AIMC Topic: Binding Sites

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Identification of STAT3 phosphorylation inhibitors using generative deep learning, virtual screening, molecular dynamics simulations, and biological evaluation for non-small cell lung cancer therapy.

Molecular diversity Dec 23, 2024
The development of phosphorylation-suppressing inhibitors targeting Signal Transducer and Activator of Transcription 3 (STAT3) represents a promising therapeutic strategy for non-small cell lung cancer (NSCLC). In this study, a generative model was d...
Binding Sites Molecular Dynamics Simulation STAT3 Transcription Factor Cell Line, Tumor Lung Neoplasms Molecular Docking Simulation Humans Antineoplastic Agents Deep Learning Protein Binding Carcinoma, Non-Small-Cell Lung Phosphorylation
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A 4D tensor-enhanced multi-dimensional convolutional neural network for accurate prediction of protein-ligand binding affinity.

Molecular diversity Dec 23, 2024
Protein-ligand interactions are the molecular basis of many important cellular activities, such as gene regulation, cell metabolism, and signal transduction. Protein-ligand binding affinity is a crucial metric of the strength of the interaction betwe...
Models, Molecular Neural Networks, Computer Convolutional Neural Networks Deep Learning Ligands Protein Binding Proteins Binding Sites
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DeepMiRBP: a hybrid model for predicting microRNA-protein interactions based on transfer learning and cosine similarity.

BMC bioinformatics Dec 18, 2024
BACKGROUND: Interactions between microRNAs and RNA-binding proteins are crucial for microRNA-mediated gene regulation and sorting. Despite their significance, the molecular mechanisms governing these interactions remain underexplored, apart from sequ...
Deep Learning Computational Biology RNA-Binding Proteins MicroRNAs Binding Sites Neural Networks, Computer
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Residue-Level Multiview Deep Learning for ATP Binding Site Prediction and Applications in Kinase Inhibitors.

Journal of chemical information and modeling Dec 17, 2024
Accurate identification of adenosine triphosphate (ATP) binding sites is crucial for understanding cellular functions and advancing drug discovery, particularly in targeting kinases for cancer treatment. Existing methods face significant challenges d...
Protein Kinase Inhibitors Binding Sites Deep Learning Adenosine Triphosphate Ligands Molecular Docking Simulation Drug Discovery Humans
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Docking-Informed Machine Learning for Kinome-wide Affinity Prediction.

Journal of chemical information and modeling Dec 10, 2024
Kinase inhibitors are an important class of anticancer drugs, with 80 inhibitors clinically approved and >100 in active clinical testing. Most bind competitively in the ATP-binding site, leading to challenges with selectivity for a specific kinase, r...
Protein Conformation Protein Kinase Inhibitors Binding Sites Protein Binding Protein Kinases Humans Machine Learning Molecular Docking Simulation
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MMD-DTA: A Multi-Modal Deep Learning Framework for Drug-Target Binding Affinity and Binding Region Prediction.

IEEE/ACM transactions on computational biology and bioinformatics Dec 10, 2024
The prediction of drug-target affinity (DTA) plays a crucial role in drug development and the identification of potential drug targets. In recent years, computer-assisted DTA prediction has emerged as a significant approach in this field. In this stu...
Computational Biology Proteins Pharmaceutical Preparations Drug Discovery Humans Binding Sites Deep Learning Neural Networks, Computer Protein Binding
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Intra-Inter Graph Representation Learning for Protein-Protein Binding Sites Prediction.

IEEE/ACM transactions on computational biology and bioinformatics Dec 10, 2024
Graph neural networks have drawn increasing attention and achieved remarkable progress recently due to their potential applications for a large amount of irregular data. It is a natural way to represent protein as a graph. In this work, we focus on p...
Protein Binding Computational Biology Databases, Protein Neural Networks, Computer Proteins Ligands Machine Learning Binding Sites Algorithms
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Transcription Factor Binding Site Prediction Using CnNet Approach.

IEEE/ACM transactions on computational biology and bioinformatics Dec 10, 2024
Controlling the gene expression is the most important development in a living organism, which makes it easier to find different kinds of diseases and their causes. It's very difficult to know what factors control the gene expression. Transcription Fa...
Transcription Factors Humans Sequence Analysis, DNA Neural Networks, Computer Binding Sites Deep Learning Algorithms Computational Biology
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RGCNPPIS: A Residual Graph Convolutional Network for Protein-Protein Interaction Site Prediction.

IEEE/ACM transactions on computational biology and bioinformatics Dec 10, 2024
Accurate identification of protein-protein interaction (PPI) sites is crucial for understanding the mechanisms of biological processes, developing PPI networks, and detecting protein functions. Currently, most computational methods primarily concentr...
Humans Binding Sites Algorithms Neural Networks, Computer Protein Interaction Mapping Proteins Protein Interaction Maps Databases, Protein Computational Biology
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ATP_mCNN: Predicting ATP binding sites through pretrained language models and multi-window neural networks.

Computers in biology and medicine Dec 8, 2024
Adenosine triphosphate plays a vital role in providing energy and enabling key cellular processes through interactions with binding proteins. The increasing amount of protein sequence data necessitates computational methods for identifying binding si...
Proteins Databases, Protein Adenosine Triphosphate Humans Neural Networks, Computer Computational Biology Binding Sites
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