics to accelerate lipid nanoparticle (LNP) discovery for mRNA delivery. Free access to the manuscript: https://t.co/HTAaQPrvSe Code available on GitHub: https://t.co/OgLJ4OamGa Check the video here: https://t.co/t3sffZw53i LUMI-lab (Large-scale Unsupervised Modeling followed by Iterative experiments) is a self-driving laboratory that tightly closes the loop between an AI foundation model and automated robotics to accelerate LNP discovery for mRNA delivery. To tackle data scarcity in emerging mRNA delivery domains, we pretrained the model on 28M+ molecular structures, then iteratively improved it with closed-loop experimental data. This is the kind of workflow we believe can meaningfully expand the accessible chemical space for next-generation RNA medicines. In this work, across ten active-learning cycles, LUMI-lab synthesized and evaluated 1,700+ new LNPs and unexpectedly identified a new design feature for efficient delivery: brominated lipid tails. These brominated-tail ionizable lipids delivered mRNA into human lung cells more efficiently than approved benchmarks, despite representing only a small fraction of the initial chemical space explored. Huge thanks to our team @YueXu1995, @HAOTIANCUI1, Kuan Pang, Reagan Li, and collaborator @BoWang87 at @UofT and @PMResearch_UHN, and to @acceleration_c @CIHR_IRSC @NSERC_CRSNG @InnovationCA @GSK for supporting this platform. #mRNA #LNP #AI #SelfDrivingLab @bradwouters @EricTopol @elonmusk$

7 months ago

@BoWang87 @CellCellPress Bo, Thanks for sharing!

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over 1 year ago

LUMI-lab: A foundation model-driven autonomous platform enabling discovery of new ionizable lipid designs for mRNA delivery ;url=https://t.co/L4LXzKLag5;

Yuexulab retweeted

over 1 year ago

How can generative AI and Robotics help advance drug discovery？ 🚀 Excited to introduce LUMI-lab! A foundation model-driven Self-Driving Lab (SDL) for autonomous ionizable lipid discovery in mRNA delivery 🤖🔍 🔬 What is LUMI-lab? LUMI-lab integrates molecular foundation models with autonomous robotic experiments to efficiently explore new LNPs (lipid nanoparticles, mRNA delivery vehicles) with minimal wet-lab data. 🔥 Key Highlights: - 🧠 Foundation model trained on 28M molecules using a three-step strategy: - Unsupervised pretraining to capture broad molecular knowledge - Continual pretraining to specialize in lipid-like molecules - Active learning fine-tuning within a closed-loop experimental system - 🤖 1,700+ new LNPs synthesized & tested across 10 iterative cycles - 🧪 Brominated lipids autonomously identified as a novel structural feature that enhances mRNA transfection—an insight previously unrecognized in LNP design - 🏆 20.3% in vivo CRISPR gene editing efficiency in lung epithelial cells—the highest reported for inhaled LNPs 🚀 Why it matters? LNPs are the backbone of mRNA therapeutics, yet discovery has been slow due to data scarcity. LUMI-lab shows that AI-powered autonomous labs can accelerate mRNA delivery innovation🚀💡 🌐 Beyond mRNA drugs, LUMI-lab exemplifies a scalable framework for AI-driven molecular discovery, pushing boundaries in material science & drug delivery. 📜 Read the preprint: 🔗 https://t.co/bezIekapkn 💻 Code available on GitHub: 🔗 https://t.co/g1Cah59BuR #AI #DrugDiscovery #mRNA #LNP #SyntheticBiology 🙏 A huge team effort behind this work, with special appreciation to @BowenLi_Lab for driving the project. Kudos to @HAOTIANCUI1, @YueXu1995, @KKuanPang, @Gen_Li_Reagan, and @GongFangli36418!

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Yuexulab retweeted

about 2 years ago

Thrilled to announce that our AGILE framework, an AI-guided LNP development for mRNA delivery, is now published in @NatureComms! 🎉 This is a collaborative work with @BowenLi_lab. (Re-)Introducing the AGILE Platform: A Deep Learning-Powered Approach to Accelerate LNP Development for mRNA Delivery! This groundbreaking platform is revolutionizing mRNA therapy with #AI and combinatorial chemistry. **Highlights:** 1. Synthesized a huge chemical diversity of ionizable lipids using the Ugi three-component reaction, enabling unprecedented molecular variety. ⚛️ 2. Utilized self-supervised learning on a vast virtual library of 60k lipids to equip the AGILE model with a broad understanding of lipid characteristics, followed by supervised fine-tuning on 1.2k experimental data points from @BowenLi_lab, refining the model's capability to pinpoint high-potential candidates. This approach combines the best of computational and experimental insights. 🧪 3. Performed comprehensive in silico lipid screening with the AGILE model, allowing for precise and efficient identification of optimal lipid candidates. 🧠 4. Adaptable to various cell lines, ensuring broad applicability across different therapeutic contexts. This versatility makes AGILE a powerful tool for a wide range of mRNA delivery applications. 🧬 AGILE dramatically accelerates the development process, reducing timelines from months or even years to mere weeks! ⏱️ This rapid advancement opens up new possibilities for mRNA-based therapies, potentially leading to quicker responses to emerging health challenges. 📚 Access the paper on Nature Comm: https://t.co/KJXodcBK7x Code: https://t.co/JNtmE0MCTy This breakthrough exemplifies the platform's potential to drive innovation and enhance the efficacy of mRNA delivery systems. Shoutout to the three amazing students York (@YueXu1995), Rex (@RexMa9) and Haotian (@HAOTIANCUI1 ) who co-lead this project!🧑‍🎓 Join us in celebrating this leap forward in mRNA delivery technology! The future of medicine is here, and it’s powered by cutting-edge AI and pioneering scientific collaboration. #mRNA #LNP #AI #CombinatorialChemistry #Biotechnology @UHN_Research @researchuoft @VectorInst @PMResearch_UHN @UHNAIHUB

BoWang87's tweet photo. Thrilled to announce that our AGILE framework, an AI-guided LNP development for mRNA delivery, is now published in @NatureComms! 🎉 This is a collaborative work with @BowenLi_lab.

(Re-)Introducing the AGILE Platform: A Deep Learning-Powered Approach to Accelerate LNP Development for mRNA Delivery! This groundbreaking platform is revolutionizing mRNA therapy with #AI and combinatorial chemistry.

**Highlights:**
1. Synthesized a huge chemical diversity of ionizable lipids using the Ugi three-component reaction, enabling unprecedented molecular variety. ⚛️

2. Utilized self-supervised learning on a vast virtual library of 60k lipids to equip the AGILE model with a broad understanding of lipid characteristics, followed by supervised fine-tuning on 1.2k experimental data points from @BowenLi_lab, refining the model's capability to pinpoint high-potential candidates. This approach combines the best of computational and experimental insights. 🧪

3. Performed comprehensive in silico lipid screening with the AGILE model, allowing for precise and efficient identification of optimal lipid candidates. 🧠

4. Adaptable to various cell lines, ensuring broad applicability across different therapeutic contexts. This versatility makes AGILE a powerful tool for a wide range of mRNA delivery applications. 🧬

AGILE dramatically accelerates the development process, reducing timelines from months or even years to mere weeks! ⏱️ This rapid advancement opens up new possibilities for mRNA-based therapies, potentially leading to quicker responses to emerging health challenges.

📚 Access the paper on Nature Comm: https://t.co/KJXodcBK7x

Code: https://t.co/JNtmE0MCTy

This breakthrough exemplifies the platform's potential to drive innovation and enhance the efficacy of mRNA delivery systems.

Shoutout to the three amazing students York (@YueXu1995), Rex (@RexMa9) and Haotian (@HAOTIANCUI1 ) who co-lead this project!🧑‍🎓

Join us in celebrating this leap forward in mRNA delivery technology! The future of medicine is here, and it’s powered by cutting-edge AI and pioneering scientific collaboration. #mRNA #LNP #AI #CombinatorialChemistry #Biotechnology

@UHN_Research @researchuoft @VectorInst @PMResearch_UHN @UHNAIHUB

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Yuexulab retweeted