{"total":35,"rows":[{"id":"2302.00294","title":"The geometry of hidden representations of large transformer models","created":"2023-02-01","cited_by_count":112,"area":null},{"id":"2206.02096","title":"PEER: A Comprehensive and Multi-Task Benchmark for Protein Sequence Understanding","created":"2022-06-05","cited_by_count":77,"area":null},{"id":"2302.04611","title":"A Text-guided Protein Design Framework","created":"2023-02-09","cited_by_count":64,"area":null},{"id":"2401.06199","title":"xTrimoPGLM: Unified 100B-Scale Pre-trained Transformer for Deciphering the Language of Protein","created":"2024-01-11","cited_by_count":50,"area":null},{"id":"2301.06568","title":"Ankh: Optimized Protein Language Model Unlocks General-Purpose Modelling","created":"2023-01-16","cited_by_count":45,"area":null},{"id":"2109.03400","title":"Entangled Datasets for Quantum Machine Learning","created":"2021-09-08","cited_by_count":27,"area":null},{"id":"2108.07435","title":"Modeling Protein Using Large-scale Pretrain Language Model","created":"2021-08-17","cited_by_count":21,"area":null},{"id":"2103.10334","title":"Structure Inducing Pre-Training","created":"2021-03-18","cited_by_count":10,"area":null},{"id":"2308.05326","title":"OpenProteinSet: Training data for structural biology at scale","created":"2023-08-10","cited_by_count":8,"area":null},{"id":"2102.03881","title":"Mimetic Neural Networks: A unified framework for Protein Design and Folding","created":"2021-02-07","cited_by_count":7,"area":null},{"id":"2302.12563","title":"Retrieved Sequence Augmentation for Protein Representation Learning","created":"2023-02-24","cited_by_count":7,"area":null},{"id":"2403.12995","title":"ESM All-Atom: Multi-scale Protein Language Model for Unified Molecular Modeling","created":"2024-03-05","cited_by_count":7,"area":null},{"id":"2505.11812","title":"VenusX: Unlocking Fine-Grained Functional Understanding of Proteins","created":"2025-05-17","cited_by_count":5,"area":null},{"id":"2506.06294","title":"GLProtein: Global-and-Local Structure Aware Protein Representation Learning","created":"2025-05-17","cited_by_count":5,"area":null},{"id":"2211.02936","title":"Learning the shape of protein micro-environments with a holographic convolutional neural network","created":"2022-11-05","cited_by_count":4,"area":null},{"id":"2105.07407","title":"Protein sequence-to-structure learning: Is this the end(-to-end revolution)?","created":"2021-05-16","cited_by_count":3,"area":null},{"id":"2206.12240","title":"PSP: Million-level Protein Sequence Dataset for Protein Structure Prediction","created":"2022-06-24","cited_by_count":3,"area":null},{"id":"2209.15567","title":"Holographic-(V)AE: an end-to-end SO(3)-Equivariant (Variational) Autoencoder in Fourier Space","created":"2022-09-30","cited_by_count":3,"area":null},{"id":"2301.13154","title":"Protein Representation Learning via Knowledge Enhanced Primary Structure Modeling","created":"2023-01-30","cited_by_count":3,"area":null},{"id":"2505.20052","title":"Ankh3: Multi-Task Pretraining with Sequence Denoising and Completion Enhances Protein Representations","created":"2025-05-26","cited_by_count":3,"area":null},{"id":"2601.23151","title":"Manifold-Aware Perturbations for Constrained Generative Modeling","created":"2026-01-30","cited_by_count":3,"area":null},{"id":"2102.06125","title":"Artificial Intelligence Advances for De Novo Molecular Structure Modeling in Cryo-EM","created":"2021-02-11","cited_by_count":2,"area":null},{"id":"2212.03320","title":"Reinforcement Learning for Molecular Dynamics Optimization: A Stochastic Pontryagin Maximum Principle Approach","created":"2022-12-06","cited_by_count":2,"area":null},{"id":"2408.00057","title":"GOProteinGNN: Leveraging Protein Knowledge Graphs for Protein Representation Learning","created":"2024-07-31","cited_by_count":2,"area":null},{"id":"2502.07299","title":"Life-Code: Central Dogma Modeling with Multi-Omics Sequence Unification","created":"2025-02-11","cited_by_count":2,"area":null},{"id":"2512.09365","title":"KGOT: Unified Knowledge Graph and Optimal Transport Pseudo-Labeling for Molecule-Protein Interaction Prediction","created":"2025-12-10","cited_by_count":2,"area":null},{"id":"2212.02251","title":"Multiscale Graph Neural Networks for Protein Residue Contact Map Prediction","created":"2022-12-02","cited_by_count":1,"area":null},{"id":"2407.06703","title":"HERMES: Holographic Equivariant neuRal network model for Mutational Effect and Stability prediction","created":"2024-07-09","cited_by_count":1,"area":null},{"id":"2512.12932","title":"Investigating Data Pruning for Pretraining Biological Foundation Models at Scale","created":"2025-12-15","cited_by_count":1,"area":null},{"id":"2111.06593","title":"Using Deep Learning Sequence Models to Identify SARS-CoV-2 Divergence","created":"2021-11-12","cited_by_count":0,"area":null},{"id":"2410.02430","title":"Predictive Attractor Models","created":"2024-10-03","cited_by_count":0,"area":null},{"id":"2503.10655","title":"Language modelling techniques for analysing the impact of human genetic variation","created":"2025-03-07","cited_by_count":0,"area":null},{"id":"2511.03354","title":"Generative Artificial Intelligence in Bioinformatics: A Systematic Review of Models, Applications, and Methodological Advances","created":"2025-11-05","cited_by_count":0,"area":null},{"id":"2603.11133","title":"Beyond Pairwise Attention: Higher-Order Modular Attention for Efficient Sequence Learning","created":"2026-03-11","cited_by_count":0,"area":null},{"id":"2606.00955","title":"CryoProt: A Protein Pretraining Framework with Cross-Box Interactions on Cryo-EM Density Maps","created":"2026-05-31","cited_by_count":0,"area":null}],"next_cursor":null}