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Opportunities and challenges for machine learning-assisted enzyme engineering
Enzymes can be engineered at the level of their amino acid sequences to optimize key
properties such as expression, stability, substrate range, and catalytic efficiency─ or even to …
properties such as expression, stability, substrate range, and catalytic efficiency─ or even to …
Unveiling the evolution of policies for enhancing protein structure predictions: A comprehensive analysis
Predicting protein structure is both fascinating and formidable, playing a crucial role in
structure-based drug discovery and unraveling diseases with elusive origins. The Critical …
structure-based drug discovery and unraveling diseases with elusive origins. The Critical …
Multimodal pretraining for unsupervised protein representation learning
Proteins are complex biomolecules essential for numerous biological processes, making
them crucial targets for advancements in molecular biology, medical research, and drug …
them crucial targets for advancements in molecular biology, medical research, and drug …
Re-dock: towards flexible and realistic molecular docking with diffusion bridge
Accurate prediction of protein-ligand binding structures, a task known as molecular docking
is crucial for drug design but remains challenging. While deep learning has shown promise …
is crucial for drug design but remains challenging. While deep learning has shown promise …
Advances of deep learning in protein science: A comprehensive survey
Protein representation learning plays a crucial role in understanding the structure and
function of proteins, which are essential biomolecules involved in various biological …
function of proteins, which are essential biomolecules involved in various biological …
S‐PLM: Structure‐Aware Protein Language Model via Contrastive Learning Between Sequence and Structure
Proteins play an essential role in various biological and engineering processes. Large
protein language models (PLMs) present excellent potential to reshape protein research by …
protein language models (PLMs) present excellent potential to reshape protein research by …
Prot2Token: A multi-task framework for protein language processing using autoregressive language modeling
This paper proposes a versatile tokenization method and introduces Prot2Token, a model
that combines autoregressive language modeling with protein language models (PLMs) to …
that combines autoregressive language modeling with protein language models (PLMs) to …