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Protein Binding

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CSConv2d: A 2-D Structural Convolution Neural Network with a Channel and Spatial Attention Mechanism for Protein-Ligand Binding Affinity Prediction.

Biomolecules
The binding affinity of small molecules to receptor proteins is essential to drug discovery and drug repositioning. Chemical methods are often time-consuming and costly, and models for calculating the binding affinity are imperative. In this study, w...

Predicting Absorption-Distribution Properties of Neuroprotective Phosphine-Borane Compounds Using In Silico Modeling and Machine Learning.

Molecules (Basel, Switzerland)
Phosphine-borane complexes are novel chemical entities with preclinical efficacy in neuronal and ophthalmic disease models. In vitro and in vivo studies showed that the metabolites of these compounds are capable of cleaving disulfide bonds implicated...

Machine learning guided aptamer refinement and discovery.

Nature communications
Aptamers are single-stranded nucleic acid ligands that bind to target molecules with high affinity and specificity. They are typically discovered by searching large libraries for sequences with desirable binding properties. These libraries, however, ...

Optimization of therapeutic antibodies by predicting antigen specificity from antibody sequence via deep learning.

Nature biomedical engineering
The optimization of therapeutic antibodies is time-intensive and resource-demanding, largely because of the low-throughput screening of full-length antibodies (approximately 1 × 10 variants) expressed in mammalian cells, which typically results in fe...

A Cascade Graph Convolutional Network for Predicting Protein-Ligand Binding Affinity.

International journal of molecular sciences
Accurate prediction of binding affinity between protein and ligand is a very important step in the field of drug discovery. Although there are many methods based on different assumptions and rules do exist, prediction performance of protein-ligand bi...

Development of a graph convolutional neural network model for efficient prediction of protein-ligand binding affinities.

PloS one
Prediction of protein-ligand interactions is a critical step during the initial phase of drug discovery. We propose a novel deep-learning-based prediction model based on a graph convolutional neural network, named GraphBAR, for protein-ligand binding...

Machine learning predicts nucleosome binding modes of transcription factors.

BMC bioinformatics
BACKGROUND: Most transcription factors (TFs) compete with nucleosomes to gain access to their cognate binding sites. Recent studies have identified several TF-nucleosome interaction modes including end binding (EB), oriented binding, periodic binding...

Improved Protein-Ligand Binding Affinity Prediction with Structure-Based Deep Fusion Inference.

Journal of chemical information and modeling
Predicting accurate protein-ligand binding affinities is an important task in drug discovery but remains a challenge even with computationally expensive biophysics-based energy scoring methods and state-of-the-art deep learning approaches. Despite th...

Discovery of novel dual adenosine A1/A2A receptor antagonists using deep learning, pharmacophore modeling and molecular docking.

PLoS computational biology
Adenosine receptors (ARs) have been demonstrated to be potential therapeutic targets against Parkinson's disease (PD). In the present study, we describe a multistage virtual screening approach that identifies dual adenosine A1 and A2A receptor antago...