Therapeutics Data Commons
@ProjectTDC
Therapeutics Data Commons: Multimodal Foundation for Therapeutic Science, developed @Harvard
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Excited to share our new paper in Nature Chemical Biology @nchembio AI is poised to transform #therapeutic #science The Commons is an initiative to access and evaluate #AI capability across therapeutic modalities and stages of discovery 1/4 nature.com/articles/s4158…
Highlight of PDGrapher in @thecrimson nature.com/articles/s4155… Great work by @justguadaa Xiang Lin and collaboration with K. Veselkov and @mmbronstein
Researchers at Harvard Medical School developed a tool that uses artificial intelligence to accelerate drug discovery and development. Andrew R. Levy and Juliana L. Yao report. thecrimson.com/article/2025/1…
1⃣ more day⌛️ to submit your exciting research to our #NeurIPS workshop on AI Virtual Cells and Instruments! Submit an extended abstract & join our excellent lineup of presenters! 🌟 Please do not hesitate to reach out if you have any questions! ai4d3.github.io/2025/cfp.html
(1/6) We’re thrilled 🎉 to launch the #NeurIPS2025 Workshop on AI Virtual Cells and Instruments: A New Era in Drug Discovery & Development (AI4D3-2025) in San Diego, CA on December 6 or 7!🥳 🔗Workshop site: ai4d3.github.io
Update from CURE-Bench at @NeurIPSConf: 524 entrants and 298 submissions! Thank you to the CURE-Bench community! Working on AI for drug discovery and reasoning in medicine? Your agent belongs here New teams welcome. Tasks, rules, and leaderboard: curebench.ai…
🚀 260+ teams have entered the CUREBench @NeurIPSConf 2025 Challenge! 🚀 curebench.ai Challenge Tracks: • Track 1: AI models relying solely on built-in memory • Track 2: AI systems that leverage external tools and resources Evaluation is by agentic judges…
Pitch the dataset that could spark the next AI for Science revolution 🚀 The PDB revolutionized structural biology (and even helped win a🏅Nobel Prize in 2024). We’re hunting for the next breakthrough dataset that could unlock similar leaps across science—and we want your idea!
📣 Competition launch alert! CUREBench competition at @NeurIPSConf 2025 Start here: curebench.ai Benchmarking AI for therapeutic reasoning, drug discovery, treatment planning, and therapeutic decision-making 🎯 Track 1: Develop AI models that rely on parametric…
🌍 Excited to open up our global evaluation of AI for drug decision-making and therapeutic reasoning @GaoShanghua Want to shape the future of therapeutic AI, from understanding existing medicines to developing new treatments for diseases with limited options? Start here:…
📢 AI-enabled drug discovery reaches clinical milestone rdcu.be/eugUu Few AI-designed drug candidates have gone beyond in silico benchmarks. Now, a study in @NatureMedicine @biogerontology reports a successful phase 2a trial of rentosertib, an AI-discovered drug and…
🔍 Call for Reviewers: AI4D3@NeurIPS 2025 We're seeking experts in AI & drug discovery to review submissions for our NeurIPS workshop. 🗓️ Deadline: May 30, 2025 📍 Workshop: Dec 2025, San Diego ✅ Sign up: forms.gle/o3kWHdJ6nVfXhd… #AI4D3 #NeurIPS2025 #DrugDiscovery #AI
📢 🧬 New preprint! Can we predict which cancer patients will benefit, before treatment begins? @WanXiang_Shen Immunotherapy saves lives but many patients don’t respond to treatment, and we still lack reliable tools to predict who will benefit We introduce COMPASS, foundation…
New in the Kempner's Deeper Learning blog: @AdaFang_ and @marinkazitnik introduce ATOMICA, a geometric foundation model that enables reasoning about biological systems with atom-level precision. bit.ly/KempnerATOMICA #AI #ML #MedTech
📄 Preprint: biorxiv.org/content/10.110… 🚀 Open source code, weights, and data: github.com/mims-harvard/A…
ATOMICA characterizes interesting clusters of putative bacterial zinc fingers and cytochrome proteins. We're working on getting some of these validated in the lab 🧫👩🔬. Stay tuned!
Using masked token accuracy to proxy representation quality, we see training of ATOMICA follows scaling laws where representation quality improves with increasing biomolecular data modalities 📈
ATOMICA builds multi-scale representations at the atom, block (amino acid / nucleotide / common chemical motif), and interaction complex scale. 💡 The key is capturing *interaction complexes* - to learn patterns fundamental to chemistry, such as hydrogen bonds & pi-pi stacking.
Introducing ATOMICA 💫 A model to universally represent molecular interactions (for proteins, nucleic acids, small molecules, and ions) at an all-atom scale 🧵
DBMI's @zakkohane & @marinkazitnik, plus @AdamRodmanMD & other collaborators, on how AI is transforming medicine. “Having an instant second opinion... will change, for the better, the nature of the doctor-patient relationship.” news.harvard.edu/gazette/story/…
news.harvard.edu
How AI is transforming medicine — Harvard Gazette
Artificial intelligence is up to the challenge of reducing human suffering, Harvard Medical School experts say. Are we?
👉 Preprint: arxiv.org/abs/2503.10970 👉 TxAgent: zitniklab.hms.harvard.edu/TxAgent 👉 Open code and data: github.com/mims-harvard/T… 👉 ToolUniverse: github.com/mims-harvard/T… We are excited about the next steps as we work with clinical researchers, disease foundations, and patient-led…
TxAgent An AI Agent for Therapeutic Reasoning Across a Universe of Tools
Personalized therapy design in oncology MADRIGAL personalizes drug combination response using genomic profiles from leukemia patient samples and xenograft models Predicting drug safety and transporter interactions MADRIGAL identifies organ-specific toxicities and…
Predicting clinical outcomes of drug combinations from preclinical data is a major challenge @YepHuang We know a drug works in the lab. But will it work in patients? 🔬 ➡️ 🏥 This is key for safe and effective therapies and it's one of the hardest challenges in medicine.…
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