AIMC Topic: Proteins

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A Point Cloud Graph Neural Network for Protein-Ligand Binding Site Prediction.

International journal of molecular sciences Aug 27, 2024
Predicting protein-ligand binding sites is an integral part of structural biology and drug design. A comprehensive understanding of these binding sites is essential for advancing drug innovation, elucidating mechanisms of biological function, and exp...
Binding Sites Databases, Protein Ligands Deep Learning Neural Networks, Computer Algorithms Protein Binding Proteins
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Rapid discrimination between deleterious and benign missense mutations in the CAGI 6 experiment.

Human genomics Aug 27, 2024
We describe the machine learning tool that we applied in the CAGI 6 experiment to predict whether single residue mutations in proteins are deleterious or benign. This tool was trained using only single sequences, i.e., without multiple sequence align...
Machine Learning Software Proteins Mutation, Missense Computational Biology Humans
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Optimizing protein sequence classification: integrating deep learning models with Bayesian optimization for enhanced biological analysis.

BMC medical informatics and decision making Aug 27, 2024
Efforts to enhance the accuracy of protein sequence classification are of utmost importance in driving forward biological analyses and facilitating significant medical advancements. This study presents a cutting-edge model called ProtICNN-BiLSTM, whi...
Bayes Theorem Sequence Analysis, Protein Computational Biology Proteins Humans Deep Learning
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An end-to-end framework for the prediction of protein structure and fitness from single sequence.

Nature communications Aug 27, 2024
Significant research progress has been made in the field of protein structure and fitness prediction. Particularly, single-sequence-based structure prediction methods like ESMFold and OmegaFold achieve a balance between inference speed and prediction...
Protein Conformation Sequence Analysis, Protein Mutation Protein Stability Proteins Models, Molecular Algorithms Computational Biology Neural Networks, Computer
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Protein Classes Predicted by Molecular Surface Chemical Features: Machine Learning-Assisted Classification of Cytosol and Secreted Proteins.

The journal of physical chemistry. B Aug 26, 2024
Chemical structures of protein surfaces govern intermolecular interaction, and protein functions include specific molecular recognition, transport, self-assembly, etc. Therefore, the relationship between the chemical structure and protein functions p...
Cytosol Surface Properties Protein Structure, Secondary Proteins Databases, Protein Machine Learning
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Holistic in silico developability assessment of novel classes of small proteins using publicly available sequence-based predictors.

Journal of computer-aided molecular design Aug 20, 2024
The development of novel therapeutic proteins is a lengthy and costly process, with an average attrition rate of 91% (Thomas et al. Clinical Development Success Rates and Contributing Factors 2011-2020, 2021). To increase the probability of success a...
Humans Computer Simulation Drug Design Drug Discovery Machine Learning Algorithms Proteins
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FitScore: a fast machine learning-based score for 3D virtual screening enrichment.

Journal of computer-aided molecular design Aug 16, 2024
Enhancing virtual screening enrichment has become an urgent problem in computational chemistry, driven by increasingly large databases of commercially available compounds, without a commensurate drop in in vitro screening costs. Docking these large d...
Molecular Docking Simulation Ligands Drug Discovery Binding Sites Proteins Drug Evaluation, Preclinical Protein Binding Machine Learning Humans Software
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MGNDTI: A Drug-Target Interaction Prediction Framework Based on Multimodal Representation Learning and the Gating Mechanism.

Journal of chemical information and modeling Aug 13, 2024
Drug-Target Interaction (DTI) prediction facilitates acceleration of drug discovery and promotes drug repositioning. Most existing deep learning-based DTI prediction methods can better extract discriminative features for drugs and proteins, but they ...
Humans Animals Proteins Pharmaceutical Preparations Caenorhabditis elegans Drug Discovery Deep Learning
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Hierarchical Graph Capsule Networks for Molecular Function Classification With Disentangled Representations.

IEEE/ACM transactions on computational biology and bioinformatics Aug 8, 2024
In biochemistry, graph structures have been widely used for modeling compounds, proteins, functional interactions, etc. A common task that divides these graphs into different categories, known as graph classification, highly relies on the quality of ...
Computational Biology Proteins Neural Networks, Computer Algorithms
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Predicting Drug-Target Interactions Via Dual-Stream Graph Neural Network.

IEEE/ACM transactions on computational biology and bioinformatics Aug 8, 2024
Drug target interaction prediction is a crucial stage in drug discovery. However, brute-force search over a compound database is financially infeasible. We have witnessed the increasing measured drug-target interactions records in recent years, and t...
Proteins Humans Pharmaceutical Preparations Deep Learning Neural Networks, Computer Algorithms Computational Biology Drug Discovery
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