AIMC Topic: Ligands

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Benchmarking Active Learning Protocols for Ligand-Binding Affinity Prediction.

Journal of chemical information and modeling Mar 6, 2024
Active learning (AL) has become a powerful tool in computational drug discovery, enabling the identification of top binders from vast molecular libraries. To design a robust AL protocol, it is important to understand the influence of AL parameters, a...
Ligands Machine Learning Drug Discovery
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Enhancing Generalizability in Protein-Ligand Binding Affinity Prediction with Multimodal Contrastive Learning.

Journal of chemical information and modeling Mar 5, 2024
Improving the generalization ability of scoring functions remains a major challenge in protein-ligand binding affinity prediction. Many machine learning methods are limited by their reliance on single-modal representations, hindering a comprehensive ...
Neural Networks, Computer Mutation Ligands Machine Learning Drug Discovery
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Machine Learning for Sequence and Structure-Based Protein-Ligand Interaction Prediction.

Journal of chemical information and modeling Feb 22, 2024
Developing new drugs is too expensive and time -consuming. Accurately predicting the interaction between drugs and targets will likely change how the drug is discovered. Machine learning-based protein-ligand interaction prediction has demonstrated si...
Proteins Ligands Machine Learning
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graphLambda: Fusion Graph Neural Networks for Binding Affinity Prediction.

Journal of chemical information and modeling Feb 17, 2024
Predicting the binding affinity of protein-ligand complexes is crucial for computer-aided drug discovery (CADD) and the identification of potential drug candidates. The deep learning-based scoring functions have emerged as promising predictors of bin...
Drug Discovery Protein Binding Proteins Ligands Neural Networks, Computer
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An ensemble-based approach to estimate confidence of predicted protein-ligand binding affinity values.

Molecular informatics Feb 15, 2024
When designing a machine learning-based scoring function, we access a limited number of protein-ligand complexes with experimentally determined binding affinity values, representing only a fraction of all possible protein-ligand complexes. Consequent...
Ligands Protein Binding Proteins Machine Learning
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Machine learning approaches in predicting allosteric sites.

Current opinion in structural biology Feb 13, 2024
Allosteric regulation is a fundamental biological mechanism that can control critical cellular processes via allosteric modulator binding to protein distal functional sites. The advantages of allosteric modulators over orthosteric ones have sparked t...
Binding Sites Allosteric Site Ligands Allosteric Regulation Artificial Intelligence Machine Learning Proteins
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Machine Learning Methods as a Cost-Effective Alternative to Physics-Based Binding Free Energy Calculations.

Molecules (Basel, Switzerland) Feb 13, 2024
The rank ordering of ligands remains one of the most attractive challenges in drug discovery. While physics-based in silico binding affinity methods dominate the field, they still have problems, which largely revolve around forcefield accuracy and sa...
Proteins Machine Learning Protein Binding Ligands Thermodynamics Retrospective Studies Cost-Benefit Analysis Physics Binding Sites
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Identifying potential ligand-receptor interactions based on gradient boosted neural network and interpretable boosting machine for intercellular communication analysis.

Computers in biology and medicine Feb 6, 2024
Cell-cell communication is essential to many key biological processes. Intercellular communication is generally mediated by ligand-receptor interactions (LRIs). Thus, building a comprehensive and high-quality LRI resource can significantly improve in...
Humans Neural Networks, Computer Molecular Docking Simulation Squamous Cell Carcinoma of Head and Neck Ligands Head and Neck Neoplasms
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A deep learning-based theoretical protocol to identify potentially isoform-selective PI3Kα inhibitors.

Molecular diversity Feb 2, 2024
Phosphoinositide 3-kinase alpha (PI3Kα) is one of the most frequently dysregulated kinases known for their pivotal role in many oncogenic diseases. While the side effects linked to existing drugs against PI3Kα-induced cancers provide an avenue for fu...
Molecular Docking Simulation Drug Design Humans Phosphatidylinositol 3-Kinases Protein Binding Deep Learning Ligands Class I Phosphatidylinositol 3-Kinases Protein Kinase Inhibitors Phosphoinositide-3 Kinase Inhibitors
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Improving structure-based protein-ligand affinity prediction by graph representation learning and ensemble learning.

PloS one Jan 17, 2024
Predicting protein-ligand binding affinity presents a viable solution for accelerating the discovery of new lead compounds. The recent widespread application of machine learning approaches, especially graph neural networks, has brought new advancemen...
Correlation of Data Generalization, Psychological Machine Learning Neural Networks, Computer Ligands
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