From Prediction to Simulation: AlphaFold 3 as a Differentiable Framework for Structural Biology 1. AlphaFold 3 represents a significant leap in computational biology, transforming protein structure prediction into a differentiable simulation process. This innovation bridges the gap between static structural modeling and dynamic molecular simulations, offering a new paradigm for understanding protein dynamics and interactions. 2. The core advancements of AlphaFold 3 include multi-scale transformer architectures, biologically-informed cross-attention mechanisms, and geometry-aware optimization strategies. These features enhance predictive accuracy and generalization across diverse protein families, surpassing previous methods. 3. The multi-scale transformer architecture captures protein features at multiple spatial resolutions, from local secondary structures to global quaternary arrangements. This hierarchical design improves accuracy and efficiency, particularly for large multi-domain proteins. 4. Biologically-informed cross-attention mechanisms integrate evolutionary and biophysical priors directly into the model. By incorporating information from sequence alignments, structural templates, and known motifs, these mechanisms guide the model toward biologically plausible conformations, enhancing interpretability and robustness. 5. Geometry-aware loss functions ensure that predicted structures obey fundamental geometric and energetic rules of protein folding. This approach reduces the hypothesis space, improves physical realism, and accelerates training and generalization. 6. AlphaFold 3’s differentiable simulation paradigm allows for continuous, gradient-based optimization of molecular conformations and interactions. This enables the modeling of folding pathways, conformational ensembles, and protein–ligand interactions, with significant implications for drug discovery and precision medicine. 7. The applications of AlphaFold 3 are far-reaching, including accelerating drug lead optimization, facilitating enzyme design, and modeling patient-specific mutant proteins. This integrated approach promises substantial impacts on precision medicine and rational drug discovery. 📜Paper: arxiv.org/abs/2508.18446v1 #AlphaFold3 #ComputationalBiology #StructuralBiology #DifferentiableSimulation #ProteinFolding #DrugDiscovery #PrecisionMedicine

Meet us this week at #CORL2024 in Munich! We will present several papers on #DifferentiableSimulation, #eventcameras, and #ReinforcementLearning at the main conference and workshops! Full list with times, rooms, and links to PDFs, Code, and Videos at: docs.google.com/document/d/1… With Jiaxu Xing, Yunlong Song, Anish Bhattacharya, Elie Aljalbout! @jixing24 @realyunlong @EliJalbout @anishb1010

Congratulations to my student Yunlong Song @realyunlong for successfully defending his Ph.D. in “Learning Robot Control: From #ReinforcementLearning to #DifferentiableSimulation”! Many thanks to the external reviewers Yuke Zhu @yukez from the U. of Texas at Austin, Marco Hutter from ETH Zurich @leggedrobotics, and Martin Riedmiller from @GoogleDeepMind! Yunlong's contributions are: - To show that Reinforcement Learning (RL) outperforms Optimal Control in autonomous racing because it directly optimizes a non-differentiable task-level objective. - To propose a policy-search-for-model-predictive-control (MPC) framework, combining RL's ability to optimize high-level task objectives with MPC's precise actuation and constraint handling. - To introduce a differentiable simulation framework to leverage robot dynamics for more stable and efficient policy training. - To develop a high-performance drone racing system that outperforms optimal control methods and professional pilots. - To develop Flightmare, a flexible modular quadrotor simulator for reinforcement learning and vision-based flight. - Video Recording of the PhD defense: youtu.be/DskNmbKzwf0 - Yunlong's webpage (publications, source code, slides): yun-long.github.io/ - Google Scholar: scholar.google.com/citations… Congratulations, Yunlong; it has been an honor to work with you! @UZH_en @UZH_Science @UZHspacehub @ERC_Research @nccrrobotics

A new blog diving into #DifferentiableSimulation #OptimalControl #AI. Explore our work on "Improving Gradient Computation for Differentiable Physics Simulation with Contacts" in this enlightening post by my collaborator @ZhongDesmond: desmondzhong.com/blog/2023-i…