AIMC Topic: Ligands

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Sfcnn: a novel scoring function based on 3D convolutional neural network for accurate and stable protein-ligand affinity prediction.

BMC bioinformatics Jun 8, 2022
BACKGROUND: Computer-aided drug design provides an effective method of identifying lead compounds. However, success rates are significantly bottlenecked by the lack of accurate and reliable scoring functions needed to evaluate binding affinities of p...
Neural Networks, Computer Ligands Proteins Machine Learning Protein Binding
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Translating from Proteins to Ribonucleic Acids for Ligand-binding Site Detection.

Molecular informatics Jun 1, 2022
Identifying druggable ligand-binding sites on the surface of the macromolecular targets is an important process in structure-based drug discovery. Deep-learning models have been shown to successfully predict ligand-binding sites of proteins. As a ste...
Neural Networks, Computer Ligands Binding Sites RNA Proteins
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TocoDecoy: A New Approach to Design Unbiased Datasets for Training and Benchmarking Machine-Learning Scoring Functions.

Journal of medicinal chemistry Jun 1, 2022
Development of accurate machine-learning-based scoring functions (MLSFs) for structure-based virtual screening against a given target requires a large unbiased dataset with structurally diverse actives and decoys. However, most datasets for the devel...
Molecular Conformation Ligands Machine Learning
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On the Frustration to Predict Binding Affinities from Protein-Ligand Structures with Deep Neural Networks.

Journal of medicinal chemistry May 24, 2022
Accurate prediction of binding affinities from protein-ligand atomic coordinates remains a major challenge in early stages of drug discovery. Using modular message passing graph neural networks describing both the ligand and the protein in their free...
Proteins Neural Networks, Computer Drug Discovery Ligands Protein Binding
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Prediction of protein-ligand binding affinity from sequencing data with interpretable machine learning.

Nature biotechnology May 23, 2022
Protein-ligand interactions are increasingly profiled at high throughput using affinity selection and massively parallel sequencing. However, these assays do not provide the biophysical parameters that most rigorously quantify molecular interactions....
Transcription Factors Chromatin Immunoprecipitation Machine Learning DNA Ligands Binding Sites Protein Binding
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Differences in ligand-induced protein dynamics extracted from an unsupervised deep learning approach correlate with protein-ligand binding affinities.

Communications biology May 19, 2022
Prediction of protein-ligand binding affinity is a major goal in drug discovery. Generally, free energy gap is calculated between two states (e.g., ligand binding and unbinding). The energy gap implicitly includes the effects of changes in protein dy...
Binding Sites Proteins Ligands Protein Binding Deep Learning
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Predicting target-ligand interactions with graph convolutional networks for interpretable pharmaceutical discovery.

Scientific reports May 19, 2022
Drug Discovery is an active research area that demands great investments and generates low returns due to its inherent complexity and great costs. To identify potential therapeutic candidates more effectively, we propose protein-ligand with adversari...
Proteins Neural Networks, Computer Ligands Polyesters Pharmaceutical Preparations
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Delta Machine Learning to Improve Scoring-Ranking-Screening Performances of Protein-Ligand Scoring Functions.

Journal of chemical information and modeling May 17, 2022
Protein-ligand scoring functions are widely used in structure-based drug design for fast evaluation of protein-ligand interactions, and it is of strong interest to develop scoring functions with machine-learning approaches. In this work, by expanding...
Ligands Proteins Reproducibility of Results Machine Learning Molecular Docking Simulation Protein Binding
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Emerging frontiers in virtual drug discovery: From quantum mechanical methods to deep learning approaches.

Current opinion in chemical biology May 13, 2022
Virtual screening-based approaches to discover initial hit and lead compounds have the potential to reduce both the cost and time of early drug discovery stages, as well as to find inhibitors for even challenging target sites such as protein-protein ...
Ligands Drug Discovery Proteins Deep Learning Machine Learning
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Decoding the protein-ligand interactions using parallel graph neural networks.

Scientific reports May 10, 2022
Protein-ligand interactions (PLIs) are essential for biochemical functionality and their identification is crucial for estimating biophysical properties for rational therapeutic design. Currently, experimental characterization of these properties is ...
Humans Neural Networks, Computer SARS-CoV-2 Proteins COVID-19 Artificial Intelligence Ligands
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