AIMC Topic: Proteins

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Predicting Functional Surface Topographies Combining Topological Data Analysis and Deep Learning Across the Human Protein Universe.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Characterizing geometric and topological properties of protein structures encompassing surface pockets, interior cavities, and cross channels is important for understanding their functions. Our knowledge of protein structures has been greatly advance...

Knot or not? Identifying unknotted proteins in knotted families with sequence-based Machine Learning model.

Protein science : a publication of the Protein Society
Knotted proteins, although scarce, are crucial structural components of certain protein families, and their roles continue to be a topic of intense research. Capitalizing on the vast collection of protein structure predictions offered by AlphaFold (A...

Predicting protein functions using positive-unlabeled ranking with ontology-based priors.

Bioinformatics (Oxford, England)
UNLABELLED: Automated protein function prediction is a crucial and widely studied problem in bioinformatics. Computationally, protein function is a multilabel classification problem where only positive samples are defined and there is a large number ...

DEAttentionDTA: protein-ligand binding affinity prediction based on dynamic embedding and self-attention.

Bioinformatics (Oxford, England)
MOTIVATION: Predicting protein-ligand binding affinity is crucial in new drug discovery and development. However, most existing models rely on acquiring 3D structures of elusive proteins. Combining amino acid sequences with ligand sequences and bette...

A survey of generative AI for de novo drug design: new frontiers in molecule and protein generation.

Briefings in bioinformatics
Artificial intelligence (AI)-driven methods can vastly improve the historically costly drug design process, with various generative models already in widespread use. Generative models for de novo drug design, in particular, focus on the creation of n...

GEMF: a novel geometry-enhanced mid-fusion network for PLA prediction.

Briefings in bioinformatics
Accurate prediction of protein-ligand binding affinity (PLA) is important for drug discovery. Recent advances in applying graph neural networks have shown great potential for PLA prediction. However, existing methods usually neglect the geometric inf...

ifDEEPre: large protein language-based deep learning enables interpretable and fast predictions of enzyme commission numbers.

Briefings in bioinformatics
Accurate understanding of the biological functions of enzymes is vital for various tasks in both pathologies and industrial biotechnology. However, the existing methods are usually not fast enough and lack explanations on the prediction results, whic...

Hierarchical multimodal self-attention-based graph neural network for DTI prediction.

Briefings in bioinformatics
Drug-target interactions (DTIs) are a key part of drug development process and their accurate and efficient prediction can significantly boost development efficiency and reduce development time. Recent years have witnessed the rapid advancement of de...

Prediction of drug-protein interaction based on dual channel neural networks with attention mechanism.

Briefings in functional genomics
The precise identification of drug-protein inter action (DPI) can significantly speed up the drug discovery process. Bioassay methods are time-consuming and expensive to screen for each pair of drug proteins. Machine-learning-based methods cannot acc...