Ahmad Salim Al-Sibahi — accepted AI-conference papers

2022

Ancestral protein sequence reconstruction using a tree-structured Ornstein-Uhlenbeck variational autoencoder

Lys Sanz Moreta, Ola Rønning, Ahmad Salim Al-Sibahi, Jotun Hein, Douglas Theobald, Thomas Hamelryck

ICLR 2022poster

We introduce a deep generative model for representation learning of biological sequences that, unlike existing models, explicitly represents the evolutionary process. The model makes use of a tree-structured Ornstein-Uhlenbeck process, obtained from a given phylogenetic tree, as an informative prior…

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2021

Efficient Generative Modelling of Protein Structure Fragments using a Deep Markov Model

Christian B Thygesen, Christian Skjødt Steenmans, Ahmad Salim Al-Sibahi, Lys Sanz Moreta, Anders Bundgård Sørensen, Thomas Hamelryck

ICML 2021spotlight

Fragment libraries are often used in protein structure prediction, simulation and design as a means to significantly reduce the vast conformational search space. Current state-of-the-art methods for fragment library generation do not properly account for aleatory and epistemic uncertainty, respectiv…

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