AIMC Topic: Structure-Activity Relationship

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Identification of novel inhibitors of Keap1/Nrf2 by a promising method combining protein-protein interaction-oriented library and machine learning.

Scientific reports Apr 1, 2021
Protein-protein interactions (PPIs) are prospective but challenging targets for drug discovery, because screening using traditional small-molecule libraries often fails to identify hits. Recently, we developed a PPI-oriented library comprising 12,593...
Protein Interaction Maps Protein Binding Structure-Activity Relationship Molecular Structure Humans Binding Sites Machine Learning Small Molecule Libraries Molecular Docking Simulation Protein Interaction Mapping Molecular Dynamics Simulation Drug Discovery Kelch-Like ECH-Associated Protein 1 Ligands Molecular Conformation NF-E2-Related Factor 2
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Classification and Design of HIV-1 Integrase Inhibitors Based on Machine Learning.

Computational and mathematical methods in medicine Apr 1, 2021
A key enzyme in human immunodeficiency virus type 1 (HIV-1) life cycle, integrase (IN) aids the integration of viral DNA into the host DNA, which has become an ideal target for the development of anti-HIV drugs. A total of 1785 potential HIV-1 IN inh...
HIV Infections Computational Biology Databases, Pharmaceutical Decision Trees Humans Molecular Structure Structure-Activity Relationship HIV Integrase Bayes Theorem Machine Learning HIV-1 Drug Design HIV Integrase Inhibitors
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Epigenetic Target Fishing with Accurate Machine Learning Models.

Journal of medicinal chemistry Mar 26, 2021
Epigenetic targets are of significant importance in drug discovery research, as demonstrated by the eight approved epigenetic drugs for treatment of cancer and the increasing availability of chemogenomic data related to epigenetics. This data represe...
Epigenomics Transcription Factors Structure-Activity Relationship Machine Learning Molecular Structure Drug Discovery Databases, Chemical Organic Chemicals Proof of Concept Study Histone Deacetylases
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Prediction of activity cliffs on the basis of images using convolutional neural networks.

Journal of computer-aided molecular design Mar 19, 2021
An activity cliff (AC) is formed by a pair of structurally similar compounds with a large difference in potency. Accordingly, ACs reveal structure-activity relationship (SAR) discontinuity and provide SAR information for compound optimization. Herein...
Drug Design Structure-Activity Relationship Neural Networks, Computer
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Artificial Intelligence Applied to the Rapid Identification of New Antimalarial Candidates with Dual-Stage Activity.

ChemMedChem Mar 16, 2021
Increasing reports of multidrug-resistant malaria parasites urge the discovery of new effective drugs with different chemical scaffolds. Protein kinases play a key role in many cellular processes such as signal transduction and cell division, making ...
Plasmodium falciparum Molecular Structure Parasitic Sensitivity Tests Drug Evaluation, Preclinical Artificial Intelligence Protozoan Proteins Antimalarials Protein Kinase Inhibitors Dose-Response Relationship, Drug Mitogen-Activated Protein Kinase Kinases Structure-Activity Relationship
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Accelerated antimicrobial discovery via deep generative models and molecular dynamics simulations.

Nature biomedical engineering Mar 11, 2021
The de novo design of antimicrobial therapeutics involves the exploration of a vast chemical repertoire to find compounds with broad-spectrum potency and low toxicity. Here, we report an efficient computational method for the generation of antimicrob...
Pseudomonas aeruginosa Acinetobacter baumannii Antimicrobial Cationic Peptides Structure-Activity Relationship Molecular Dynamics Simulation Amino Acid Sequence Deep Learning Microbial Sensitivity Tests Mice Klebsiella pneumoniae Klebsiella Infections Escherichia coli Staphylococcus aureus Mice, Inbred BALB C Female Animals Drug Design Drug Discovery Anti-Bacterial Agents Drug Resistance, Bacterial
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Recapitulating the Binding Affinity of Nrf2 for KEAP1 in a Cyclic Heptapeptide, Guided by NMR, X-ray Crystallography, and Machine Learning.

Journal of the American Chemical Society Mar 8, 2021
Macrocycles, including macrocyclic peptides, have shown promise for targeting challenging protein-protein interactions (PPIs). One PPI of high interest is between Kelch-like ECH-Associated Protein-1 (KEAP1) and Nuclear Factor (Erythroid-derived 2)-li...
Humans Machine Learning Crystallography, X-Ray Hydrogen Bonding Fluorescence Polarization Nuclear Magnetic Resonance, Biomolecular Peptides Protein Binding Recombinant Proteins Structure-Activity Relationship Kelch-Like ECH-Associated Protein 1 NF-E2-Related Factor 2 Mutagenesis, Site-Directed Amino Acid Motifs Cyclization
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DeepCys: Structure-based multiple cysteine function prediction method trained on deep neural network: Case study on domains of unknown functions belonging to COX2 domains.

Proteins Feb 22, 2021
Cysteine (Cys) is the most reactive amino acid participating in a wide range of biological functions. In-silico predictions complement the experiments to meet the need of functional characterization. Multiple Cys function prediction algorithm is scar...
Sulfonic Acids Cysteine Cations, Divalent Software Protein Structure, Secondary Deep Learning Electron Transport Complex IV Nitroso Compounds Amino Acid Sequence Sulfinic Acids Sulfides Protein Processing, Post-Translational Glutathione Structure-Activity Relationship Disulfides Models, Molecular Protein Domains
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SAR and QSAR research on tyrosinase inhibitors using machine learning methods.

SAR and QSAR in environmental research Feb 1, 2021
Tyrosinase is a key rate-limiting enzyme in the process of melanin synthesis, which is closely related to human pigmentation disorders. Tyrosinase inhibitors can down-regulate tyrosinase to effectively reduce melanin synthesis. In this work, we condu...
Neural Networks, Computer Quantitative Structure-Activity Relationship Machine Learning Structure-Activity Relationship Monophenol Monooxygenase Enzyme Inhibitors
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Repurposing potential of FDA-approved and investigational drugs for COVID-19 targeting SARS-CoV-2 spike and main protease and validation by machine learning algorithm.

Chemical biology & drug design Dec 22, 2020
The present study aimed to assess the repurposing potential of existing antiviral drug candidates (FDA-approved and investigational) against SARS-CoV-2 target proteins that facilitates viral entry and replication into the host body. To evaluate molec...
Viral Matrix Proteins Cell Line SARS-CoV-2 COVID-19 Drug Treatment Humans Cell Survival Molecular Docking Simulation Binding Sites Drug Repositioning Machine Learning Antiviral Agents Protease Inhibitors COVID-19 Structure-Activity Relationship Spike Glycoprotein, Coronavirus Deoxyadenosines
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