AIMC Topic: Proteins

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Graph-BERT and language model-based framework for protein-protein interaction identification.

Scientific reports
Identification of protein-protein interactions (PPI) is among the critical problems in the domain of bioinformatics. Previous studies have utilized different AI-based models for PPI classification with advances in artificial intelligence (AI) techniq...

Effectively Identifying Compound-Protein Interaction Using Graph Neural Representation.

IEEE/ACM transactions on computational biology and bioinformatics
Effectively identifying compound-protein interactions (CPIs) is crucial for new drug design, which is an important step in silico drug discovery. Current machine learning methods for CPI prediction mainly use one-demensional (1D) compound/protein str...

IGPRED-MultiTask: A Deep Learning Model to Predict Protein Secondary Structure, Torsion Angles and Solvent Accessibility.

IEEE/ACM transactions on computational biology and bioinformatics
Protein secondary structure, solvent accessibility and torsion angle predictions are preliminary steps to predict 3D structure of a protein. Deep learning approaches have achieved significant improvements in predicting various features of protein str...

A Deep Learning Framework for Predicting Protein Functions With Co-Occurrence of GO Terms.

IEEE/ACM transactions on computational biology and bioinformatics
The understanding of protein functions is critical to many biological problems such as the development of new drugs and new crops. To reduce the huge gap between the increase of protein sequences and annotations of protein functions, many methods hav...

Reverse tracking from drug-induced transcriptomes through multilayer molecular networks reveals hidden drug targets.

Computers in biology and medicine
Identifying molecular targets of a drug is an essential process for drug discovery and development. The recent in-silico approaches are usually based on the structure information of chemicals and proteins. However, 3D structure information is hard to...

BiComp-DTA: Drug-target binding affinity prediction through complementary biological-related and compression-based featurization approach.

PLoS computational biology
Drug-target binding affinity prediction plays a key role in the early stage of drug discovery. Numerous experimental and data-driven approaches have been developed for predicting drug-target binding affinity. However, experimental methods highly rely...

Leveraging deep learning to improve vaccine design.

Trends in immunology
Deep learning has led to incredible breakthroughs in areas of research, from self-driving vehicles to solutions, to formal mathematical proofs. In the biomedical sciences, however, the revolutionary results seen in other fields are only now beginning...

Enzyme function prediction using contrastive learning.

Science (New York, N.Y.)
Enzyme function annotation is a fundamental challenge, and numerous computational tools have been developed. However, most of these tools cannot accurately predict functional annotations, such as enzyme commission (EC) number, for less-studied protei...

HAC-Net: A Hybrid Attention-Based Convolutional Neural Network for Highly Accurate Protein-Ligand Binding Affinity Prediction.

Journal of chemical information and modeling
Applying deep learning concepts from image detection and graph theory has greatly advanced protein-ligand binding affinity prediction, a challenge with enormous ramifications for both drug discovery and protein engineering. We build upon these advanc...

Improving protein-protein interaction site prediction using deep residual neural network.

Analytical biochemistry
Accurate identification of protein-protein interaction (PPI) sites is significantly important for understanding the mechanism of life and developing new drugs. However, it is expensive and time-consuming to identify PPI sites using wet-lab experiment...