Jiarui Lu
61 posts
📢📢 Proteina-Complexa 📢📢 Atomistic Binder Design with Generative Pretraining and Test-Time Compute + Experimental Validation at Scale ⭐️ Project page (research.nvidia.com/labs/genair/pr…) for: 📜 Method paper (ICLR 2026 Oral) 🧬 Wet lab paper 🛠️ Code & models 📁 Data 🧵 Thread (1/n)
Apple (yes that Apple) released SimpleFold, a new Protein folding model in their paper titled "SimpleFold: Folding Proteins is Simpler than You Think" yesterday. With SimpleFold, Apple makes a case that specialised architecture is not required for protein folding, which is a departure from famous AlphaFold like methods. SimpleFold uses standard transformer blocks trained via a generative flow-matching. And similar to Protenix-mini does not require MSA, which makes it faster. We have integrated SimpleFold into OpenBio. Trying it out is as simple as asking OpenBio to predict the structure of your protein sequence. You can use it now and see how good SimpleFold is.
Apple (yes that Apple) released SimpleFold, a new Protein folding model in their paper titled "SimpleFold: Folding Proteins is Simpler than You Think" yesterday. With SimpleFold, Apple makes a case that specialised architecture is not required for protein folding, which is a departure from famous AlphaFold like methods. SimpleFold uses standard transformer blocks trained via a generative flow-matching. And similar to Protenix-mini does not require MSA, which makes it faster. We have integrated SimpleFold into OpenBio. Trying it out is as simple as asking OpenBio to predict the structure of your protein sequence. You can use it now and see how good SimpleFold is.
SimpleFold: Folding Proteins is Simpler than You Think "we introduce SimpleFold, the first flow-matching based protein folding model that solely uses general purpose transformer blocks. Protein folding models typically employ computationally expensive modules involving triangular updates, explicit pair representations or multiple training objectives curated for this specific domain. Instead, SimpleFold employs standard transformer blocks with adaptive layers and is trained via a generative flow-matching objective with an additional structural term."
New preprint & open-source! 🚨 “SimpleFold: Folding Proteins is Simpler than You Think” (arxiv.org/abs/2509.18480). We ask: Do protein folding models really need expensive and domain-specific modules like pair representation? We build SimpleFold, a 3B scalable folding model solely built on general-purpose transformers + flow matching, and is trained on 9M structures. SimpleFold supports easy deployment and efficient inference on consumer-level hardware with PyTorch/MLX (try it on your MacBook!) (1/n)
Apple (yes that Apple) released SimpleFold, a new Protein folding model in their paper titled "SimpleFold: Folding Proteins is Simpler than You Think" yesterday. With SimpleFold, Apple makes a case that specialised architecture is not required for protein folding, which is a departure from famous AlphaFold like methods. SimpleFold uses standard transformer blocks trained via a generative flow-matching. And similar to Protenix-mini does not require MSA, which makes it faster. We have integrated SimpleFold into OpenBio. Trying it out is as simple as asking OpenBio to predict the structure of your protein sequence. You can use it now and see how good SimpleFold is.
New preprint & open-source! 🚨 “SimpleFold: Folding Proteins is Simpler than You Think” (arxiv.org/abs/2509.18480). We ask: Do protein folding models really need expensive and domain-specific modules like pair representation? We build SimpleFold, a 3B scalable folding model solely built on general-purpose transformers + flow matching, and is trained on 9M structures. SimpleFold supports easy deployment and efficient inference on consumer-level hardware with PyTorch/MLX (try it on your MacBook!) (1/n)
Wrapping up #ICML2025 on a high note — thrilled (and pleasantly surprised!) to win the Best Paper Award at @genbio_workshop 🎉 Big shoutout to the team that made this happen! Paper: Forward-Only Regression Training of Normalizing Flows (arxiv.org/abs/2506.01158) @Mila_Quebec
What makes a great scientist? Most AI scientist benchmarks miss the key skill: designing and analyzing experiments. 🧪 We're introducing SciGym: the first simulated lab environment to benchmark #LLM on experimental design and analysis capabilities. #AI4SCIENCE #ICML25
