AIMC Topic: Structure-Activity Relationship

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Machine learning approaches for elucidating the biological effects of natural products.

Natural product reports Mar 4, 2021
Covering: 2000 to 2020 Machine learning (ML) is an efficient tool for the prediction of bioactivity and the study of structure-activity relationships. Over the past decade, an emerging trend for combining these approaches with the study of natural pr...
Machine Learning Algorithms Anti-Infective Agents Humans Biological Products Structure-Activity Relationship Databases, Factual Anti-Inflammatory Agents Drug Discovery Antineoplastic Agents
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Identification of novel CDK2 inhibitors by a multistage virtual screening method based on SVM, pharmacophore and docking model.

Journal of enzyme inhibition and medicinal chemistry Dec 1, 2020
Cyclin-dependent kinase 2 (CDK2) is the family of Ser/Thr protein kinases that has emerged as a highly selective with low toxic cancer therapy target. A multistage virtual screening method combined by SVM, protein-ligand interaction fingerprints (PLI...
Protein Kinase Inhibitors Cyclin-Dependent Kinase 2 Drug Screening Assays, Antitumor Cell Proliferation Structure-Activity Relationship Support Vector Machine A549 Cells Dose-Response Relationship, Drug Humans Molecular Structure Molecular Docking Simulation Drug Evaluation, Preclinical Antineoplastic Agents HCT116 Cells
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Systematic selection of chemical fingerprint features improves the Gibbs energy prediction of biochemical reactions.

Bioinformatics (Oxford, England) Aug 1, 2019
MOTIVATION: Accurate and wide-ranging prediction of thermodynamic parameters for biochemical reactions can facilitate deeper insights into the workings and the design of metabolic systems.
Machine Learning Software Structure-Activity Relationship
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NetGO: improving large-scale protein function prediction with massive network information.

Nucleic acids research Jul 2, 2019
Automated function prediction (AFP) of proteins is of great significance in biology. AFP can be regarded as a problem of the large-scale multi-label classification where a protein can be associated with multiple gene ontology terms as its labels. Bas...
Gene Ontology Protein Interaction Mapping Sequence Alignment Plants Amino Acid Sequence Molecular Sequence Annotation Sequence Homology, Amino Acid Sequence Analysis, Protein Prokaryotic Cells Proteins Databases, Protein Structure-Activity Relationship Models, Molecular Internet Benchmarking Software Animals Computational Biology Humans Machine Learning
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Computational Approaches as Rational Decision Support Systems for Discovering Next-Generation Antitubercular Agents: Mini-Review.

Current computer-aided drug design Jan 1, 2019
Tuberculosis, malaria, dengue, chikungunya, leishmaniasis etc. are a large group of neglected tropical diseases that prevail in tropical and subtropical countries, affecting one billion people every year. Minimal funding and grants for research on th...
Drug Design Antitubercular Agents Drug Discovery Mycobacterium tuberculosis Humans Tuberculosis Molecular Targeted Therapy Structure-Activity Relationship Machine Learning Ligands Animals
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Machine Learning Approach for Predicting New Uses of Existing Drugs and Evaluation of Their Reliabilities.

Methods in molecular biology (Clifton, N.J.) Jan 1, 2019
In this chapter, a new method to evaluate the reliability of predicting new uses of existing drugs was proposed. The prediction was performed with a support vector machine (SVM) using various data. Because the reliability of prediction could not be e...
Structure-Activity Relationship Computational Biology Support Vector Machine Drug Repositioning Machine Learning Humans Algorithms Reproducibility of Results
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Oy Vey! A Comment on "Machine Learning of Toxicological Big Data Enables Read-Across Structure Activity Relationships Outperforming Animal Test Reproducibility".

Toxicological sciences : an official journal of the Society of Toxicology Jan 1, 2019
Machine Learning Reproducibility of Results Animals Big Data Structure-Activity Relationship
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Virtual Screening Meets Deep Learning.

Current computer-aided drug design Jan 1, 2019
BACKGROUND: Automated compound testing is currently the de facto standard method for drug screening, but it has not brought the great increase in the number of new drugs that was expected. Computer- aided compounds search, known as Virtual Screening,...
Structure-Activity Relationship Drug Evaluation, Preclinical Computer Simulation Drug Discovery Deep Learning
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A Novel Amino Acid Sequence-based Computational Approach to Predicting Cell-penetrating Peptides.

Current computer-aided drug design Jan 1, 2019
INTRODUCTION: Machine Learning is a useful tool for the prediction of cell-penetration compounds as drug candidates.
Cell-Penetrating Peptides Machine Learning Computer Simulation Computational Biology Humans Structure-Activity Relationship Amino Acid Sequence Cell Membrane Permeability
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Machine Learning of Toxicological Big Data Enables Read-Across Structure Activity Relationships (RASAR) Outperforming Animal Test Reproducibility.

Toxicological sciences : an official journal of the Society of Toxicology Sep 1, 2018
Earlier we created a chemical hazard database via natural language processing of dossiers submitted to the European Chemical Agency with approximately 10 000 chemicals. We identified repeat OECD guideline tests to establish reproducibility of acute o...
Models, Theoretical Structure-Activity Relationship Humans Data Mining Sensitivity and Specificity Machine Learning Databases, Chemical Animals Reproducibility of Results Animal Testing Alternatives Big Data Hazardous Substances
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