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Quantitative Structure-Activity Relationship

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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...
Humans Machine Learning Algorithms ROC Curve Models, Molecular Molecular Structure Molecular Dynamics Simulation Reproducibility of Results Molecular Docking Simulation Drug Discovery Diacylglycerol O-Acyltransferase Cheminformatics Enzyme Inhibitors Databases, Chemical Quantitative Structure-Activity Relationship
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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...
Quantitative Structure-Activity Relationship Algorithms Neural Networks, Computer Drug Discovery Bayes Theorem Models, Theoretical Deep Learning Mutagens Molecular Structure Mutagenesis
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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...
Neural Networks, Computer Machine Learning Algorithms Drug Design Humans Pharmacokinetics Support Vector Machine Quantitative Structure-Activity Relationship Tissue Distribution Models, Molecular ERG1 Potassium Channel Drug-Related Side Effects and Adverse Reactions
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Integrated support vector machine and pharmacophore based virtual screening driven identification of thiophene carboxamide scaffold containing compound as potential PARP1 inhibitor.

Journal of biomolecular structure & dynamics May 5, 2021
Poly (ADP-ribose) polymerase-1 (PARP1) inhibition strategy for cancer treatment is gaining advantage particularly in patients having a mutation in BRCA1/BRCA2 gene. To date, four drugs have obtained FDA approval and some inhibitors are in clinical tr...
Poly (ADP-Ribose) Polymerase-1 Ligands Thiophenes Quantitative Structure-Activity Relationship Support Vector Machine Molecular Dynamics Simulation Humans Molecular Docking Simulation
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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...
Molecular Structure Quantitative Structure-Activity Relationship Cytochrome P-450 Enzyme Inhibitors Humans Artificial Intelligence Drug Discovery Machine Learning Predictive Value of Tests Computer Simulation Models, Molecular Drug Design
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Support vector machine-based model for toxicity of organic compounds against fish.

Regulatory toxicology and pharmacology : RTP Apr 30, 2021
Predicting the toxicity of chemicals to various fish species through chemometric approach is crucial for ecotoxicological assessment of existing as well as not yet synthesized chemicals. This paper reports a quantitative structure-activity/toxicity r...
Water Pollutants, Chemical Fishes Algorithms Quantitative Structure-Activity Relationship Ecotoxicology Animals Organic Chemicals Support Vector Machine
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Advances in Predictions of Oral Bioavailability of Candidate Drugs in Man with New Machine Learning Methodology.

Molecules (Basel, Switzerland) Apr 28, 2021
Oral bioavailability (F) is an essential determinant for the systemic exposure and dosing regimens of drug candidates. F is determined by numerous processes, and computational predictions of human estimates have so far shown limited results. We descr...
Pharmacokinetics Supervised Machine Learning Quantitative Structure-Activity Relationship Administration, Oral Computer Simulation Humans Models, Biological Biological Availability Pharmaceutical Preparations Machine Learning Drug Evaluation, Preclinical
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Predicting drug metabolism and pharmacokinetics features of in-house compounds by a hybrid machine-learning model.

Drug metabolism and pharmacokinetics Apr 20, 2021
We constructed machine learning-based pharmacokinetic prediction models with very high performance. The models were trained on 26138 and 16613 compounds involved in metabolic stability and cytochrome P450 inhibition, respectively. Because the compoun...
Pharmaceutical Preparations Cytochrome P-450 Enzyme System Pharmacokinetics Reproducibility of Results Inactivation, Metabolic Humans Quantitative Structure-Activity Relationship Drug Discovery Machine Learning Metabolic Clearance Rate Predictive Value of Tests Computer Simulation
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Artificial intelligence to deep learning: machine intelligence approach for drug discovery.

Molecular diversity Apr 12, 2021
Drug designing and development is an important area of research for pharmaceutical companies and chemical scientists. However, low efficacy, off-target delivery, time consumption, and high cost impose a hurdle and challenges that impact drug design a...
Machine Learning Support Vector Machine Algorithms Models, Molecular Data Mining Drug Discovery Humans Research Design Big Data Deep Learning Quantitative Structure-Activity Relationship Artificial Intelligence Animals Drug Development Chemistry Techniques, Synthetic Drug Design
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Density Prediction Models for Energetic Compounds Merely Using Molecular Topology.

Journal of chemical information and modeling Apr 12, 2021
Newly developed high-throughput methods for property predictions make the process of materials design faster and more efficient. Density is an important physical property for energetic compounds to assess detonation velocity and detonation pressure, ...
Support Vector Machine Quantitative Structure-Activity Relationship Nitro Compounds Neural Networks, Computer
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