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Probabilistic Thermal Stability Prediction Through Sparsity Promoting Transformer Representation by Zainchkovskyy, Yevgen; Ferkinghoff-Borg, Jesper; Bennett, Anja; Egebjerg, Thomas; Lorenzen, Nikolai; Greisen, Per Jr.; Hauberg, Søren; Stahlhut, Carsten is a scholarly article available to read on EtoBox.

What is Probabilistic Thermal Stability Prediction Through Sparsity Promoting Transformer Representation about?

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.

Author

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

Published

2022

Language

EN