AI Medical Compendium Topic:
Protein Binding

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Deep Neural Network Based Predictions of Protein Interactions Using Primary Sequences.

Molecules (Basel, Switzerland)
Machine learning based predictions of protein⁻protein interactions (PPIs) could provide valuable insights into protein functions, disease occurrence, and therapy design on a large scale. The intensive feature engineering in most of these methods make...

Insight Analysis of Promiscuous Estrogen Receptor α-Ligand Binding by a Novel Machine Learning Scheme.

Chemical research in toxicology
Estrogen receptor α (ERα) plays a significant role in occurrence of breast cancer and may cause various adverse side-effects when ERα is an off-target protein. A theoretical model was derived to predict the binding affinity of ERα using the pharmacop...

Machine learning approaches infer vitamin D signaling: Critical impact of vitamin D receptor binding within topologically associated domains.

The Journal of steroid biochemistry and molecular biology
The vitamin D-modulated transcriptome of highly responsive human cells, such as THP-1 monocytes, comprises more than 500 genes, half of which are primary targets. Recently, we proposed a chromatin model of vitamin D signaling demonstrating that nearl...

Convolutional neural network scoring and minimization in the D3R 2017 community challenge.

Journal of computer-aided molecular design
We assess the ability of our convolutional neural network (CNN)-based scoring functions to perform several common tasks in the domain of drug discovery. These include correctly identifying ligand poses near and far from the true binding mode when giv...

Prediction of RNA-protein sequence and structure binding preferences using deep convolutional and recurrent neural networks.

BMC genomics
BACKGROUND: RNA regulation is significantly dependent on its binding protein partner, known as the RNA-binding proteins (RBPs). Unfortunately, the binding preferences for most RBPs are still not well characterized. Interdependencies between sequence ...

A Data Driven Model for Predicting RNA-Protein Interactions based on Gradient Boosting Machine.

Scientific reports
RNA protein interactions (RPI) play a pivotal role in the regulation of various biological processes. Experimental validation of RPI has been time-consuming, paving the way for computational prediction methods. The major limiting factor of these meth...

Visualizing convolutional neural network protein-ligand scoring.

Journal of molecular graphics & modelling
Protein-ligand scoring is an important step in a structure-based drug design pipeline. Selecting a correct binding pose and predicting the binding affinity of a protein-ligand complex enables effective virtual screening. Machine learning techniques c...

Development of a machine-learning model to predict Gibbs free energy of binding for protein-ligand complexes.

Biophysical chemistry
The possibility of using the atomic coordinates of protein-ligand complexes to assess binding affinity has a beneficial impact in the early stages of drug development and design. From the computational view, the creation of reliable scoring functions...

Agonists of G-Protein-Coupled Odorant Receptors Are Predicted from Chemical Features.

The journal of physical chemistry letters
Predicting the activity of chemicals for a given odorant receptor is a longstanding challenge. Here the activity of 258 chemicals on the human G-protein-coupled odorant receptor (OR)51E1, also known as prostate-specific G-protein-coupled receptor 2 (...