Protein Structure Prediction

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Protein structure prediction aims to infer how amino-acid sequences form three-dimensional structures, which helps explain protein function and interactions. Approaches range from evolutionary information in multiple-sequence alignments and protein language models to geometric neural networks and generative methods such as diffusion; experimental restraints from cryo-EM or cross-linking can further guide predictions. Evaluation spans structure-accuracy benchmarks such as CAMEO and tests of complexes, binding, and physical plausibility. Models now tackle large assemblies and multiple conformations, but balancing accuracy, inference cost, reliable evaluation, and realistic dynamic behavior remains challenging.

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40 papers10 papers in the last four weeks, against 1 the four weeks before. 0.1% of all new papers.

ProtDBench: A Unified Benchmark of Protein Binder Design and Evaluation
May 5, 2026Cong Liu, Milong Ren, Jiaqi Guan +4Protein Structure Prediction Benchmark Construction
AMLab, AI4Science Lab, University of Amsterdam, Amsterdam, The Netherlands · Work done at ByteDance Seed · ByteDance Seed
ConforNets: Latents-Based Conformational Control in OpenFold3
Apr 20, 2026Minji Lee, Colin Kalicki, Minkyu Jeon +3Representation Learning Protein Structure Prediction
Department of Computer Science, Columbia University, NY, USA · Department of Systems Biology, Columbia University, NY, USA · Department of Computer Science, Princeton University, Princeton, NJ, USA
Polyformer: a generative framework for thermodynamic modeling of polymeric molecules
Apr 15, 2026Alessio Valentini, David Pekker, Chungwen Liang +2Protein Structure Generation Protein Structure Prediction
PsiDagger, San Diego, CA · Department of Physics and Astronomy, University of Pittsburgh, Pittsburgh, PA · Department of Chemistry and The PULSE Institute, Stanford University, Stanford, CA +1
Towards protein folding pathways by reconstructing protein residue networks with a policy-driven model
Apr 6, 2026Susan KhorProtein Structure Prediction
Sampling at intermediate temperatures is optimal for training large language models in protein structure prediction
Mar 31, 2026L. Ghiringhelli, A. Zambon, G. TianaProtein Structure Prediction AI-Assisted Scientific Research
Department of Physics, via Celoria 16, 20133 Milano, Italy · Department of Physics and INFN, via Celoria 16, 20133 Milano, Italy
From Snapshots to Symphonies: The Evolution of Protein Prediction from Static Structures to Generative Dynamics and Multimodal Interactions
Mar 19, 2026Jingzhi Chen, Lijian XuProtein Structure Prediction Protein Representation Learning
Shenzhen University of Advanced Technology
Fold-CP: A Context Parallelism Framework for Biomolecular Modeling
Mar 16, 2026Dejun Lin, Simon Chu, Vishanth Iyer +35Context Parallelism Protein Structure Prediction
NVIDIA · Rezo Therapeutics · Proxima +1
Multi-objective Genetic Programming with Multi-view Multi-level Feature for Enhanced Protein Secondary Structure Prediction
Mar 11, 2026Yining Qian, Lijie Su, Meiling Xu +1Multi-View Feature Fusion Protein Structure Prediction
National Frontiers Science Center for Industrial Intelligence and Systems Optimization, Shenyang 110819, China · Key Laboratory of Data Analytics and Optimization for Smart Industry (Northeastern University), Ministry of Education, Shenyang 110819, China · Liaoning Engineering Laboratory of Data Analytics and Optimization for Smart Industry, Shenyang 110819, China +2
Enhanced Protein Intrinsic Disorder Prediction Through Dual-View Multiscale Features and Multi-objective Evolutionary Algorithm
Mar 6, 2026Shaokuan Wang, Pengshan Cui, Yining Qian +2Multi-Scale Feature Fusion Protein Structure Prediction
College of Information Science and Engineering, Northeastern University, Shenyang 110819, China · School of Computer Science and Engineering, Northeastern University, Shenyang 110819, China · Key Laboratory of Data Analytics and Optimization for Smart Industry, Ministry of Education, Northeastern University, Shenyang 110819, China
Two Stages of Folding: Convergent Mechanisms in AI Protein Folding Trunks
Feb 5, 2026Kevin Lu, Jannik Brinkmann, Stefan Huber +4Protein Structure Prediction AI-Assisted Scientific Research
1Northeastern University · 2TU Clausthal · 3Harvard University +2

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ProtDBench: A Unified Benchmark of Protein Binder Design and Evaluation

Cong Liu, Milong Ren, Jiaqi Guan, Chengyue Gong+3

AMLab, AI4Science Lab, University of Amsterdam, Amsterdam, The Netherlands · Work done at ByteDance Seed · ByteDance Seed

Protein Structure Prediction Benchmark Construction Benchmark Design AI-Assisted Scientific Research Protein Design Protein Binder Design

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Recent advances in de novo protein binder design have enabled increasing experimental validation, yet reported in silico metrics remain difficult to interpret or compare across studies due to non-standardized evaluation protocols. We introduce ProtDBench, a standardized and throughput-aware evaluation framework for protein binder design. ProtDBench defines unified benchmark tasks, evaluation protocols, and success criteria, enabling systematic analysis of how evaluation design influences observed performance. Using a large wet-lab annotated dataset, we analyze commonly used structure prediction models as evaluation verifiers, revealing substantial verifier-dependent bias and limited agreement under identical filtering protocols. We then benchmark representative open-source generative binder design methods across ten diverse protein targets under a fixed evaluation protocol. Beyond per-sequence success rates, ProtDBench incorporates throughput-aware metrics based on a fixed 24-hour budget, as well as cluster-level success criteria to account for structural diversity. Together, these results expose systematic differences induced by filtering rules, success definitions, and throughput-aware evaluation between computational efficiency, success rate, and structural diversity. Overall, ProtDBench provides a fair and reproducible evaluation pipeline that supports systematic and controlled comparison of protein binder design methods under realistic evaluation settings.