AI in Molecular Prediction

OrbitAll: A Unified Quantum Mechanical Representation Deep Learning Framework for All Molecular Systems Accurately modeling chemical systems across diverse charges, spin states, and environments remains a central challenge in molecular machine learning. No existing machine learning–based methods can simultaneously handle molecules with varying charges, spins, and environments. A few recently developed approaches address one or two of these factors individually by designing task-specific architectures, but this limits their applicability to broader chemical scenarios. OrbitAll is the first deep learning-based method that can simultaneously incorporate spin, charge, and environmental information using consistent and physically grounded quantum mechanical features. It has superior accuracy, generalization, and data efficiency on diverse chemical systems. We introduce a unified quantum mechanical representation that naturally incorporates spin, charge, and environmental effects within a single, physics-informed framework. Specifically, OrbitAll utilizes spin-polarized orbital features from the underlying quantum mechanical method, and combines it with graph neural networks satisfying SE(3)-equivariance. This enables our model, OrbitAll, to achieve accurate, robust, and data-efficient predictions across a wide range of chemical systems–including charged and open-shell species, as well as solvated molecules–without the need for domain-specific tuning. OrbitAll achieves chemical accuracy using 10 times fewer training data than competing AI models, with a speedup of more than thousand times compared to density functional theory. It can extrapolate to molecules more than 10times larger than those in training data. This universality distinguishes our approach from current deep learning models.

Big breakthrough: A few months my lab at MIT introduced SPARKS, our autonomous scientific discovery model. Since then we have demonstrated applicability to broad problem spaces across domains from proteins, bio-inspired materials to inorganic materials. SPARKS learns by doing, thinks by critiquing itself & creates knowledge through recursive interaction; not just with data, but with the physical & logical consequences of its own ideas. It closes the entire scientific loop - hypothesis generation, data retrieval, coding, simulation, critique, refinement, & detailed manuscript drafting - without prompts, manual tuning, or human oversight. SPARKS is fundamentally different from frontier models. While models like o3-pro and o3 deep research can produce summaries, they stop short of full discovery. SPARKS conducts the entire scientific process autonomously, generating & validating falsifiable hypotheses, interpreting results & refining its approach until a reproducible, fully validated evidence-based discovery emerges. This is the first time we've seen AI discover new science. SPARKS is orders of magnitude more capable than frontier models & even when comparing just the writing, SPARKS still outperforms: in our benchmark evaluation, it scored 1.6× higher than o3-pro and over 2.5× higher than o3 deep research - not because it writes more, but because it writes with purpose, grounded in original, validated compositional reasoning from start to finish. We benchmarked SPARKS on several case studies, where it uncovered two previously unknown protein design rules: 1⃣ Length-dependent mechanical crossover β-sheet-rich peptides outperform α-helices—but only once chains exceed ~80 amino acids. Below that, helices dominate. No prior systematic study had exposed this crossover, leaving protein designers without a quantitative rule for sizing sheet-rich materials. This discovery resolves a long-standing ambiguity in molecular design and provides a principle to guide the structural tuning of biomaterials and protein-based nanodevices based on mechanical strength. 2⃣ A stability “frustration zone” At intermediate lengths (~50- 70 residues) with balanced α/β content, peptide stability becomes highly variable. Sparks mapped this volatile region and explained its cause: competing folding nuclei and exposed edge strands that destabilize structure. This insight pinpoints a failure regime in protein design where instability arises not from randomness, but from well-defined physical constraints, giving designers new levers to avoid brittle configurations or engineer around them. This gives engineers and biologists a roadmap for avoiding stability traps in de novo design - especially when exploring hybrid motifs. Stay tuned for more updates & examples, papers and more details.

🧬 AI just wrote the code for living organisms and they actually work. Researchers at Arc Institute and Stanford have achieved something unprecedented: using genome language models Evo 1 and Evo 2 to generate 16 viable bacteriophage genomes from scratch the first time AI has designed complete, functional genomes that work in the real world. Think about that for a moment. Not just designing a protein. Not simulating a genome on a computer. Actually creating living viral systems with substantial evolutionary novelty that infect bacteria and replicate successfully. Here's what makes this revolutionary: In 1977, ΦX174 was the first genome ever sequenced. In 2003, it was the first genome chemically synthesized. Now in 2025, it's the template for the first AI-generated genomes. We've gone from reading DNA, to writing it, to designing it. The results? Several AI-generated phages outperformed the wild-type virus, with one variant called EVO-Φ69 being 65x more powerful than natural viruses. One even uses an evolutionarily distant DNA packaging protein that researchers wouldn't have rationally designed. The implications are staggering: → Cocktails of these generated phages rapidly overcome antibiotic-resistant bacteria → Accelerated development of phage therapies for drug-resistant infections → A blueprint for designing synthetic biological systems at genome scale → Foundation for creating useful living systems with entirely novel capabilities This isn't science fiction anymore. AI models trained on 2 million bacteriophage genomes can now propose new genetic codes—and 16 out of 302 designs actually worked MIT Technology Review. We're watching the birth of generative biology in real-time. The ability to design life at the genomic level opens possibilities we're only beginning to imagine. What do you believe the opportunities and risks are of this virus making AI? The conversation is just beginning. 📄 Study: https://lnkd.in/ddP3Fjdp #SyntheticBiology #AI #GenerativeAI #Genomics #Biotechnology #Innovation #Science

For 50 years, a key protein behind heart disease, among the leading cause of death worldwide remained a scientific mystery. It was too large and complex for traditional methods; its structure was invisible to us. Now, researchers have combined cryo-electron microscopy with DeepMind's AlphaFold to reveal the atomic structure of that protein: apoB100, the very scaffold of "bad cholesterol." This marks a deeper shift in how we approach science.  When we can see biology at this level of detail, healthcare moves from managing symptoms to engineering interventions at the molecular root. AI starts to function as a new kind of microscope, one that reveals the invisible machinery of life and allows entirely new questions to be asked. This is the kind of progress that matters.     AI as an instrument for understanding, precision, and prevention. It’s a glimpse into a future where compute and science converge to tackle humanity’s hardest health challenges at their source.    Read the full story: https://lnkd.in/gbum2dKu #AIInHealthCare #AIForGood

I’m often asked where I see AI make a tangible, real impact in the world today. To that, I answer with #AlphaFold, the revolutionary AI model from Google DeepMind, that is able to predict the structure of a protein simply from its amino acid sequence. 5 years ago, AlphaFold solved the 50-year grand challenge of protein folding, followed by the equally meaningful decision to make 200 million protein structures freely available to the scientific community. Since then, Demis Hassabis and John Jumper have been recognized with a Nobel Prize for their work on AlphaFold, and we see over 3.3 million users of it globally, with more than a third of users right here in Asia-Pacific. Here is just a snapshot of those applications: 🔬 Dr. Su Datt Lam at the National University of Malaysia (UKM) is learning more about Melioidosis to better fight the silent killer. 🧬 Researchers Lim Jackwee lim and Yinxia Chao at Singapore’s A*STAR - Agency for Science, Technology and Research and National Neuroscience Institute (NNI) are visualizing proteins linked to Parkinson’s. 🔍 Professor Ji-Joon Song’s team at the Korea Advanced Institute of Science and Technology lead to cancer and other diseases. 🪢Dr. Danny Hsu at Academia Sinica, Taiwan is advancing our understanding of exceptionally complex protein “knots”. ♨️ Dr. Syun-ichi Urayama’s team is uncovering new evolutionary insights from microbes in Japan’s hot springs! Listen to one of their stories below, and read more about all of them here: https://lnkd.in/d7wyACpK #GoogleDeepMind #AIforGood 

GENERATIVE BIOLOGY AI just wrote genetic instructions that cells actually followed – a breakthrough that turns biology into a programming language. For the first time ever, researchers at the Center for Genomic Regulation created AI-generated DNA sequences that successfully controlled gene expression in healthy mammalian cells. Think of it as writing software, but for living organisms. Why this matters: → The AI can design custom 250-letter DNA fragments with specific instructions like "activate this gene in stem cells becoming red blood cells but not platelets" → These synthetic enhancers worked EXACTLY as predicted when tested in mouse blood cells → Unlike previous efforts focused on cancer cells, this team worked with healthy cells, uncovering subtle mechanisms that shape our immune system → The researchers built a library of 64,000+ synthetic enhancers tested across seven stages of blood cell development Most fascinating was discovering "negative synergy" - where two factors that individually activate genes can completely shut them down when combined. This unlocks precision we never had before. The implications are enormous for gene therapy. Instead of being limited to DNA sequences evolution produced, we can now design ultra-selective gene switches customized to specific cells and tissues - potentially making treatments more effective with fewer side effects. Full paper: https://lnkd.in/en3bGZP9 Follow-up with @EricTopol's post about curing rare diseases with the existing genomic technology stack https://lnkd.in/eGCYMjGJ

Just finished reading Google DeepMind's AlphaGenome pre-print - impressive research that builds on their AlphaFold. If you recall, #AlphaFold uses AI to predict protein structures from amino acid sequences. #AlphaGenome is its complementary counterpart, tackling an equally complex challenge: predicting DNA regulation - essentially when and where genes get turned on or off. Previous tools faced a fundamental trade-off: analyze large DNA regions but miss fine details, OR examine precise details but miss the bigger picture. AlphaGenome solves this by processing 1-million-letter DNA sequences while maintaining single-letter precision - like having both satellite and ground-level weather monitoring simultaneously. The results are impressive: 0.8-0.85 correlation with experimental data (vs. ~0.7 for previous methods) and outperforming specialized models on 22 of 24 tasks while predicting 11 different biological processes simultaneously. While it’s probably accurate to call this solid engineering progress rather than a revolutionary breakthrough (gene regulation remains as chaotic as weather prediction) it gives researchers better tools to move from statistical correlations in genetic studies to more testable biological hypotheses. For drug discovery and understanding genetic disease, this could accelerate the path from "we found a genetic association" to "here's how it actually works biologically. Once again, I am impressed by Demis Hassabis and the crew at DeepMind's contributions to advancing medical research. Here is the full pre-print: https://lnkd.in/eYuuibQz

AI Tool from Mayo Clinic Identifies 9 Types of Dementia with One Scan, Boosting Speed and Accuracy of Diagnosis: 🧠Mayo Clinic has developed an AI tool called StateViewer that can identify nine types of dementia, including Alzheimer’s, from a single FDG-PET scan 🧠 In testing, StateViewer correctly identified the dementia type in 88% of cases and helped clinicians analyze scans twice as fast, with up to 3x greater accuracy than standard workflows 🧠 The AI was trained on over 3,600 scans from both patients with dementia and people without cognitive issues, allowing it to detect subtle brain activity patterns linked to specific dementia types 🧠 The tool compares how the brain uses glucose for energy against a large database of confirmed diagnoses, pinpointing activity patterns tied to memory, attention, movement, language, and behavior 🧠 Color-coded brain maps help explain the AI’s interpretation to all clinicians, including non-specialists, potentially expanding diagnostic access beyond top neurology centers 🧠 Accurate early diagnosis is essential as new treatments emerge, especially when multiple brain conditions overlap and symptoms are complex or misleading #digitalhealth #ai

AI and Protein Localization! 🧬🔬 The journey from understanding protein structures to predicting their precise locations within cells has taken a monumental leap forward. Introducing ProtGPS, a cutting-edge machine-learning model developed by researchers at the Whitehead Institute and Massachusetts Institute of Technology's CSAIL, led by Professor Richard Young and his team. Why is this a game-changer? 🤔 🔹 Predictive power: ProtGPS accurately forecasts where proteins will localize in cells, crucial for understanding both their functions and the mechanisms of diseases. 🔹 Disease insight: By examining over 200,000 proteins with disease-associated mutations, ProtGPS uncovers profound links between mis-localization and disease, paving the way for novel therapeutic strategies. 🔹 Generative potential: Beyond predictions, ProtGPS creates new proteins, designing sequences to target specific cellular locales. This innovation could revolutionize drug design by enhancing precision and minimizing side effects. 🔹 Experimental validation: Unlike many AI models, ProtGPS's predictions have been validated in real cell experiments, bridging the gap between computational design and biological application. The potential applications? Endless.....From developing targeted therapies to uncovering fundamental cellular mechanisms, the implications of this research are vast. ProtGPS isn't just a tool; it’s the start of a new era in biological exploration and therapeutic innovation. #AI #MachineLearning #Biotechnology #Proteomics #ResearchInnovation #Therapeutics #MIT #WhiteheadInstitute

Five years ago, AlphaFold solved the protein structure prediction problem at CASP14, cracking a 50-year grand challenge in biology. It has been an absolute honour and privilege to have been part of this journey alongside Demis and John. Over 3 million researchers across 190 countries have since used AlphaFold to predict the structure of more than 200 million proteins. The impact spans from revealing apoB100's structure, advancing heart disease research, to supporting endangered honeybee conservation in Europe. Protein structure prediction was the root node problem in structural biology. By solving it, we opened up entirely new avenues for discovery. What AlphaFold demonstrated is that AI can accelerate scientific progress when applied to the right foundational challenges. We've since expanded this approach across biology. AlphaMissense and AlphaGenome are helping researchers understand genetic mutations and disease. AlphaProteo is designing new protein binders for targets in cancer and diabetes. We're applying similar thinking to challenges in fusion energy, materials discovery and climate science. Today, we're sharing The Thinking Game, following our team through the journey that made AlphaFold possible. To understand more about AlphaFold's impact, see the blog here: https://lnkd.in/eiPSAeKc #AlphaFold #AIforScience