AIMC Topic: Ligands

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Target-Specific Prediction of Ligand Affinity with Structure-Based Interaction Fingerprints.

Journal of chemical information and modeling Aug 19, 2019
Discovery and optimization of small molecule inhibitors as therapeutic drugs have immensely benefited from rational structure-based drug design. With recent advances in high-resolution structure determination, computational power, and machine learnin...
Humans Machine Learning Molecular Docking Simulation HIV-1 Ligands HIV Protease HIV Protease Inhibitors Drug Design Drug Discovery HIV Infections Protein Binding
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Significance of Data Selection in Deep Learning for Reliable Binding Mode Prediction of Ligands in the Active Site of CYP3A4.

Chemical & pharmaceutical bulletin Aug 17, 2019
For rational drug design, it is essential to predict the binding mode of protein-ligand complexes. Although various machine learning-based models have been reported that use convolutional neural networks (deep learning) to predict binding modes from ...
Deep Learning Ligands Cytochrome P-450 CYP3A Binding Sites Humans Databases, Factual
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Artificial Intelligence Approach To Investigate the Longevity Drug.

The journal of physical chemistry letters Aug 14, 2019
Longevity is a very important and interesting topic, and has been demonstrated to be related to longevity. We combined network pharmacology, machine learning, deep learning, and molecular dynamics (MD) simulation to investigate potent lead drugs. Re...
Receptor, Insulin Inhibitory Concentration 50 Binding Sites Thermodynamics Signal Transduction Molecular Docking Simulation Glucuronidase Medicine, Chinese Traditional Drug Discovery Klotho Proteins Protein Interaction Maps Protein Binding Receptor, IGF Type 1 Artificial Intelligence Antigens, CD Algorithms Ligands Databases, Factual
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Applicability Domain of Active Learning in Chemical Probe Identification: Convergence in Learning from Non-Specific Compounds and Decision Rule Clarification.

Molecules (Basel, Switzerland) Jul 26, 2019
Efficient identification of chemical probes for the manipulation and understanding of biological systems demands specificity for target proteins. Computational means to optimize candidate compound selection for experimental selectivity evaluation are...
Computational Biology Models, Theoretical Decision Trees Algorithms Ligands Chemical Phenomena Decision Support Techniques Reproducibility of Results Databases, Chemical Drug Discovery Machine Learning Quantitative Structure-Activity Relationship
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Artificial Intelligence Approach to Find Lead Compounds for Treating Tumors.

The journal of physical chemistry letters Jul 22, 2019
It has been demonstrated that MMP13 enzyme is related to most cancer cell tumors. The world's largest traditional Chinese medicine database was applied to screen for structure-based drug design and ligand-based drug design. To predict drug activity, ...
Matrix Metalloproteinase 13 Proto-Oncogene Proteins c-mdm2 Cyclin-Dependent Kinase 6 Databases, Pharmaceutical Databases, Protein Models, Molecular Mutation Ligands Proto-Oncogene Proteins c-bcl-2 Tumor Suppressor Protein p53 Binding Sites Antineoplastic Agents Deep Learning Cyclin-Dependent Kinase Inhibitor p16 Proteins Algorithms Peptides Drug Design Machine Learning
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Artificial Intelligence: A Novel Approach for Drug Discovery.

Trends in pharmacological sciences Jul 3, 2019
Molecular dynamics (MD) simulations can mechanistically explain receptor function. However, the enormous data sets that they may imply can be a hurdle. Plante and colleagues (Molecules, 2019) recently described a machine learning approach to the anal...
Artificial Intelligence Machine Learning Drug Discovery Molecular Dynamics Simulation Ligands
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A Ligand-Based Virtual Screening Method Using Direct Quantification of Generalization Ability.

Molecules (Basel, Switzerland) Jun 30, 2019
Machine learning plays an important role in ligand-based virtual screening. However, conventional machine learning approaches tend to be inefficient when dealing with such problems where the data are imbalanced and features describing the chemical ch...
Models, Chemical HIV-1 Machine Learning Ligands Computer Simulation HIV Integrase
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DeepConv-DTI: Prediction of drug-target interactions via deep learning with convolution on protein sequences.

PLoS computational biology Jun 14, 2019
Identification of drug-target interactions (DTIs) plays a key role in drug discovery. The high cost and labor-intensive nature of in vitro and in vivo experiments have highlighted the importance of in silico-based DTI prediction approaches. In severa...
Ligands Binding Sites Sequence Analysis, Protein Computational Biology Proteins Computer Simulation Models, Molecular Drug Discovery Amino Acid Sequence Deep Learning
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A Machine Learning Approach for the Discovery of Ligand-Specific Functional Mechanisms of GPCRs.

Molecules (Basel, Switzerland) Jun 2, 2019
G protein-coupled receptors (GPCRs) play a key role in many cellular signaling mechanisms, and must select among multiple coupling possibilities in a ligand-specific manner in order to carry out a myriad of functions in diverse cellular contexts. Muc...
Drug Design Drug Discovery Protein Binding Receptors, G-Protein-Coupled Molecular Dynamics Simulation Ligands Receptor, Serotonin, 5-HT2A Humans Machine Learning Algorithms Molecular Docking Simulation Quantitative Structure-Activity Relationship Binding Sites
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Design of Natural-Product-Inspired Multitarget Ligands by Machine Learning.

ChemMedChem May 16, 2019
A virtual screening protocol based on machine learning models was used to identify mimetics of the natural product (-)-galantamine. This fully automated approach identified eight compounds with bioactivities on at least one of the macromolecular targ...
Stereoisomerism Molecular Structure Drug Design Drug Evaluation, Preclinical Cell Line, Tumor Alzheimer Disease Ligands Humans Neuroprotective Agents Acetylcholinesterase Machine Learning Biological Products Cholinesterase Inhibitors Molecular Docking Simulation Galantamine
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