Pre-trained protein language models have demonstrated significant applicability in different protein engineering task. A general usage of these pre-trained transformer models latent representation is to use a mean pool across residue positions to reduce the feature dimensions to further downstream tasks such as predicting bio-physics properties or other functional behaviours. In this paper we provide a two-fold contribution to machine learning (ML) driven drug design. Firstly, we demonstrate the power of sparsity by promoting penalization of pre-trained transformer models to secure more robust and accurate melting temperature (Tm) prediction of single-chain variable fragments with a mean absolute error of 0.23C. Secondly, we demonstrate the power of framing our prediction problem in a probabilistic framework. Specifically, we advocate for the need of adopting probabilistic frameworks especially in the context of ML driven drug design.
@article{arxiv.2211.05698,
title = {Probabilistic thermal stability prediction through sparsity promoting transformer representation},
author = {Yevgen Zainchkovskyy and Jesper Ferkinghoff-Borg and Anja Bennett and Thomas Egebjerg and Nikolai Lorenzen and Per Jr. Greisen and Søren Hauberg and Carsten Stahlhut},
journal= {arXiv preprint arXiv:2211.05698},
year = {2022}
}