AIMC Topic: Quantitative Structure-Activity Relationship

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Graph Classification of Molecules Using Force Field Atom and Bond Types.

Molecular informatics Oct 7, 2019
Classification of the biological activities of chemical substances is important for developing new medicines efficiently. Various machine learning methods are often employed to screen large libraries of compounds and predict the activities of new sub...
Quantitative Structure-Activity Relationship Benzenesulfonates Azepines Machine Learning Molecular Structure Molecular Dynamics Simulation
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Cell fishing: A similarity based approach and machine learning strategy for multiple cell lines-compound sensitivity prediction.

PloS one Oct 7, 2019
The prediction of cell-lines sensitivity to a given set of compounds is a very important factor in the optimization of in-vitro assays. To date, the most common prediction strategies are based upon machine learning or other quantitative structure-act...
Humans Machine Learning Small Molecule Libraries Software Animals Cell Line Drug Discovery Quantitative Structure-Activity Relationship Drug Resistance
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Evaluating genotoxicity of metal oxide nanoparticles: Application of advanced supervised and unsupervised machine learning techniques.

Ecotoxicology and environmental safety Sep 30, 2019
Presence of missing data points in datasets is among main challenges in handling the toxicological data for nanomaterials. As the processing of missing data is an important part of data analysis, we have introduced a read-across approach that uses a ...
Quantitative Structure-Activity Relationship Humans Metal Nanoparticles Models, Theoretical Cell Line Comet Assay Oxides Mutagens DNA Damage Salmonella typhimurium Unsupervised Machine Learning
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Prediction Model with High-Performance Constitutive Androstane Receptor (CAR) Using DeepSnap-Deep Learning Approach from the Tox21 10K Compound Library.

International journal of molecular sciences Sep 30, 2019
The constitutive androstane receptor (CAR) plays pivotal roles in drug-induced liver injury through the transcriptional regulation of drug-metabolizing enzymes and transporters. Thus, identifying regulatory factors for CAR activation is important for...
Drug Discovery Deep Learning Ligands Quantitative Structure-Activity Relationship Receptors, Cytoplasmic and Nuclear Small Molecule Libraries Algorithms Reproducibility of Results Constitutive Androstane Receptor
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All-Assay-Max2 pQSAR: Activity Predictions as Accurate as Four-Concentration ICs for 8558 Novartis Assays.

Journal of chemical information and modeling Sep 26, 2019
Profile-quantitative structure-activity relationship (pQSAR) is a massively multitask, two-step machine learning method with unprecedented scope, accuracy, and applicability domain. In step one, a "profile" of conventional single-assay random forest ...
Biological Assay Inhibitory Concentration 50 Quantitative Structure-Activity Relationship Proteins Models, Chemical Machine Learning Drug Discovery Algorithms Logistic Models Dose-Response Relationship, Drug
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Toxicity Prediction Method Based on Multi-Channel Convolutional Neural Network.

Molecules (Basel, Switzerland) Sep 17, 2019
Molecular toxicity prediction is one of the key studies in drug design. In this paper, a deep learning network based on a two-dimension grid of molecules is proposed to predict toxicity. At first, the van der Waals force and hydrogen bond were calcul...
Quantitative Structure-Activity Relationship Neural Networks, Computer Algorithms Deep Learning Data Interpretation, Statistical Molecular Structure
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From Target to Drug: Generative Modeling for the Multimodal Structure-Based Ligand Design.

Molecular pharmaceutics Sep 10, 2019
Chemical space is impractically large, and conventional structure-based virtual screening techniques cannot be used to simply search through the entire space to discover effective bioactive molecules. To address this shortcoming, we propose a generat...
Humans Ligands Drug Discovery Neural Networks, Computer Molecular Conformation Small Molecule Libraries Proteins Drug Design Quantitative Structure-Activity Relationship Models, Chemical
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Identification of potential histone deacetylase1 (HDAC1) inhibitors using multistep virtual screening approach including SVM model, pharmacophore modeling, molecular docking and biological evaluation.

Journal of biomolecular structure & dynamics Sep 9, 2019
Histone Deacetylases (HDACs) play a significant role in the regulation of gene expression by modifying histones and non-histone substrates. Since they are key regulators in the reversible epigenetic mechanism, they are considered as promising drug ta...
Histone Deacetylase Inhibitors Small Molecule Libraries Molecular Docking Simulation Histone Deacetylases Support Vector Machine Molecular Dynamics Simulation Quantitative Structure-Activity Relationship Histones
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A Machine Learning-Based QSAR Model for Benzimidazole Derivatives as Corrosion Inhibitors by Incorporating Comprehensive Feature Selection.

Interdisciplinary sciences, computational life sciences Sep 4, 2019
BACKGROUND: Computational prediction of inhibition efficiency (IE) for inhibitor molecules is a crucial supplementary way to design novel molecules that can efficiently inhibit corrosion onto metallic surfaces.
Hydrazines Linear Models Conservation of Natural Resources Cluster Analysis Predictive Value of Tests Chemistry Corrosion Quantitative Structure-Activity Relationship Static Electricity Benzimidazoles Machine Learning Algorithms Metals Imaging, Three-Dimensional Computational Biology Support Vector Machine
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Rivality index neighbourhood algorithm with density and distances weighted schemes for the building of robust QSAR classification models with high reliable applicability domain.

SAR and QSAR in environmental research Aug 30, 2019
The rivality index () is a normalized distance measurement between a molecule and their first nearest neighbours providing a robust prediction of the activity of a molecule based on the known activity of their nearest neighbours. Negative values of t...
Quantitative Structure-Activity Relationship Machine Learning Reproducibility of Results Algorithms Models, Theoretical
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