AI Medical Compendium Topic:
Protein Binding

Clear Filters Showing 761 to 770 of 827 articles

Protein-ligand binding residue prediction enhancement through hybrid deep heterogeneous learning of sequence and structure data.

Bioinformatics (Oxford, England)
MOTIVATION: Knowledge of protein-ligand binding residues is important for understanding the functions of proteins and their interaction mechanisms. From experimentally solved protein structures, how to accurately identify its potential binding sites ...

Uncovering tissue-specific binding features from differential deep learning.

Nucleic acids research
Transcription factors (TFs) can bind DNA in a cooperative manner, enabling a mutual increase in occupancy. Through this type of interaction, alternative binding sites can be preferentially bound in different tissues to regulate tissue-specific expres...

Learning from the ligand: using ligand-based features to improve binding affinity prediction.

Bioinformatics (Oxford, England)
MOTIVATION: Machine learning scoring functions for protein-ligand binding affinity prediction have been found to consistently outperform classical scoring functions. Structure-based scoring functions for universal affinity prediction typically use fe...

Recognizing Proteins with Binding Function in Elymus nutans Based on Machine Learning Methods.

Combinatorial chemistry & high throughput screening
BACKGROUND: We research the binding function proteins in Elymus nutans. Recognition for proteins is essential for study of biology. Machine learning methods have been widely used for the prediction of proteins.

Strategies for Design of Molecular Structures with a Desired Pharmacophore Using Deep Reinforcement Learning.

Chemical & pharmaceutical bulletin
The goal of drug design is to discover molecular structures that have suitable pharmacological properties in vast chemical space. In recent years, the use of deep generative models (DGMs) is getting a lot of attention as an effective method of genera...

Computational Models for Self-Interacting Proteins Prediction.

Protein and peptide letters
Self-Interacting Proteins (SIPs), whose two or more copies can interact with each other, have significant roles in cellular functions and evolution of Protein Interaction Networks (PINs). Knowing whether a protein can act on itself is important to un...

Recent Advances on the Semi-Supervised Learning for Long Non-Coding RNA-Protein Interactions Prediction: A Review.

Protein and peptide letters
In recent years, more and more evidence indicates that long non-coding RNA (lncRNA) plays a significant role in the development of complex biological processes, especially in RNA progressing, chromatin modification, and cell differentiation, as well ...

Deep Learning in the Study of Protein-Related Interactions.

Protein and peptide letters
Protein-related interaction prediction is critical to understanding life processes, biological functions, and mechanisms of drug action. Experimental methods used to determine proteinrelated interactions have always been costly and inefficient. In re...

evERdock BAI: Machine-learning-guided selection of protein-protein complex structure.

The Journal of chemical physics
Computational techniques for accurate and efficient prediction of protein-protein complex structures are widely used for elucidating protein-protein interactions, which play important roles in biological systems. Recently, it has been reported that s...

ACME: pan-specific peptide-MHC class I binding prediction through attention-based deep neural networks.

Bioinformatics (Oxford, England)
MOTIVATION: Prediction of peptide binding to the major histocompatibility complex (MHC) plays a vital role in the development of therapeutic vaccines for the treatment of cancer. Algorithms with improved correlations between predicted and actual bind...