Structure-Activity Relationship

Applications of artificial intelligence to drug design and discovery in the big data era: a comprehensive review.

Molecular diversity Jun 10, 2021

Artificial intelligence (AI) renders cutting-edge applications in diverse sectors of society. Due to substantial progress in high-performance computing, the development of superior algorithms, and the accumulation of huge biological and chemical data...

Drug Design Drug Discovery Big Data Protein Binding Humans Artificial Intelligence Algorithms Workflow Data Mining Protein Interaction Mapping Protein Folding Databases, Pharmaceutical Models, Theoretical Proteins Structure-Activity Relationship Models, Molecular

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Structure-based protein function prediction using graph convolutional networks.

Nature communications May 26, 2021

The rapid increase in the number of proteins in sequence databases and the diversity of their functions challenge computational approaches for automated function prediction. Here, we introduce DeepFRI, a Graph Convolutional Network for predicting pro...

Protein Structure, Tertiary Computational Biology Deep Learning Proteins Databases, Protein Structure-Activity Relationship Models, Molecular Models, Biological Amino Acid Sequence Datasets as Topic

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A convolutional neural network for the prediction and forward design of ribozyme-based gene-control elements.

eLife Apr 16, 2021

Ribozyme switches are a class of RNA-encoded genetic switch that support conditional regulation of gene expression across diverse organisms. An improved elucidation of the relationships between sequence, structure, and activity can improve our capaci...

Nucleic Acid Conformation Saccharomyces cerevisiae RNA, Catalytic Ligands High-Throughput Nucleotide Sequencing Gene Expression Regulation Base Sequence Neural Networks, Computer Machine Learning Computer-Aided Design Aptamers, Nucleotide Sequence Analysis, RNA Models, Genetic Escherichia coli 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...

Humans Protein Binding Structure-Activity Relationship Molecular Structure Molecular Dynamics Simulation Machine Learning Molecular Docking Simulation Drug Discovery Protein Interaction Maps Small Molecule Libraries Protein Interaction Mapping Kelch-Like ECH-Associated Protein 1 Ligands Molecular Conformation NF-E2-Related Factor 2 Binding Sites

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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...

Bayes Theorem Computational Biology Humans HIV Integrase HIV-1 Decision Trees Machine Learning HIV Integrase Inhibitors Structure-Activity Relationship Drug Design Databases, Pharmaceutical Molecular Structure HIV Infections

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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...

Databases, Chemical Machine Learning Epigenomics Proof of Concept Study Drug Discovery Transcription Factors Histone Deacetylases Organic Chemicals Structure-Activity Relationship Molecular Structure

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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 Neural Networks, Computer Structure-Activity Relationship

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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 ...

Artificial Intelligence Dose-Response Relationship, Drug Parasitic Sensitivity Tests Structure-Activity Relationship Protozoan Proteins Plasmodium falciparum Protein Kinase Inhibitors Molecular Structure Mitogen-Activated Protein Kinase Kinases Drug Evaluation, Preclinical Antimalarials

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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...

Antimicrobial Cationic Peptides Structure-Activity Relationship Molecular Dynamics Simulation Amino Acid Sequence Pseudomonas aeruginosa Acinetobacter baumannii Female Klebsiella Infections Animals Escherichia coli Drug Design Staphylococcus aureus Drug Discovery Mice, Inbred BALB C Anti-Bacterial Agents Deep Learning Drug Resistance, Bacterial Microbial Sensitivity Tests Mice Klebsiella pneumoniae

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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 NF-E2-Related Factor 2 Mutagenesis, Site-Directed Amino Acid Motifs Kelch-Like ECH-Associated Protein 1 Cyclization Crystallography, X-Ray Peptides Hydrogen Bonding Protein Binding Recombinant Proteins Fluorescence Polarization Structure-Activity Relationship Nuclear Magnetic Resonance, Biomolecular

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AI Medical Compendium Topic

Explore the latest research on artificial intelligence and machine learning in medicine.

Applications of artificial intelligence to drug design and discovery in the big data era: a comprehensive review.

Structure-based protein function prediction using graph convolutional networks.

A convolutional neural network for the prediction and forward design of ribozyme-based gene-control elements.

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

Classification and Design of HIV-1 Integrase Inhibitors Based on Machine Learning.

Epigenetic Target Fishing with Accurate Machine Learning Models.

Prediction of activity cliffs on the basis of images using convolutional neural networks.

Artificial Intelligence Applied to the Rapid Identification of New Antimalarial Candidates with Dual-Stage Activity.

Accelerated antimicrobial discovery via deep generative models and molecular dynamics simulations.

Recapitulating the Binding Affinity of Nrf2 for KEAP1 in a Cyclic Heptapeptide, Guided by NMR, X-ray Crystallography, and Machine Learning.

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