AIMC Topic: Proteins

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MLapRVFL: Protein sequence prediction based on Multi-Laplacian Regularized Random Vector Functional Link.

Computers in biology and medicine
Protein sequence classification is a crucial research field in bioinformatics, playing a vital role in facilitating functional annotation, structure prediction, and gaining a deeper understanding of protein function and interactions. With the rapid d...

Integrated Molecular Modeling and Machine Learning for Drug Design.

Journal of chemical theory and computation
Modern therapeutic development often involves several stages that are interconnected, and multiple iterations are usually required to bring a new drug to the market. Computational approaches have increasingly become an indispensable part of helping r...

CSM-Potential2: A comprehensive deep learning platform for the analysis of protein interacting interfaces.

Proteins
Proteins are molecular machinery that participate in virtually all essential biological functions within the cell, which are tightly related to their 3D structure. The importance of understanding protein structure-function relationship is highlighted...

Ensemble of local and global information for Protein-Ligand Binding Affinity Prediction.

Computational biology and chemistry
Accurately predicting protein-ligand binding affinities is crucial for determining molecular properties and understanding their physical effects. Neural networks and transformers are the predominant methods for sequence modeling, and both have been s...

A Review of Machine Learning and Algorithmic Methods for Protein Phosphorylation Site Prediction.

Genomics, proteomics & bioinformatics
Post-translational modifications (PTMs) have key roles in extending the functional diversity of proteins and, as a result, regulating diverse cellular processes in prokaryotic and eukaryotic organisms. Phosphorylation modification is a vital PTM that...

Machine learning-based model for accurate identification of druggable proteins using light extreme gradient boosting.

Journal of biomolecular structure & dynamics
The identification of druggable proteins (DPs) is significant for the development of new drugs, personalized medicine, understanding of disease mechanisms, drug repurposing, and economic benefits. By identifying new druggable targets, researchers can...

ResCNNT-fold: Combining residual convolutional neural network and Transformer for protein fold recognition from language model embeddings.

Computers in biology and medicine
A comprehensive understanding of protein functions holds significant promise for disease research and drug development, and proteins with analogous tertiary structures tend to exhibit similar functions. Protein fold recognition stands as a classical ...

Naive Prediction of Protein Backbone Phi and Psi Dihedral Angles Using Deep Learning.

Molecules (Basel, Switzerland)
Protein structure prediction represents a significant challenge in the field of bioinformatics, with the prediction of protein structures using backbone dihedral angles recently achieving significant progress due to the rise of deep neural network re...

ResBiGAAT: Residual Bi-GRU with attention for protein-ligand binding affinity prediction.

Computational biology and chemistry
Protein-ligand interaction plays a crucial role in drug discovery, facilitating efficient drug development and enabling drug repurposing. Several computational algorithms, such as Graph Neural Networks and Convolutional Neural Networks, have been pro...

PointDE: Protein Docking Evaluation Using 3D Point Cloud Neural Network.

IEEE/ACM transactions on computational biology and bioinformatics
Protein-protein interactions (PPIs) play essential roles in many vital movements and the determination of protein complex structure is helpful to discover the mechanism of PPI. Protein-protein docking is being developed to model the structure of the ...