Update README.md

README.md

@@ -18,11 +18,10 @@ configs:
     path: data/test-*.parquet
 ---
 
-# LiteFold/FLIP2
+# FLIP2
 
-This repository now includes a Dataset Viewer-friendly Parquet version of the LiteFold FLIP2 tables. The default `load_dataset()` configuration reads the normalized Parquet files in `data/`.
-
-The normalized table contains 890,356 rows from 16 source tables. The original wrapped JSONL source tables remain available in the repository under `tables/`.
+FLIP2 is the second-generation Fitness Landscape Inference for Proteins benchmark, released as a bioRxiv preprint in February 2026 by a group spanning NVIDIA, Microsoft, Caltech, Profluent, and Duke. It picks up where the original FLIP (Dallago et al., NeurIPS 2021) left off, that benchmark was confined to thermostability (Meltome), GB1 binding, and AAV capsid viability, and the field had moved past it. FLIP2 expands the scope to seven new datasets covering enzymes, protein-protein interactions, and light-sensitive proteins, so that protein language model and supervised fitness predictor evaluations cover the kinds of targets people actually engineer.
+The central design idea is the same as FLIP1 but pushed harder: provide standardized train, validation, and test splits that explicitly probe generalization under realistic protein engineering distribution shifts (sequence-similarity-based splits, low-data regimes, extrapolation to higher mutational distance, and held-out interaction partners) rather than random splits. Each dataset ships with multiple split protocols, sequences, fitness labels, and reference baselines (best zero-shot pLM likelihood per split, ridge regression on pLM embeddings, etc.), all in a uniform format compatible with the existing FLIP tooling at benchmark.protein.properties. The intent is that FLIP2 sits alongside ProteinGym (which leans on DMS landscapes and clinical labels) as the protein-engineering-oriented half of the standard evaluation stack.
 
 ## Splits
 
@@ -88,6 +87,16 @@ column_mapping = load_dataset(
 )
 ```
 
-## Rebuild
+# Citation
 
-The normalization script used for this upload is included at `scripts/prepare_wrapped_jsonl_dataset.py`.
+```
+@article{didi2026flip2,
+  title     = {{FLIP2}: Expanding Protein Fitness Landscape Benchmarks for Real-World Machine Learning Applications},
+  author    = {Didi, Kieran and Alamdari, Sarah and Lu, Alex X. and Wittmann, Bruce and Johnston, Kadina E. and Amini, Ava P. and Madani, Ali and Czeneszew, Maya and Dallago, Christian and Yang, Kevin K.},
+  journal   = {bioRxiv},
+  year      = {2026},
+  publisher = {Cold Spring Harbor Laboratory},
+  doi       = {10.64898/2026.02.23.707496},
+  url       = {https://www.biorxiv.org/content/10.64898/2026.02.23.707496v1}
+}
+```