@inproceedings{
rollins2023ablef,
title={Ab{LEF}: Antibody Language Ensemble Fusion for thermodynamically empowered property predictions},
author={Zachary A Rollins and Talal Widatalla and Andrew Waight and Alan C Cheng and Essam Metwally},
booktitle={NeurIPS 2023 Workshop on New Frontiers of AI for Drug Discovery and Development},
year={2023},
url={https://openreview.net/forum?id=6KdR429i2Q}
}
Pre-trained protein language and/or structural models are often fine-tuned on drug development properties (i.e., developability properties) to accelerate 
drug discovery initiatives. However, these models generally rely on a single structural conformation and/or a single sequence as a molecular representation. 
We present a physics-based model whereby structural ensemble representations are fused by a transformer-based architecture and concatenated to 
a language representation to predict antibody protein properties. AbLEF enables the direct infusion of thermodynamic information into latent space and this enhances
property prediction by explicitly infusing dynamic molecular behavior that occurs during experimental measurement. 
We find that $\textbf{(1)}$ ensembles of structures generated from molecular simulation can further improve antibody property prediction for small datasets,
$\textbf{(2)}$ fine-tuned large protein language models can match smaller antibody-specific language models at predicting antibody properties, 
$\textbf{(3)}$ trained multimodal sequence and structural representations outperform sequence representations alone, $\textbf{(4)}$ pre-trained sequence with 
structure models are competitive with shallow machine learning (ML) methods in the small data regime, and $\textbf{(5)}$ predicting measured antibody properties remains 
difficult for limited high fidelity datasets. AbLEF has been made publicly available at https://github.com/merck/AbLEF.
AbLEF: Antibody Language Ensemble Fusion for thermodynamically empowered property predictions
Zachary A Rollins Talal Widatalla Andrew Waight Alan C Cheng Essam Metwally