Chris Myers 💻🧬🧪 ☕ ℏ⁻³/N!∬exp(-βĤ) d𝓅 d𝓺

This is a great analogy, @ylecun!

#compchem Despite #AI, characterizing dynamic interactions in realistic biological environments remains a massive computational bottleneck in modern #drugdiscovery. This perspective identifies the integration of #HPC, #machinelearning & #QuantumComputing as a definitive strategy to adress this issue. 🔬 𝐂𝐨𝐫𝐞 𝐒𝐜𝐢𝐞𝐧𝐭𝐢𝐟𝐢𝐜 𝐂𝐨𝐧𝐭𝐫𝐢𝐛𝐮𝐭𝐢𝐨𝐧𝐬 ▸𝐅𝐞𝐍𝐍𝐢𝐱-𝐁𝐢𝐨𝐥 𝐅𝐨𝐮𝐧𝐝𝐚𝐭𝐢𝐨𝐧 𝐌𝐨𝐝𝐞𝐥: A family of Machine Learning models engineered for high-speed, reactive atomistic #moleculardynamics simulations, achieving quantum-level precision with #GPU-accelerated scalability. ▸𝐇𝐢𝐠𝐡-𝐀𝐜𝐜𝐮𝐫𝐚𝐜𝐲 𝐐𝐮𝐚𝐧𝐭𝐮𝐦 𝐀𝐥𝐠𝐨𝐫𝐢𝐭𝐡𝐦𝐬: we introduce new approaches to extent the 𝐈𝐠𝐧𝐢𝐬 quantum chemistry database used for FeNNix-Bio: from improved VQE for #NISQ to innovative #FTQC building blocks. Our hybrid methodology utilizes a classical embedding "shortcut" to reach the Complete-Basis-Set limit (CBS). Using less qubits, we achieve chemical accuracy that would typically require 300+ qubits, up to a 10-fold reduction in resource overhead. ▸𝐁𝐫𝐢𝐝𝐠𝐢𝐧𝐠 𝐭𝐡𝐞 𝐓𝐢𝐦𝐞𝐬𝐜𝐚𝐥𝐞 𝐆𝐚𝐩: 𝐂𝐥𝐚𝐬𝐬𝐢𝐜𝐚𝐥 𝐄𝐧𝐡𝐚𝐧𝐜𝐞𝐝 𝐒𝐚𝐦𝐩𝐥𝐢𝐧𝐠. We introduce accelerators for drug binding in proteins: Lambda-ABF-OPES & Dual-LAO for absolute/relative free energies of binding, provide up to a 30-fold speedup over standard protocols. 𝐐𝐮𝐚𝐧𝐭𝐮𝐦-𝐄𝐧𝐡𝐚𝐧𝐜𝐞𝐝 𝐒𝐭𝐚𝐭𝐢𝐬𝐭𝐢𝐜𝐚𝐥 𝐌𝐞𝐜𝐡𝐚𝐧𝐢𝐜𝐬. We leverage quadratic speedups for sampling & highlight an experimental milestone: the realization of quantum Markov Chain Monte Carlo on Quantinuum’s H2/Helios #QPU. ▸📷 𝐓𝐡𝐞 𝐇𝐲𝐩𝐞𝐫𝐢𝐨𝐧 𝐐𝐮𝐚𝐧𝐭𝐮𝐦 𝐏𝐥𝐚𝐭𝐟𝐨𝐫𝐦 Our GPU-accelerated emulator/orchestrator enables custom kernels, specifically engineered for efficient sparse matrix–sparse vector multiplication & use of NVIDIA cuTensor for: - 𝐒𝐮𝐩𝐞𝐫 𝐅𝐚𝐬𝐭 𝐄𝐱𝐚𝐜𝐭 𝐒𝐭𝐚𝐭𝐞-𝐕𝐞𝐜𝐭𝐨𝐫 & 𝐌𝐚𝐭𝐫𝐢𝐱 𝐏𝐫𝐨𝐝𝐮𝐜𝐭 𝐒𝐭𝐚𝐭𝐞𝐬 (𝐌𝐏𝐒) 𝐄𝐦𝐮𝐥𝐚𝐭𝐢𝐨𝐧: 30++ logical qubits & 10,000++ CNOT gates on GPUs enabling to efficiently trigger VQE/QPE at full accuracy. - 𝐇𝐚𝐫𝐝𝐰𝐚𝐫𝐞-𝐀𝐠𝐧𝐨𝐬𝐭𝐢𝐜 𝐐𝐏𝐔 𝐎𝐫𝐜𝐡𝐞𝐬𝐭𝐫𝐚𝐭𝐢𝐨𝐧: real-life benchmarks across IBM Heron, Quantinuum H2/Helios, IonQ Aria/Forte & Pasqal technologies. Thanks to all co-authors including those on X: @Narjes_Ansari @ChemCesar @loco_daniele @FefeAviat @blazhynska66497 @Dr_DiataTraore @NicolaiGouraud @Thomas__Ple

🧬 We're moving from a screening-driven to a generative design-driven paradigm in computational biology. Introducing Proteina-Complexa: the first framework to apply inference-time compute scaling to atomistic design at this scale. 🧵👇

@Meta_Myself @therobbieharvey why?

@therobbieharvey Should be under ten years.

18-Year-Old Caden James Fontenette pleaded guilty to aggravated robbery and got the sentence from Judge Raquel West in Jefferson County's 252nd District Court back on March 10th. According to the Jefferson County District Attorney's office, he has to serve at least half that time before he can even be considered for parole since a deadly weapon was involved in the crime. The whole thing went down just before 1:20 a.m. on October 10, 2025, at the Bulldog Convenience Store on Twin City Highway at Helena Avenue. Fontenette and a couple other teens from Port Arthur hit the place. One of them waved around an assault-style rifle and used it to hit the clerk while they grabbed stuff, all caught on the store's surveillance cameras. When Nederland police got there, the group jumped into two cars and took off. They missed a turn, crossed a grassy spot and slammed into a concrete median. Two juveniles got arrested right at the crash, and Fontenette plus one more were picked up after a foot chase from the store or shortly after.

A non-hyped explainer of the “cell simulation” paper. The recent study about the “4D” simulation of a minimal cell has been getting a lot of attention on social media. Unfortunately, most posts about it have serious errors. I’ve seen people claim that the model simulates every chemical reaction in the cell, for example, which is not true. Some biomolecules and reactions *are* tracked individually in the simulation, including proteins and RNA (and ribosomes), and the chromosome. But the simulation does not track individual metabolites (like ATP or glucose), water, nucleotide precursors, lipds, and so on. These "other" molecules are represented, instead, as concentrations (using ordinary differential equations). But anyway, here goes my quick explanation: Researchers built a computational model that simulates roughly 100 minutes of biological time, or one cell division, for a single bacterial cell. Each simulation takes 4–6 days to run on two NVIDIA A100 GPUs, and the authors ran it 50 times in replicate. The cell simulation includes some elements of randomness, so each replication attempt leads to a slightly different outcome. When they plotted out these replicates and averaged results, they found that the model could predict a few things without being fitted to experimental data: The simulated cells “divided” every 105 minutes, on average, which matches experimental results; and the mRNA molecules had an average half-life of 3.63 minutes, which is roughly what we’d expect from experiments, too. The cell they are modeling is called JCVI-syn3A, and it is not a naturally-occurring organism. It’s a bacterium that has been engineered, over many years, to have a small genome. It only has 493 genes (compared to 4,000+ for E. coli), all of which are housed on a single chromosome. The Syn3A cell was made by taking a natural organism, called Mycoplasma mycoides, and then slashing out non-essential genes. Its entire proteome, transcriptome, and metabolism have been studied in depth, which is why it’s being used to build these whole-cell simulations. The actual *simulation*, though, is not a single thing! Instead, the authors wrote down all the “stuff” that happens inside a cell (transcription! translation! metabolism! lipid biosynthesis!) and decided which type of mathematical model would be best-suited to describe each thing. Some cell processes were modelled deterministically, others had “spatial” elements, and other parts were relatively random. More specifically, they used four different types of models to build this simulation: 1. A Reaction-Diffusion Master Equation, which was used to model the individual proteins, RNAs, and ribosomes. 2. A Chemical Master Equation, which was used to model things where spatial location doesn’t matter as much (it basically treats the whole cell as one mixed entity); including tRNA charging. 3. Ordinary Differential Equations, which you may be familiar with from Calculus class, were used to model changes in ATP concentration, lipids, and so on. 4. Brownian Dynamics, which simulated the chromosome as a physical chain of beads, where each bead represents 10 base pairs of DNA. The Reaction-Diffusion Equation works like this: Basically, they chopped up the entire digital cell into a 3D grid of cubes. Each cube measures 10 nanometers on each side. The whole cell is about 500 nanometers across, so there are tens of thousands of cubes in the cell's interior. (This is a useful way to coarse grain the simulation; if the cubes were smaller, the simulation would take much longer to run.) Each cube is a little box that contains some number of molecules. At every “step” in the simulation, only one of two things can happen to the molecules in each box: Either they react with a molecule in the same box, or they diffuse (“hop”) to an adjacent box. That’s it; the model is just rolling a die for each molecule at each time step in each box, and using those results to decide how each molecule changes over time. (The reason this spatial model is important is because biology only works if molecules physically bump into each other. And so this spatial grid means that, unlike simpler models, a protein actually has to “diffuse” across boxes in the cell to encounter its reaction partner; only then can it react and do something useful.) So anyway, each of these models is used to represent a different type of molecule. It’s not like there is a single, all-powerful simulation that they are running here; instead, they’re running these four models together, using a script that synchronizes their results with each other. The Reaction-Diffusion equation is the main part of the simulation. It takes time steps of 50 microseconds of biological time. Every 12.5 milliseconds of biological time — meaning every 250 RDME steps — the simulation pauses so that the other models can synchronize based on the latest state of the simulation. The Brownian Dynamics part runs on a completely separate GPU, and only updates every four seconds of biological time. So that's the gist here. But let's also be honest about what this simulation does NOT do: - It does not include polysomes, which are a cluster of ribosomes that all latch onto a single mRNA and translate at the same time. Polysomes are really common inside of cells, but this simulation assumes that each mRNA can only be translated by one ribosome at a time. - It does not include polycistronic transcription. In bacteria, genes are often grouped next to each other on the chromosome and thus “transcribed” (or turned into mRNA) all at once, together. The majority of genes in E. coli, for example, are arranged in these operons, and the authors of this paper acknowledge that many Syn3A genes are likely co-transcribed the same way. But the simulation doesn't capture it. - The authors manually tuned many parameters to get the model to make predictions that more closely resemble experiments. Earlier simulations were waaaayyyyy off from experimental results. For example, they adjusted the ratio of mRNA binding rates to ribosomes versus degradosomes because, in earlier simulations, mRNA was being degraded too quickly, before ribosomes could translate it, causing most proteins to be severely underproduced. - In the Brownian Dynamics model, the authors added a “fake” 12 pN physical force to push the two daughter chromosomes apart during division, because the real biological mechanism for chromosome partitioning in Syn3A is not known. - And some other things. That being said: This model is really cool! I love papers like this! I'm enamored by scientists who choose really difficult problems (like simulating an entire cell) and actually go after it and make progress! This paper is amazing because it shows us what we are able to simulate well, and what we don't yet understand, and to figure out which experiments we ought to perform to reconcile the two. So instead of framing this paper as "OH MY GOSH SCIENTISTS FIGURED OUT HOW TO SIMULATE AN ENTIRE CELL!" we should frame it as proof that there is still plenty of room at the bottom, many measurements to be made, and many avenues to explore as we seek to understand biology better.

Extremely excited to announce LigandForge 🧬⚡ Generate high-quality peptides at over 10,000x - 1M the speed of state-of-the-art methods like Bindcraft and Boltzgen. Predict binding affinity with 83% correlation to experimental binding data. 150 protein targets benchmarked.

Isomorphic Labs Revealed the Successor to AlphaFold 3! Isomorphic Labs officially introduced IsoDDE, a unified drug design engine that fundamentally outperforms AlphaFold 3 in real-world pharmaceutical applications. IsoDDE doesn't just predict protein structures; it tackles the hardest problem in the field—generalizing to completely novel, unseen biological systems. It doubles AF3's accuracy on complex protein-ligand structures, beats highly expensive physics models at predicting binding affinities, and can even instantly discover hidden "cryptic pockets" on proteins just by looking at their sequence!

🚨 We’re Hiring in Generative AI for Biomolecular Design! 🧬🤖 We’re looking for Research Scientists working on biomolecular design, including small molecules, proteins, RNA, and molecular dynamics. Join us to help shape the next generation of AI-driven discovery, expanding our frameworks (e.g., Proteina-Complexa, La-Proteina, ReaSyn, and GenMol) to new domains and problems. If you’re excited about pushing the frontier of generative AI for biology and chemistry, we’d love to hear from you. 🔗 Apply: nvidia.wd5.myworkdayjobs.com/en-US/NVIDIAEx… 🌐 Group page: research.nvidia.com/labs/genair/

@celine681743 @IanGee2023 @jk_rowling that's case-in-point of the problem...adults and parents have to be the adults in the room again, not being led by the very children they are tasked to lead

@IanGee2023 @jk_rowling you realise, he has to do this because one of his children claims he is trans :( poor david probably was lied as well and doesnt know how to come back

And it will forever be appalling to me that a bunch of celebrities with influence over young people used their platforms to amplify unevidenced claims for the benefit of transitioning minors while smearing those urging caution as bigots.

#compchem Our paper in J. Phys. Chem. Lett. (@JPhysChem): "Accelerating Molecular Dynamics Simulations with Foundation Neural Network Models using Multiple Time-Step and Distillation" made it to one of the covers! pubs.acs.org/doi/full/10.10…

Most recent paper out now at science advances. We investigated a BASEL phage belonging to the felixounavirus genus and discovered evolutionary relationships with distantly related phages. Collaborative work between @HiCryoEM and @mihnea_bostina science.org/doi/10.1126/sc…

A sneak peak of a complex and technically challenging experiment that my lab developed: doi.org/10.1096/fj.202…. Super proud of PhD candidate Hannah Johnson for showcasing our Whole-gut spatial genomic analysis in #zebrafish. This video illustrates one landmark in the protocol after multiple rounds of sequential #HCR and 3D imaging in zebrafish larvae to reveal spatial expression of numerous mRNAs in the same specimen. Data from these imaging data sets are then computationally analyzed for spatial cell groups, spatially variable genes, and differentially expressed genes along 3D. We are leveraging this systems-level SGA to uncover unappreciated mechanistic insight at the cell and tissue levels into #ENS construction. Stay tuned for our work that exploits this pipeline within various mutant and perturbation conditions. Reach out if you are interested in trying this! #fruitypebbles

@echo_4_joe @NEWSMAX apparently a lot

@NEWSMAX Anyone advocating for war should be closely scrutinized. I’m not saying she wrong. I’m saying what do they know that we don’t?

Former Secretary of State Condoleezza Rice urged President Donald Trump to "take care of Iran for good," praising "Operation Epic Fury," which resulted in the death of Iranian Supreme Leader Ali Khamenei. MORE: bit.ly/40cMVbm

@PatriotXV11 @megynkelly nah...just stupid

@megynkelly A wise Senator once said …

Actually you spat and sputtered and humiliated yourself but okay.

Nucleosome aficionados! Our new review "Nucleosome spacing across cell types, diseases, and ages" is out in NAR: academic.oup.com/nar/article/54… A huge effort to pull together what we’ve learned about nucleosome spacing in many systems. Enjoy! #nucleosomics #nucleosome #chromatin #cfDNA

@theliamnissan Where's the lie? You sound mad, bro?

Charlie Kirk dropped out of community college and then told millions of American youth that higher education is a liberal scam. His banner should NOT be hanging from the Dept of Education right now. Holy fuck

I tried very hard to understand the theory of chaos. Or nonlinear dynamics.. And

Since the year 2000, the world's green leaf area has increased by 5%. That increase is roughly 5.5 million square kilometers - equivalent to adding an entire Amazon Rainforest to the planet in just over two decades. NASA research indicates that this extra leaf cover acts as a natural air conditioner. At least 30% of the greened areas have seen a slight cooling effect due to the way leaves manage water vapor and air turbulence. While some models predicted the growth would stop, NASA found that even in intensively farmed lands, food production (grains, vegetables, fruits) has jumped 35–40% since 2000. It’s poetic irony that the Mycorrhizal networks under a single footstep in the Taiga can extend for miles, communicating at the speed of slow, pulsing chemical waves. If the greening is the visible manifestation of a living world, the soil biome is its conscious mind. While humans are distracted by the everyday business of combat, borders and running bureaucracies, the world beneath our feet is engaged in a massive, silent, and sophisticated cooperative economy. Earth is not a fragile machine we have to fix but a sophisticated, ancient intelligence that knows exactly how to spend its 'carbon capital.' The entire climate narrative then changes. It’s no longer about a Code Red of fear; it's about a deep green sense of wonder.

Electricity output is the single best proxy for industrial capacity

𝗪𝗵𝗮𝘁 𝗶𝘀 𝗰𝗼𝗺𝗽𝘂𝘁𝗮𝘁𝗶𝗼𝗻 𝗶𝗻 𝗱𝘆𝗻𝗮𝗺𝗶𝗰𝗮𝗹 𝘀𝘆𝘀𝘁𝗲𝗺𝘀? Interesting paper tackling this difficult question. Answer (in part): it's complicated! doi.org/10.1088/2632-0…

What a psychopath.