Mstack AI accepted at ICML conference

𝗘𝗾𝘂𝗶𝘃𝗮𝗿𝗶𝗮𝗻𝘁 𝗚𝗿𝗮𝗽𝗵 𝗡𝗲𝘂𝗿𝗮𝗹 𝗡𝗲𝘁𝘄𝗼𝗿𝗸𝘀 𝗳𝗼𝗿 𝟯𝗗 𝗠𝗮𝗰𝗿𝗼𝗺𝗼𝗹𝗲𝗰𝘂𝗹𝗮𝗿 𝗦𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲 Machine learning models often struggle with 3D biological data. Most networks fail when you rotate or move a molecule in space. This happens because the model does not understand 3D symmetry. Equivariant Graph Neural Networks solve this problem. These networks respect the physical properties of molecules. If you rotate a protein, the model output rotates with it. This creates better accuracy for molecular modeling. Key benefits: - Better handling of 3D geometric data. - Consistent predictions regardless of orientation. - Higher precision in biological structure analysis. Researchers use these models to understand how proteins function. This work speeds up drug discovery and structural biology. Read the full breakdown here: https://lnkd.in/gU3MUVfe Optional learning community: https://t.me/GyaanSetuAi

A thoughtful comment by James T. Metz, PhD on my previous post made me rethink a deeper question: When we represent molecules as graphs, surfaces, voxels, or fingerprints in AI, we often speak as if one representation is somehow “closer to reality.” But is it? Do we ever truly *see* a molecule? When an experimentalist measures a Kd, they observe a signal. When a crystallographer fits electron density, they infer a structure. When we train a GNN, we optimize a representation. None of these are the molecule itself. They are models. This reminded me of George Box’s famous idea: “All models are wrong, but some are useful.” So perhaps in drug discovery the question is not: ❌ Which representation is true? But: ✅ Which representation preserves the information needed for the decision we care about? A molecular graph is not reality. A docking pose is not reality. Experimental measurements are not reality either—they are observations filtered through methods and assumptions. Every representation introduces trade-offs. Every model carries inductive bias. Maybe progress in AI for drug discovery is not about seeing molecules more accurately— but about building representations that fail less often. Curious to hear your perspective: In computational biology, what do we actually optimize for— truth, prediction, interpretability, or utility? #DrugDiscovery #ComputationalBiology #RepresentationLearning #Bioinformatics #ScientificModeling #ArtificialIntelligence #MolecularAI

🔊Diffusion Models: Noise-to-Molecule Learning Diffusion models are a class of probabilistic generative techniques that synthesize new data by learning to reverse a progressive corruption process, iteratively refining random noise into structured and meaningful outputs. In recent years, these models have become a leading approach for molecular and protein engineering, enabling the generation of novel biomolecules with significant potential for drug discovery and therapeutic development But what makes them so remarkable? The Scientific Principle, Diffusion models are probabilistic generative models that learn how to reverse a gradual noise process. In the forward process, noise is progressively added to molecular structures until they become nearly random. The neural network is then trained to learn the reverse process, removing noise step by step to reconstruct chemically meaningful structures. Noise → Patterns → Molecular Fragments → Novel Drug Candidate Diffusion models learn the underlying probability distribution of molecular space and generate entirely new compounds by iterative denoising. Why Does This Matter? The number of potential drug-like molecules is estimated to exceed 10⁶⁰, making exhaustive exploration computationally impossible. Diffusion models provide a data-driven approach to navigate this vast chemical space and generate molecules with desirable biological and physicochemical properties. Why Are Diffusion Models Gaining Attention? ✓ Stable training dynamics compared with GANs ✓ High-quality and diverse molecular generation ✓ Capability to model complex 3D molecular geometries ✓ Improved chemical validity of generated compounds ✓ Property-guided design through conditional generation Representative Models 1. DiffDock for protein–ligand docking 2. GeoDiff for molecular conformer generation 3. RFdiffusion for protein design 4. Equivariant Diffusion Models (EDM) for 3D molecular structures Implications Diffusion models are transforming drug discovery from a process of screening existing compounds into one of designing entirely new therapeutic molecules. By learning to convert randomness into molecular order, these models are redefining how artificial intelligence can accelerate the discovery of next-generation medicines. Diffusion models introduce a fundamentally different idea: Instead of asking, Which molecule should we choose? we can now ask, Which molecule should we design? Diffusion models are not merely generating molecules—they are learning the language of chemistry itself. ✍🏻 Pranavi Uppuluri #GenAI #DrugDiscovery #DiffusionModels #ArtificialIntelligence #MachineLearning #DeepLearning #Bioinformatics #PharmaceuticalResearch #DrugDiscovery

🧬 AI is no longer just writing code—it’s accelerating breakthroughs in biology. Google DeepMind’s AlphaFold 3 is changing how researchers study life at the molecular level. Unlike earlier versions that focused mainly on protein structures, AlphaFold 3 can predict interactions between proteins, DNA, RNA, and small molecules with remarkable accuracy. Why does this matter for AI/ML professionals? ✅ It showcases how generative AI and diffusion models can solve complex scientific problems beyond text and images. ✅ It has the potential to dramatically shorten the early stages of drug discovery and biomedical research. ✅ It highlights a growing trend where AI becomes a core scientific collaborator rather than just a productivity tool. ✅ It reminds us that the next wave of AI innovation may come from healthcare, biotechnology, and materials science—not just chatbots. AlphaFold has already transformed structural biology, and its impact continues to grow as researchers worldwide use it to accelerate discoveries. It's a powerful example of AI creating measurable value in science. 0 As AI engineers and data scientists, this is a reminder that the most exciting AI applications may be solving problems we've struggled with for decades. 💬 If you had the opportunity, would you rather build the next LLM or the next AI system that helps discover life-saving medicines? #AI #MachineLearning #DeepLearning #AlphaFold3 #DrugDiscovery #Bioinformatics #DataScience #GoogleDeepMind #Innovation

🔬 Today's Top AI Research Highlights from ResearchFront 1. AI-Powered Digital Twins Are Moving Personalized Drug Dosing Closer to the Clinic A recent IEEE paper proposes an explainable generative AI framework that uses patient-specific modeling and digital twins to recommend individualized drug and dose choices. The approach combines EH... 2. Transformers Are Reshaping Automated Content Moderation Research for Toxic-Text Detection A recent comparative study shows that specialized transformer-based models outperform general-purpose large language models in automated toxicity detection, improving the reliability of content mod... 3. AI-Driven Generative Design Is Opening a Faster Path to Novel Metamaterials A recent paper introduces MetaSeq, a physics-guided, sequence-based generative framework for acoustic metamaterial inverse design. The approach uses generative modeling to propose new architectures... Covering Clinical Health, Social Sciences, Physical Sciences — curated from 3214 articles tracking how AI is transforming academic research. 📩 Get daily briefings: researchfront.xyz #AIResearch #MachineLearning #Science #AcademicAI #ResearchFront

Thursday Thoughts: Can Living Brain Cells Become the Next Computer? Imagine if I told you someone trained living human neurons to play Doom. Not a neural network. Not a simulation. Actual living neurons. That immediately made me stop scrolling. I recently came across Parasma, founded by Sean Cole (YC Summer 2026), and it's one of the most fascinating AI startups I've read about this year. Their mission is to build biological compute by writing algorithms that allow living neurons to perform useful computational work. Naturally, my first questions weren't about AI performance. Can these neurons think? Can they become conscious? Can they suffer? What I appreciated most was that Parasma didn't avoid those questions. They dedicated an entire section of their website to explaining how they think about consciousness and suffering. According to their research, the neural cultures they use don't have the biological structures associated with consciousness, such as sensory organs, pain receptors, self-awareness, or the large-scale brain architecture believed to support conscious experience. The neurons can learn and adapt, but learning isn't the same as consciousness. What really made me think wasn't just how they built it, it was why. As AI models become larger, so do the costs of training and running them. More GPUs. More electricity. More hardware. More cost. Parasma is asking a completely different question: What if biology could become the next generation of compute? Whether biological computing becomes mainstream or not, I think it's refreshing to see founders exploring completely different approaches instead of simply building larger models. I also appreciate that they're openly discussing ethics alongside capability, something I think will become increasingly important as AI continues to evolve. I'm genuinely curious to see where biological computing goes over the next few years. Remember, sometimes innovation isn't about building a faster computer, it's about completely rethinking what a computer can be. Resources: https://www.parasma.ai/ https://lnkd.in/gzPyG6yg #ThursdayThoughts #AI #ArtificialIntelligence #BiologicalComputing #Neuroscience #MachineLearning #ResponsibleAI #EmergingTech #YCombinator #FutureOfAI #Innovation

🔬 Today's Top AI Research Highlights from ResearchFront 1. AI Model Maps Pandemic Spillover Risk and Early-Warning Signals A recent review highlights how AI is being used to strengthen pandemic surveillance by combining early outbreak detection, predictive modeling, and health surveillance into faster decision-support ... 2. Machine Learning Is Opening New Paths in Art Provenance Research A recent study shows that machine learning can help art historians and conservators analyze authenticity-related markers in artworks, supporting provenance research with data-driven evidence. The w... 3. AI Translation Tools Are Reshaping Cross-Cultural Communication Research for International Students A recent IEEE study used text analysis of 80 social-media comments to identify what international students value most in AI translation tools. The findings show that translation accuracy and contex... 4. AI-Accelerated Molecular Dynamics Is Moving Simulations from Bottleneck to Breakthrough A recent IEEE study describes AccelMD, a self-adaptive AI-enabled framework that speeds up molecular dynamics simulations while preserving high predictive accuracy. The system achieved an average 4... 5. Quantum machine learning is speeding materials simulation by learning quantum behavior from data A recent study shows that machine-learning-accelerated Monte Carlo simulation can scale to billion-atom materials problems by combining GPU computing with machine-learning surrogates of density fun... Covering Clinical Health, Arts & Humanities, Social Sciences, Physical Sciences — curated from 3053 articles tracking how AI is transforming academic research. 📩 Get daily briefings: researchfront.xyz #AIResearch #MachineLearning #Science #AcademicAI #ResearchFront

🚨 The Black-Box AI Era is Over: Enter the Age of Explainable and Generative AI in Medical Diagnostics. I am excited to share my advanced research project in Medical AI, focused on the diagnosis of pneumonia. You can explore the platform and test the model on Hugging Face via the link in the comments 👇🏻. I welcome opportunities for research collaboration and interdisciplinary partnerships in Medical AI, Computer Vision, and Multimodal Intelligence with researchers, academic institutions, and industry teams. #MedicalAI #ArtificialIntelligence #ExplainableAI #ComputerVision #DeepLearning #VisionTransformer #HealthcareAI #GenerativeAI #Research #HuggingFace #Python #AIResearch #ClinicalAI #MultimodalAI

🚨 Happening later this week - 📅June 11🕙 10:00 a.m. ET | 4:00 p.m. CET Beyond Classical AI: Quantum Machine Learning for Anomaly Detection Across Industries is almost here, a live session built around a real use case powered by The Washington Institute for STEM, Entrepreneurship and Research × Fraunhofer ITWM. This is about moving beyond theory and into delivered impact. What to expect: ✅ A real-world quantum machine learning use case ⚙️ How anomaly detection was implemented 🎙️ You’ll hear from: - Dr. Pascal Halffmann, Research Coordinator for Quantum Computing at Fraunhofer ITWM - Vardaan Sahgal, Quantum Solutions Head at The Washington Institute for STEM, Entrepreneurship and Research If you work in quantum, AI, data science, or strategic tech evaluation, this is a session designed to give you clarity, not hype. #QuantumComputing #QuantumMachineLearning #AnomalyDetection #DeepTech

Radical Numerics has officially launched with a 50 million dollar seed round to pioneer what they define as General Biological Intelligence. Led by Radical Ventures, this significant investment marks a major milestone in the evolution of AI-driven scientific discovery. While the previous decade of artificial intelligence was largely defined by Large Language Models and digital data processing, Radical Numerics is shifting the focus toward the physical and biological world. Their mission is to build models that understand the fundamental principles of biology, enabling the design of novel biological systems and molecules with unprecedented precision. General Biological Intelligence represents a move toward AI that can reason across biological scales, from molecular interactions to complex cellular systems. This approach has the potential to fundamentally transform drug discovery, synthetic biology, and materials science by moving beyond trial-and-error methods toward a predictive, engineering-based framework. The scale of this seed round reflects the growing institutional confidence in AI for Science as the next frontier of technological innovation. As AI begins to master the language of biology, the boundary between computational design and physical reality continues to blur. Read the full announcement here: https://lnkd.in/dEtwVeFZ #AI #Biotechnology #GeneralBiologicalIntelligence #RadicalNumerics #TechBio #Innovation #ArtificialIntelligence #SeedFunding #DeepTech