AIMC Topic: Structure-Activity Relationship

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SAR study on inhibitors of GIIA secreted phospholipase A using machine learning methods.

Chemical biology & drug design Feb 4, 2019
GIIA secreted phospholipase A (GIIA sPLA ) is a potent target for drug discovery. To distinguish the activity level of the inhibitors of GIIA sPLA , we built 24 classification models by three machine learning algorithms including support vector machi...
Humans Principal Component Analysis Enzyme Inhibitors Group II Phospholipases A2 Machine Learning Cluster Analysis Structure-Activity Relationship
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In silico prediction of chemical reproductive toxicity using machine learning.

Journal of applied toxicology : JAT Jan 27, 2019
Reproductive toxicity is an important regulatory endpoint in health hazard assessment. Because the in vivo tests are expensive, time consuming and require a large number of animals, which must be killed, in silico approaches as the alternative strate...
Machine Learning Computer Simulation Datasets as Topic Animals Structure-Activity Relationship Reproduction
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Effective binding to protein antigens by antibodies from antibody libraries designed with enhanced protein recognition propensities.

mAbs Jan 9, 2019
Antibodies provide immune protection by recognizing antigens of diverse chemical properties, but elucidating the amino acid sequence-function relationships underlying the specificity and affinity of antibody-antigen interactions remains challenging. ...
Protein Engineering Antibody Affinity Structure-Activity Relationship Humans Antibody Specificity Single-Chain Antibodies Machine Learning Peptide Library
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Machine Learning Models for the Prediction of Chemotherapy-Induced Peripheral Neuropathy.

Pharmaceutical research Jan 7, 2019
PURPOSE: Chemotherapy-induced peripheral neuropathy (CIPN) is a common adverse side effect of cancer chemotherapy that can be life debilitating and cause extreme pain. The multifactorial and poorly understood mechanisms of toxicity have impeded the i...
Humans Neural Networks, Computer Structure-Activity Relationship Molecular Structure Peripheral Nervous System Diseases Drug Design Machine Learning Antineoplastic Agents Neoplasms Incidence
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Data-driven supervised learning of a viral protease specificity landscape from deep sequencing and molecular simulations.

Proceedings of the National Academy of Sciences of the United States of America Dec 26, 2018
Biophysical interactions between proteins and peptides are key determinants of molecular recognition specificity landscapes. However, an understanding of how molecular structure and residue-level energetics at protein-peptide interfaces shape these l...
Antiviral Agents Metabolomics Structure-Activity Relationship Models, Molecular Viral Nonstructural Proteins Energy Metabolism Substrate Specificity Peptide Hydrolases Drug Resistance, Viral Supervised Machine Learning Serine Proteases Hepacivirus High-Throughput Nucleotide Sequencing
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Hispaglabridin B, a constituent of liquorice identified by a bioinformatics and machine learning approach, relieves protein-energy wasting by inhibiting forkhead box O1.

British journal of pharmacology Dec 4, 2018
BACKGROUND AND PURPOSE: Liquorice is the root of Glycyrrhiza glabra, which is a popular food in Europe and China that has previously shown benefits for skeletal fatigue and nutrient metabolism. However, the mechanism and active ingredients remain lar...
Forkhead Box Protein O1 Glycyrrhiza Transcription, Genetic Muscular Atrophy Muscle, Skeletal Structure-Activity Relationship Plant Roots Models, Molecular Molecular Structure Benzopyrans Plant Extracts Mice, Inbred C57BL Dose-Response Relationship, Drug Mice Computational Biology Machine Learning Animals Male
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G-Networks to Predict the Outcome of Sensing of Toxicity.

Sensors (Basel, Switzerland) Oct 16, 2018
G-Networks and their simplified version known as the Random Neural Network have often been used to classify data. In this paper, we present a use of the Random Neural Network to the early detection of potential of toxicity chemical compounds through ...
Structure-Activity Relationship Toxicity Tests Neural Networks, Computer Machine Learning
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Algorithmic Analysis of Cahn-Ingold-Prelog Rules of Stereochemistry: Proposals for Revised Rules and a Guide for Machine Implementation.

Journal of chemical information and modeling Aug 17, 2018
The most recent version of the Cahn-Ingold-Prelog rules for the determination of stereodescriptors as described in Nomenclature of Organic Chemistry: IUPAC Recommendations and Preferred Names 2013 (the "Blue Book"; Favre and Powell. Royal Society of ...
Stereoisomerism Models, Molecular Molecular Structure Machine Learning Computer Simulation Software Organic Chemicals Algorithms Structure-Activity Relationship
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Machine Learning in Drug Discovery.

Journal of chemical information and modeling Aug 15, 2018
Humans Neural Networks, Computer Machine Learning Computer Simulation Drug Discovery Structure-Activity Relationship Molecular Structure
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Artificial intelligence in drug design.

Science China. Life sciences Jul 18, 2018
Thanks to the fast improvement of the computing power and the rapid development of the computational chemistry and biology, the computer-aided drug design techniques have been successfully applied in almost every stage of the drug discovery and devel...
Drug Discovery Pharmaceutical Preparations Humans Computational Biology Computer Simulation Artificial Intelligence Structure-Activity Relationship Pharmacokinetics Computer-Aided Design Drug Design
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