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Posts
An Overview of Machine Learning Models for NCAA and PTM modeling
Published:
AlphaFold 2 [1] has largely solved the problem of predicting the 3D structure of proteins from their amino acid sequences, though there is still debate over the extent to which this problem is truly solved — a view I share. AlphaFold 3 [2] and a number of subsequent models [3,4,5] have extended this success to protein complexes and proteins with small-molecule binders. However, all of these are primarily designed for canonical amino acids (CAAs) and do not account for non-canonical amino acids (NCAAs). Given that NCAAs play an increasingly important role in drug discovery and protein engineering, it is crucial to review the current state of machine learning models for predicting the structure of proteins containing NCAAs and post-translational modifications (PTMs). In this post, I review the recent progress in this field, with a critical view on the extent to which these models offer fruitful solutions.
portfolio
REMO
A self-supervised learning framwork learning molecular representations from Chemical Reactions
Portfolio item number 2
Short description of portfolio item number 2 
publications
Contextual Molecule Representation Learning from Chemical Reaction Knowledge
Published in Arxiv, 2024
A pre-trained molecular encoder that incorporates chemical reaction knowledge to learn contextual molecule representations.
Recommended citation: Han Tang, Shikun Feng, Bicheng Lin, Yuyan Ni, Jingjing Liu, Wei-Ying Ma, & Yanyan Lan. (2024). Contextual Molecule Representation Learning from Chemical Reaction Knowledge. arXiv:2402.13779 [cs.LG]. https://arxiv.org/pdf/2402.13779.pdf
talks
Talk 1 on Relevant Topic in Your Field
Published:
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Conference Proceeding talk 3 on Relevant Topic in Your Field
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teaching
Teaching Assistant - 2023/2024, 2024/2025, 2025/2026 Probabilistic Machine Learning
Master Course, University of Copenhagen, 2023