AIMC Topic: Ligands

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Deep Learning in Drug Design: Protein-Ligand Binding Affinity Prediction.

IEEE/ACM transactions on computational biology and bioinformatics Feb 3, 2022
Computational drug design relies on the calculation of binding strength between two biological counterparts especially a chemical compound, i.e., a ligand, and a protein. Predicting the affinity of protein-ligand binding with reasonable accuracy is c...
Deep Learning Protein Binding Ligands Molecular Docking Simulation Drug Design
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AI in 3D compound design.

Current opinion in structural biology Jan 31, 2022
The success of Artificial Intelligence (AI) across a wide range of domains has fuelled significant interest in its application to designing novel compounds and screening compounds against a specific target. However, many existing AI methods either do...
Drug Design Drug Discovery Ligands Artificial Intelligence Machine Learning
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Predicting Ca and Mg ligand binding sites by deep neural network algorithm.

BMC bioinformatics Jan 20, 2022
BACKGROUND: Alkaline earth metal ions are important protein binding ligands in human body, and it is of great significance to predict their binding residues.
Binding Sites Protein Binding Ligands Humans Neural Networks, Computer Algorithms
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Novel Big Data-Driven Machine Learning Models for Drug Discovery Application.

Molecules (Basel, Switzerland) Jan 18, 2022
Most contemporary drug discovery projects start with a 'hit discovery' phase where small chemicals are identified that have the capacity to interact, in a chemical sense, with a protein target involved in a given disease. To assist and accelerate thi...
Big Data Protein Binding Ligands Humans Protein Conformation Computer Simulation Receptor, Adenosine A2A Machine Learning Drug Discovery Receptors, Opioid, kappa Algorithms
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Ligand Based Virtual Screening Using Self-organizing Maps.

The protein journal Jan 13, 2022
Conventional drug discovery methods rely primarily on in-vitro experiments with a target molecule and an extensive set of small molecules to choose the suitable ligand. The exploration space for the selected ligand being huge; this approach is highly...
Support Vector Machine Ligands Neural Networks, Computer Algorithms Drug Discovery
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QPoweredCompound2DeNovoDrugPropMax - a novel programmatic tool incorporating deep learning and methods for automated in silico bio-activity discovery for any compound of interest.

Journal of biomolecular structure & dynamics Jan 10, 2022
Network data is composed of nodes and edges. Successful application of machine learning/deep learning algorithms on network data to make node classification and link prediction have been shown in the area of social networks through which highly custo...
Neural Networks, Computer Deep Learning Quantum Theory Ligands Computing Methodologies
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Ensemble learning from ensemble docking: revisiting the optimum ensemble size problem.

Scientific reports Jan 10, 2022
Despite considerable advances obtained by applying machine learning approaches in protein-ligand affinity predictions, the incorporation of receptor flexibility has remained an important bottleneck. While ensemble docking has been used widely as a so...
Machine Learning Molecular Docking Simulation Structure-Activity Relationship Ligands Protein Binding Support Vector Machine Binding Sites Crystallography, X-Ray Cyclin-Dependent Kinase 2 Protein Conformation
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PlayMolecule Glimpse: Understanding Protein-Ligand Property Predictions with Interpretable Neural Networks.

Journal of chemical information and modeling Jan 3, 2022
Deep learning has been successfully applied to structure-based protein-ligand affinity prediction, yet the black box nature of these models raises some questions. In a previous study, we presented K, a convolutional neural network that predicted the ...
Proteins Ligands Neural Networks, Computer
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Protein embeddings and deep learning predict binding residues for various ligand classes.

Scientific reports Dec 13, 2021
One important aspect of protein function is the binding of proteins to ligands, including small molecules, metal ions, and macromolecules such as DNA or RNA. Despite decades of experimental progress many binding sites remain obscure. Here, we propose...
Protein Binding Ligands Binding Sites Nucleic Acids Sequence Analysis, Protein Protein Conformation Deep Learning Software Metals Molecular Docking Simulation
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Synergy and Complementarity between Focused Machine Learning and Physics-Based Simulation in Affinity Prediction.

Journal of chemical information and modeling Dec 10, 2021
We present results on the extent to which physics-based simulation (exemplified by FEP) and focused machine learning (exemplified by QuanSA) are complementary for ligand affinity prediction. For both methods, predictions of activity for LFA-1 inhibit...
Machine Learning Computer Simulation Physics Molecular Docking Simulation Ligands
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