# VenusX: Unlocking Fine-Grained Functional Understanding of Proteins

arXiv: https://arxiv.org/abs/2505.11812 · HTML: https://mlsanity.com/paper/2505.11812/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 60 references that are in the map.
- First posted on arXiv 17 May 2025.

## Builds on (most cited first, 13 of 13)

- [Decoupled Weight Decay Regularization](https://mlsanity.com/paper/1711.05101/) (2017)
- [Learning from Protein Structure with Geometric Vector Perceptrons](https://mlsanity.com/paper/2009.01411/) (2020)
- [Evaluating Protein Transfer Learning with TAPE](https://mlsanity.com/paper/1906.08230/) (2019)
- [PEER: A Comprehensive and Multi-Task Benchmark for Protein Sequence Understanding](https://mlsanity.com/paper/2206.02096/) (2022)
- [ATOM3D: Tasks On Molecules in Three Dimensions](https://mlsanity.com/paper/2012.04035/) (2020)
- [Ankh: Optimized Protein Language Model Unlocks General-Purpose Modelling](https://mlsanity.com/paper/2301.06568/) (2023)
- [ProteinNet: a standardized data set for machine learning of protein structure](https://mlsanity.com/paper/1902.00249/) (2019)
- [DIPS-Plus: The Enhanced Database of Interacting Protein Structures for Interface Prediction](https://mlsanity.com/paper/2106.04362/) (2021)
- [Retrieval-Enhanced Mutation Mastery: Augmenting Zero-Shot Prediction of Protein Language Model](https://mlsanity.com/paper/2410.21127/) (2024)
- [Simple, Efficient and Scalable Structure-aware Adapter Boosts Protein Language Models](https://mlsanity.com/paper/2404.14850/) (2024)
- [Protein Representation Learning with Sequence Information Embedding: Does it Always Lead to a Better Performance?](https://mlsanity.com/paper/2406.19755/) (2024)
- [Immunogenicity Prediction with Dual Attention Enables Vaccine Target Selection](https://mlsanity.com/paper/2410.02647/) (2024)
- [VenusFactory: A Unified Platform for Protein Engineering Data Retrieval and Language Model Fine-Tuning](https://mlsanity.com/paper/2503.15438/) (2025)

## Built on by (most cited first, 5 of 5)

- [Fast and Interpretable Protein Substructure Alignment via Optimal Transport](https://mlsanity.com/paper/2510.11752/) (2025)
- [Generative Modeling in Protein Design: Neural Representations, Conditional Generation, and Evaluation Standards](https://mlsanity.com/paper/2603.26378/) (2026)
- [BioBlobs: Unsupervised Discovery of Functional Substructures for Protein Function Prediction](https://mlsanity.com/paper/2510.01632/) (2025)
- [Protein as a Second Language for LLMs](https://mlsanity.com/paper/2510.11188/) (2025)
- [Retrieval-Augmented Multimodal Learning for Enzyme-Substrate Interaction Prediction Under Low-Homology Shift](https://mlsanity.com/paper/2606.22823/) (2026)

## Abstract (arXiv)

Deep learning models have driven significant progress in predicting protein function and interactions at the protein level. While these advancements have been invaluable for many biological applications such as enzyme engineering and function annotation, a more detailed perspective is essential for understanding protein functional mechanisms and evaluating the biological knowledge captured by models. To address this demand, we introduce VenusX, the first large-scale benchmark for fine-grained functional annotation and function-based protein pairing at the residue, fragment, and domain levels. VenusX comprises three major task categories across six types of annotations, including residue-level binary classification, fragment-level multi-class classification, and pairwise functional similarity scoring for identifying critical active sites, binding sites, conserved sites, motifs, domains, and epitopes. The benchmark features over 878,000 samples curated from major open-source databases such as InterPro, BioLiP, and SAbDab. By providing mixed-family and cross-family splits at three sequence identity thresholds, our benchmark enables a comprehensive assessment of model performance on both in-distribution and out-of-distribution scenarios. For baseline evaluation, we assess a diverse set of popular and open-source models, including pre-trained protein language models, sequence-structure hybrids, structure-based methods, and alignment-based techniques. Their performance is reported across all benchmark datasets and evaluation settings using multiple metrics, offering a thorough comparison and a strong foundation for future research. Code and data are publicly available at https://github.com/ai4protein/VenusX.