AIMC Topic: Structure-Activity Relationship

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Multistage virtual screening and identification of novel HIV-1 protease inhibitors by integrating SVM, shape, pharmacophore and docking methods.

European journal of medicinal chemistry Jul 6, 2015
The HIV-1 protease has proven to be a crucial component of the HIV replication machinery and a reliable target for anti-HIV drug discovery. In this study, we applied an optimized hierarchical multistage virtual screening method targeting HIV-1 protea...
Molecular Docking Simulation Support Vector Machine Dose-Response Relationship, Drug HIV Protease HIV Protease Inhibitors Structure-Activity Relationship Molecular Structure Drug Evaluation, Preclinical
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The study of dual COX-2/5-LOX inhibitors by using electronic-topological approach based on data on the ligand-receptor interactions.

Journal of molecular graphics & modelling Jun 19, 2015
Structural and electronic factors influencing selective inhibition of cyclooxygenase-2 and 5-lipoxygenase (COX-2/5-LOX) were studied by using Electronic-Topological Method combined with Neural Networks (ETM-NN), molecular docking, and Density Functio...
Cyclooxygenase 2 Lipoxygenase Inhibitors Substrate Specificity Molecular Structure Quantum Theory Ligands Catalytic Domain Arachidonate 5-Lipoxygenase Cyclooxygenase 2 Inhibitors Datasets as Topic Molecular Docking Simulation Protein Binding Structure-Activity Relationship Humans Neural Networks, Computer Algorithms
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Machine learning assisted design of highly active peptides for drug discovery.

PLoS computational biology Apr 7, 2015
The discovery of peptides possessing high biological activity is very challenging due to the enormous diversity for which only a minority have the desired properties. To lower cost and reduce the time to obtain promising peptides, machine learning ap...
Structure-Activity Relationship Amino Acid Sequence Pattern Recognition, Automated Molecular Sequence Data Sequence Analysis, Protein Peptides Machine Learning Bacterial Physiological Phenomena Drug Discovery Protein Interaction Mapping Antimicrobial Cationic Peptides
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Systematic artifacts in support vector regression-based compound potency prediction revealed by statistical and activity landscape analysis.

PloS one Mar 5, 2015
Support vector machines are a popular machine learning method for many classification tasks in biology and chemistry. In addition, the support vector regression (SVR) variant is widely used for numerical property predictions. In chemoinformatics and ...
Structure-Activity Relationship Humans Drug Evaluation, Preclinical Drug Discovery Artifacts Support Vector Machine
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Types and effects of protein variations.

Human genetics Jan 24, 2015
Variations in proteins have very large number of diverse effects affecting sequence, structure, stability, interactions, activity, abundance and other properties. Although protein-coding exons cover just over 1 % of the human genome they harbor an di...
Genetic Variation Proteins Humans Gene Ontology Models, Molecular Protein Structure, Secondary Animals Protein Folding Databases, Protein Protein Processing, Post-Translational Molecular Dynamics Simulation Structure-Activity Relationship Protein Structure, Quaternary Amino Acid Sequence
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Three- and four-class classification models for P-glycoprotein inhibitors using counter-propagation neural networks.

SAR and QSAR in environmental research Jan 14, 2015
P-glycoprotein (P-gp) is an ATP binding cassette (ABC) transporter that helps to protect several certain human organs from xenobiotic exposure. This efflux pump is also responsible for multi-drug resistance (MDR), an issue of the chemotherapy approac...
Computer Simulation Structure-Activity Relationship Drug Resistance, Multiple Databases, Chemical Neural Networks, Computer Cytochrome P-450 CYP3A Inhibitors ATP Binding Cassette Transporter, Subfamily B
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Active-learning strategies in computer-assisted drug discovery.

Drug discovery today Dec 9, 2014
High-throughput compound screening is time and resource consuming, and considerable effort is invested into screening compound libraries, profiling, and selecting the most promising candidates for further testing. Active-learning methods assist the s...
Structure-Activity Relationship Molecular Structure Pharmaceutical Preparations Computer-Aided Design Drug Discovery Humans Molecular Targeted Therapy Supervised Machine Learning Computational Biology Databases, Pharmaceutical Databases, Chemical Algorithms Animals
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A deep learning model for structure-based bioactivity optimization and its application in the bioactivity optimization of a SARS-CoV-2 main protease inhibitor.

European journal of medicinal chemistry Jul 5, 2025
Bioactivity optimization is a crucial and technical task in the early stages of drug discovery, traditionally carried out through iterative substituent optimization, a process that is often both time-consuming and expensive. To address this challenge...
Humans COVID-19 COVID-19 Drug Treatment Protease Inhibitors Coronavirus 3C Proteases SARS-CoV-2 Antiviral Agents Drug Discovery Structure-Activity Relationship Deep Learning Molecular Structure
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Discovery of naturally inspired antimicrobial peptides using deep learning.

Bioorganic chemistry Jun 15, 2025
Non-ribosomal peptides (NRPs) are promising lead compounds for novel antibiotics. Bioinformatic mining of silent microbial NRPS gene clusters provide crucial insights for the discovery and de novo design of bioactive peptides. Here, we describe the e...
Molecular Structure Multigene Family Antimicrobial Peptides Microbial Sensitivity Tests Dose-Response Relationship, Drug Structure-Activity Relationship Drug Discovery Humans Deep Learning Anti-Bacterial Agents
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Machine Learning Accelerated Discovery of Antimicrobial Inorganic Nanomaterials.

The journal of physical chemistry letters Jun 12, 2025
The growing prevalence of infectious diseases and the increasing threat of bacterial resistance have drawn widespread attention to antimicrobial inorganic nanomaterials. However, the diversity, abundance, and complex mechanisms of these materials pre...
Bacteria Structure-Activity Relationship Nanostructures Inorganic Chemicals Machine Learning Anti-Bacterial Agents Microbial Sensitivity Tests
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