AI Medical Compendium Topic:
Proteins

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Enhanced Protein Structural Class Prediction Using Effective Feature Modeling and Ensemble of Classifiers.

IEEE/ACM transactions on computational biology and bioinformatics
Protein Secondary Structural Class (PSSC) information is important in investigating further challenges of protein sequences like protein fold recognition, protein tertiary structure prediction, and analysis of protein functions for drug discovery. Id...

Drug-Target Interaction Prediction: End-to-End Deep Learning Approach.

IEEE/ACM transactions on computational biology and bioinformatics
The discovery of potential Drug-Target Interactions (DTIs) is a determining step in the drug discovery and repositioning process, as the effectiveness of the currently available antibiotic treatment is declining. Although putting efforts on the tradi...

A Deep Learning Framework for Gene Ontology Annotations With Sequence- and Network-Based Information.

IEEE/ACM transactions on computational biology and bioinformatics
Knowledge of protein functions plays an important role in biology and medicine. With the rapid development of high-throughput technologies, a huge number of proteins have been discovered. However, there are a great number of proteins without function...

De novo protein design by deep network hallucination.

Nature
There has been considerable recent progress in protein structure prediction using deep neural networks to predict inter-residue distances from amino acid sequences. Here we investigate whether the information captured by such networks is sufficiently...

Picture-word order compound protein interaction: Predicting compound-protein interaction using structural images of compounds.

Journal of computational chemistry
Identifying potential associations between proteins and compounds is significant and challenging in the drug discovery process. Existing deep-learning-based methods tend to treat compounds and proteins as sequences or graphs. Inspired by the rapid de...

Unsupervised Representation Learning for Proteochemometric Modeling.

International journal of molecular sciences
In silico protein-ligand binding prediction is an ongoing area of research in computational chemistry and machine learning based drug discovery, as an accurate predictive model could greatly reduce the time and resources necessary for the detection a...

Evaluation of Deep Neural Network ProSPr for Accurate Protein Distance Predictions on CASP14 Targets.

International journal of molecular sciences
The field of protein structure prediction has recently been revolutionized through the introduction of deep learning. The current state-of-the-art tool AlphaFold2 can predict highly accurate structures; however, it has a prohibitively long inference ...

Bioinspired translation of classical music intoprotein structures using deep learning and molecular modeling.

Bioinspiration & biomimetics
Architected biomaterials, as well as sound and music, are constructed from small building blocks that are assembled across time- and length-scales. Here we present a novel deep learning-enabled integrated algorithmic workflow to merge the two concept...

Simplified, interpretable graph convolutional neural networks for small molecule activity prediction.

Journal of computer-aided molecular design
We here present a streamlined, explainable graph convolutional neural network (gCNN) architecture for small molecule activity prediction. We first conduct a hyperparameter optimization across nearly 800 protein targets that produces a simplified gCNN...

PremPLI: a machine learning model for predicting the effects of missense mutations on protein-ligand interactions.

Communications biology
Resistance to small-molecule drugs is the main cause of the failure of therapeutic drugs in clinical practice. Missense mutations altering the binding of ligands to proteins are one of the critical mechanisms that result in genetic disease and drug r...