AIMC Topic: Molecular Structure

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Inferring Association between Compound and Pathway with an Improved Ensemble Learning Method.

Molecular informatics Aug 6, 2015
Emergence of compound molecular data coupled to pathway information offers the possibility of using machine learning methods for compound-pathway associations' inference. To provide insights into the global relationship between compounds and their af...
MCF-7 Cells Molecular Structure Oligonucleotide Array Sequence Analysis Drug Discovery Humans Antineoplastic Agents Algorithms Databases, Genetic
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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 Structure Drug Evaluation, Preclinical HIV Protease HIV Protease Inhibitors Support Vector Machine Dose-Response Relationship, Drug Structure-Activity Relationship Molecular Docking Simulation
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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...
Ligands Cyclooxygenase 2 Inhibitors Datasets as Topic Catalytic Domain Arachidonate 5-Lipoxygenase Cyclooxygenase 2 Structure-Activity Relationship Molecular Structure Humans Quantum Theory Neural Networks, Computer Algorithms Molecular Docking Simulation Protein Binding Lipoxygenase Inhibitors Substrate Specificity
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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 Humans Drug Discovery Databases, Pharmaceutical Algorithms Supervised Machine Learning Computational Biology Animals Databases, Chemical Molecular Targeted Therapy
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First report on analysis of chemical space, scaffold diversity, critical structural features of HDAC11 inhibitors.

Molecular diversity Aug 1, 2025
In the histone deacetylase (HDAC) family, HDAC11 is the smallest and a single member under the class IV subtype. It is important as a drug target mainly in cancer, inflammatory and autoimmune diseases. The design and development of selective HDAC11 i...
Molecular Structure Molecular Dynamics Simulation Ligands Humans Molecular Docking Simulation Drug Design Quantitative Structure-Activity Relationship Histone Deacetylase Inhibitors Histone Deacetylases
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In silico discovery of novel compounds for FAK activation using virtual screening, AI-based prediction, and molecular dynamics.

Computational biology and chemistry Aug 1, 2025
Focal Adhesion Kinase (FAK) is a non-receptor tyrosine kinase that plays a crucial role in cell proliferation, migration, and signal transduction. FAK is overexpressed in metastatic and advanced-stage cancers, where it is considered a key kinase in c...
Protein Kinase Inhibitors Focal Adhesion Protein-Tyrosine Kinases Molecular Structure Molecular Dynamics Simulation Drug Evaluation, Preclinical Molecular Docking Simulation Drug Discovery Humans Artificial Intelligence
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Structural Bias in Three-Dimensional Autoregressive Generative Machine Learning of Organic Molecules.

Journal of chemical information and modeling Jul 14, 2025
A range of generative machine learning models for the design of novel molecules and materials have been proposed in recent years. Models that can generate three-dimensional structures are particularly suitable for quantum chemistry workflows, enablin...
Machine Learning Organic Chemicals Molecular Structure
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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...
Molecular Structure COVID-19 Drug Treatment Protease Inhibitors Deep Learning Antiviral Agents Structure-Activity Relationship Coronavirus 3C Proteases Humans COVID-19 SARS-CoV-2 Drug Discovery
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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...
Drug Discovery Deep Learning Microbial Sensitivity Tests Humans Anti-Bacterial Agents Multigene Family Dose-Response Relationship, Drug Antimicrobial Peptides Structure-Activity Relationship Molecular Structure
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Accurate Identification of MDMB-Type Synthetic Cannabinoids through Design of Dual Excited-State Intramolecular Proton Transfer Site Probe and Deep-Learning.

Analytical chemistry Jun 10, 2025
Synthetic cannabinoids, a novel class of highly toxic psychoactive substances with various disguised forms, have posed significant risks to public safety, and their weak reactivity presents a substantial challenge for swift and accurate analysis. In ...
Molecular Structure Spectrometry, Fluorescence Fluorescent Dyes Hydrogen Bonding Cannabinoids Protons Deep Learning
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