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arXiv · 2211.05698

Probabilistic thermal stability prediction through sparsity promoting transformer representation

Yevgen Zainchkovskyy·Jesper Ferkinghoff-Borg·Anja Bennett·Thomas Egebjerg·Nikolai Lorenzen·Per Jr. Greisen·Søren Hauberg·Carsten Stahlhut
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Abstract

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.

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Yevgen Zainchkovskyy, Jesper Ferkinghoff-Borg, Anja Bennett, Thomas Egebjerg, Nikolai Lorenzen, Per Jr. Greisen, Søren Hauberg, Carsten Stahlhut. 2022-11-10. Probabilistic thermal stability prediction through sparsity promoting transformer representation. https://arxiv.org/abs/2211.05698

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