AI Medical Compendium Journal:
Molecular diversity

Showing 51 to 60 of 70 articles

A multi-conformational virtual screening approach based on machine learning targeting PI3Kγ.

Molecular diversity Jun 23, 2021
Nowadays, more and more attention has been attracted to develop selective PI3Kγ inhibitors, but the unique structural features of PI3Kγ protein make it a very big challenge. In the present study, a virtual screening strategy based on machine learning...
Molecular Docking Simulation Databases, Pharmaceutical Drug Discovery Class Ib Phosphatidylinositol 3-Kinase ROC Curve Protein Binding Machine Learning Structure-Activity Relationship Models, Molecular Ligands Molecular Structure Molecular Conformation Molecular Dynamics Simulation Binding Sites Phosphoinositide-3 Kinase Inhibitors
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Discovery of novel DGAT1 inhibitors by combination of machine learning methods, pharmacophore model and 3D-QSAR model.

Molecular diversity Jun 23, 2021
DGAT1 plays a crucial controlling role in triglyceride biosynthetic pathways, which makes it an attractive therapeutic target for obesity. Thus, development of DGAT1 inhibitors with novel chemical scaffolds is desired and important in the drug discov...
Drug Discovery ROC Curve Models, Molecular Molecular Structure Molecular Dynamics Simulation Enzyme Inhibitors Machine Learning Quantitative Structure-Activity Relationship Databases, Chemical Algorithms Diacylglycerol O-Acyltransferase Reproducibility of Results Cheminformatics Molecular Docking Simulation Humans
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Evolving scenario of big data and Artificial Intelligence (AI) in drug discovery.

Molecular diversity Jun 23, 2021
The accumulation of massive data in the plethora of Cheminformatics databases has made the role of big data and artificial intelligence (AI) indispensable in drug design. This has necessitated the development of newer algorithms and architectures to ...
Reproducibility of Results Drug Design Big Data Drug Discovery Artificial Intelligence Workflow Deep Learning Machine Learning Algorithms Databases, Factual
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QSAR modeling without descriptors using graph convolutional neural networks: the case of mutagenicity prediction.

Molecular diversity Jun 19, 2021
Deep neural networks are effective in learning directly from low-level encoded data without the need of feature extraction. This paper shows how QSAR models can be constructed from 2D molecular graphs without computing chemical descriptors. Two graph...
Mutagenesis Molecular Structure Bayes Theorem Drug Discovery Mutagens Deep Learning Quantitative Structure-Activity Relationship Neural Networks, Computer Models, Theoretical Algorithms
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Machine learning models to select potential inhibitors of acetylcholinesterase activity from SistematX: a natural products database.

Molecular diversity Jun 16, 2021
Alzheimer's disease is the most common form of dementia, representing 60-70% of dementia cases. The enzyme acetylcholinesterase (AChE) cleaves the ester bonds in acetylcholine and plays an important role in the termination of acetylcholine activity a...
Models, Molecular Workflow Molecular Dynamics Simulation Acetylcholinesterase Biological Products Structure-Activity Relationship Area Under Curve Cholinesterase Inhibitors Cheminformatics Databases, Pharmaceutical Humans Machine Learning Algorithms Molecular Docking Simulation Drug Discovery ROC Curve
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Development and validation of consensus machine learning-based models for the prediction of novel small molecules as potential anti-tubercular agents.

Molecular diversity Jun 10, 2021
Tuberculosis (TB) is an infectious disease and the leading cause of death globally. The rapidly emerging cases of drug resistance among pathogenic mycobacteria have been a global threat urging the need of new drug discovery and development. However, ...
Support Vector Machine Bayes Theorem Antitubercular Agents Machine Learning Algorithms Consensus
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Applications of artificial intelligence to drug design and discovery in the big data era: a comprehensive review.

Molecular diversity Jun 10, 2021
Artificial intelligence (AI) renders cutting-edge applications in diverse sectors of society. Due to substantial progress in high-performance computing, the development of superior algorithms, and the accumulation of huge biological and chemical data...
Artificial Intelligence Algorithms Drug Design Drug Discovery Humans Data Mining Protein Interaction Mapping Protein Folding Databases, Pharmaceutical Proteins Big Data Structure-Activity Relationship Protein Binding Models, Molecular Models, Theoretical Workflow
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Machine learning models for classification tasks related to drug safety.

Molecular diversity Jun 10, 2021
In this review, we outline the current trends in the field of machine learning-driven classification studies related to ADME (absorption, distribution, metabolism and excretion) and toxicity endpoints from the past six years (2015-2021). The study fo...
Humans Neural Networks, Computer Machine Learning Algorithms Pharmacokinetics Drug Design Quantitative Structure-Activity Relationship Support Vector Machine Tissue Distribution Models, Molecular ERG1 Potassium Channel Drug-Related Side Effects and Adverse Reactions
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Convolutional neural networks (CNNs): concepts and applications in pharmacogenomics.

Molecular diversity May 24, 2021
Convolutional neural networks (CNNs) have been used to extract information from various datasets of different dimensions. This approach has led to accurate interpretations in several subfields of biological research, like pharmacogenomics, addressing...
Precision Medicine Binding Sites Neural Networks, Computer Drug Development Regulatory Sequences, Nucleic Acid Artificial Intelligence DNA Pharmacogenetics Drug Interactions Data Analysis Polymorphism, Single Nucleotide RNA
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Machine learning-based modeling to predict inhibitors of acetylcholinesterase.

Molecular diversity Apr 23, 2021
Acetylcholinesterase enzyme is responsible for the degradation of acetylcholine and is an important drug target for the treatment of Alzheimer's disease. When this enzyme is inhibited, more acetylcholine is available in the synaptic cleft for the use...
Acetylcholinesterase Machine Learning Algorithms Support Vector Machine
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