AIMC Topic: Molecular Structure

Clear Filters Showing 111 to 120 of 329 articles

Molecular insights on ABL kinase activation using tree-based machine learning models and molecular docking.

Molecular diversity Jun 30, 2021
Abelson kinase (c-Abl) is a non-receptor tyrosine kinase involved in several biological processes essential for cell differentiation, migration, proliferation, and survival. This enzyme's activation might be an alternative strategy for treating disea...
Molecular Dynamics Simulation Ligands Quantitative Structure-Activity Relationship Protein Kinase Inhibitors Binding Sites Proto-Oncogene Proteins c-abl Molecular Docking Simulation Drug Design Protein Binding Molecular Structure Humans Machine Learning
View on PubMed DOI

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...
Protein Binding Structure-Activity Relationship Models, Molecular Molecular Structure Molecular Docking Simulation Drug Discovery ROC Curve Phosphoinositide-3 Kinase Inhibitors Databases, Pharmaceutical Class Ib Phosphatidylinositol 3-Kinase Molecular Dynamics Simulation Ligands Molecular Conformation Binding Sites Machine Learning
View on PubMed DOI

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...
Humans Molecular Structure Molecular Dynamics Simulation Enzyme Inhibitors Molecular Docking Simulation Databases, Chemical Machine Learning Quantitative Structure-Activity Relationship Drug Discovery Diacylglycerol O-Acyltransferase Algorithms ROC Curve Cheminformatics Reproducibility of Results Models, Molecular
View on PubMed DOI

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...
Deep Learning Models, Theoretical Molecular Structure Bayes Theorem Neural Networks, Computer Algorithms Drug Discovery Mutagens Quantitative Structure-Activity Relationship Mutagenesis
View on PubMed DOI

MLatom 2: An Integrative Platform for Atomistic Machine Learning.

Topics in current chemistry (Cham) Jun 8, 2021
Atomistic machine learning (AML) simulations are used in chemistry at an ever-increasing pace. A large number of AML models has been developed, but their implementations are scattered among different packages, each with its own conventions for input ...
Software Computer Simulation Molecular Structure Hydrocarbons, Cyclic Machine Learning
View on PubMed DOI

Feature importance of machine learning prediction models shows structurally active part and important physicochemical features in drug design.

Drug metabolism and pharmacokinetics May 3, 2021
The objective of this study was to obtain the indicators of physicochemical parameters and structurally active sites to design new chemical entities with desirable pharmacokinetic profiles by investigating the process by which machine learning predic...
Cytochrome P-450 Enzyme Inhibitors Machine Learning Computer Simulation Drug Design Drug Discovery Predictive Value of Tests Models, Molecular Humans Molecular Structure Artificial Intelligence Quantitative Structure-Activity Relationship
View on PubMed DOI

Machine Learning Prediction of Allosteric Drug Activity from Molecular Dynamics.

The journal of physical chemistry letters Apr 12, 2021
Allosteric drugs have been attracting increasing interest over the past few years. In this context, it is common practice to use high-throughput screening for the discovery of non-natural allosteric drugs. While the discovery stage is supported by a ...
Coumarins Dihydropyridines Allosteric Regulation Molecular Structure Molecular Dynamics Simulation Biphenyl Compounds Pyrones Humans Machine Learning
View on PubMed DOI

Identification of novel inhibitors of Keap1/Nrf2 by a promising method combining protein-protein interaction-oriented library and machine learning.

Scientific reports Apr 1, 2021
Protein-protein interactions (PPIs) are prospective but challenging targets for drug discovery, because screening using traditional small-molecule libraries often fails to identify hits. Recently, we developed a PPI-oriented library comprising 12,593...
Protein Interaction Maps Protein Binding Structure-Activity Relationship Molecular Structure Molecular Dynamics Simulation Ligands Molecular Conformation Humans Machine Learning Molecular Docking Simulation Drug Discovery Protein Interaction Mapping NF-E2-Related Factor 2 Kelch-Like ECH-Associated Protein 1 Binding Sites Small Molecule Libraries
View on PubMed DOI

Classification and Design of HIV-1 Integrase Inhibitors Based on Machine Learning.

Computational and mathematical methods in medicine Apr 1, 2021
A key enzyme in human immunodeficiency virus type 1 (HIV-1) life cycle, integrase (IN) aids the integration of viral DNA into the host DNA, which has become an ideal target for the development of anti-HIV drugs. A total of 1785 potential HIV-1 IN inh...
HIV Infections Molecular Structure Humans HIV Integrase Inhibitors Computational Biology Databases, Pharmaceutical Bayes Theorem Machine Learning Decision Trees HIV-1 HIV Integrase Drug Design Structure-Activity Relationship
View on PubMed DOI

Epigenetic Target Fishing with Accurate Machine Learning Models.

Journal of medicinal chemistry Mar 26, 2021
Epigenetic targets are of significant importance in drug discovery research, as demonstrated by the eight approved epigenetic drugs for treatment of cancer and the increasing availability of chemogenomic data related to epigenetics. This data represe...
Transcription Factors Structure-Activity Relationship Molecular Structure Databases, Chemical Drug Discovery Organic Chemicals Epigenomics Proof of Concept Study Machine Learning Histone Deacetylases
View on PubMed DOI
Popular Topics
Recent Journals