AI Medical Compendium Topic:
Proteins

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Structure-Based Drug Design Using Deep Learning.

Journal of chemical information and modeling
In recent years, deep learning-based methods have emerged as promising tools for drug design. Most of these methods are ligand-based, where an initial target-specific ligand data set is necessary to design potent molecules with optimized properties....

Protein-Protein Docking: Past, Present, and Future.

The protein journal
The biological significance of proteins attracted the scientific community in exploring their characteristics. The studies shed light on the interaction patterns and functions of proteins in a living body. Due to their practical difficulties, reliabl...

Amino acid environment affinity model based on graph attention network.

Journal of bioinformatics and computational biology
Proteins are engines involved in almost all functions of life. They have specific spatial structures formed by twisting and folding of one or more polypeptide chains composed of amino acids. Protein sites are protein structure microenvironments that ...

Learning the local landscape of protein structures with convolutional neural networks.

Journal of biological physics
One fundamental problem of protein biochemistry is to predict protein structure from amino acid sequence. The inverse problem, predicting either entire sequences or individual mutations that are consistent with a given protein structure, has received...

Binding affinity prediction for protein-ligand complex using deep attention mechanism based on intermolecular interactions.

BMC bioinformatics
BACKGROUND: Accurate prediction of protein-ligand binding affinity is important for lowering the overall cost of drug discovery in structure-based drug design. For accurate predictions, many classical scoring functions and machine learning-based meth...

Identification of stress response proteins through fusion of machine learning models and statistical paradigms.

Scientific reports
Proteins are a vital component of cells that perform physiological functions to ensure smooth operations of bodily functions. Identification of a protein's function involves a detailed understanding of the structure of proteins. Stress proteins are e...

Assembled graph neural network using graph transformer with edges for protein model quality assessment.

Journal of molecular graphics & modelling
Acquainting protein's structure is of vital importance to accurately understanding its function. Computational method of deep learning has made great progress in protein structure prediction from sequence, and has the potential to help structural bio...

Comparative analysis of network-based approaches and machine learning algorithms for predicting drug-target interactions.

Methods (San Diego, Calif.)
Computational prediction of drug-target interactions (DTIs) is of particular importance in the process of drug repositioning because of its efficiency in selecting potential candidates for DTIs. A variety of computational methods for predicting DTIs ...

Protein Design with Deep Learning.

International journal of molecular sciences
Computational Protein Design (CPD) has produced impressive results for engineering new proteins, resulting in a wide variety of applications. In the past few years, various efforts have aimed at replacing or improving existing design methods using De...