AIMC Topic: Molecular Structure

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Quantitative structure-activity relationship analysis using deep learning based on a novel molecular image input technique.

Bioorganic & medicinal chemistry letters Aug 27, 2018
Quantitative structure-activity relationship (QSAR) analysis uses structural, quantum chemical, and physicochemical features calculated from molecular geometry as explanatory variables predicting physiological activity. Recently, deep learning based ...
Molecular Structure Models, Chemical Molecular Conformation Quantitative Structure-Activity Relationship Deep Learning ROC Curve
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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 ...
Machine Learning Software Structure-Activity Relationship Models, Molecular Molecular Structure Algorithms Stereoisomerism Computer Simulation Organic Chemicals
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Machine Learning in Drug Discovery.

Journal of chemical information and modeling Aug 15, 2018
Molecular Structure Computer Simulation Neural Networks, Computer Drug Discovery Machine Learning Structure-Activity Relationship Humans
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Recognition Tunneling of Canonical and Modified RNA Nucleotides for Their Identification with the Aid of Machine Learning.

ACS nano Jun 28, 2018
In the present study, we demonstrate a tunneling nanogap technique to identify individual RNA nucleotides, which can be used as a mechanism to read the nucleobases for direct sequencing of RNA in a solid-state nanopore. The tunneling nanogap is compo...
Surface Properties Nanopores Microscopy, Scanning Tunneling Machine Learning Particle Size Nucleotides RNA Molecular Structure Nanotechnology
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Perturbation-Theory and Machine Learning (PTML) Model for High-Throughput Screening of Parham Reactions: Experimental and Theoretical Studies.

Journal of chemical information and modeling Jun 27, 2018
Machine learning (ML) algorithms are gaining importance in the processing of chemical information and modeling of chemical reactivity problems. In this work, we have developed a perturbation-theory and machine learning (PTML) model combining perturba...
Neural Networks, Computer Machine Learning Algorithms Models, Chemical Isoquinolines High-Throughput Screening Assays Linear Models Lithium Structure-Activity Relationship Molecular Structure Cyclization Databases, Chemical Organometallic Compounds Thermodynamics
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Predicting lysine-malonylation sites of proteins using sequence and predicted structural features.

Journal of computational chemistry May 14, 2018
Malonylation is a recently discovered post-translational modification (PTM) in which a malonyl group attaches to a lysine (K) amino acid residue of a protein. In this work, a novel machine learning model, SPRINT-Mal, is developed to predict malonylat...
Humans Machine Learning Malonates Molecular Structure Hominidae Animals Saccharopolyspora Bacterial Proteins Mice Lysine Protein Processing, Post-Translational
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Machine learning in chemoinformatics and drug discovery.

Drug discovery today May 8, 2018
Chemoinformatics is an established discipline focusing on extracting, processing and extrapolating meaningful data from chemical structures. With the rapid explosion of chemical 'big' data from HTS and combinatorial synthesis, machine learning has be...
Informatics High-Throughput Screening Assays Pattern Recognition, Automated Molecular Structure Quantitative Structure-Activity Relationship Animals Pharmaceutical Preparations Drug Discovery Diffusion of Innovation Humans Machine Learning
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Can we accelerate medicinal chemistry by augmenting the chemist with Big Data and artificial intelligence?

Drug discovery today Mar 22, 2018
AI comes to lead optimization: medicinal chemistry in all disease areas can be accelerated by exploiting our pre-competitive knowledge in an unbiased way.
Pharmaceutical Preparations Data Mining High-Throughput Screening Assays Databases, Chemical Chemistry, Pharmaceutical Drug Discovery Structure-Activity Relationship Molecular Structure Humans Artificial Intelligence Animals
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Machine learning prioritizes synthesis of primaquine ureidoamides with high antimalarial activity and attenuated cytotoxicity.

European journal of medicinal chemistry Jan 31, 2018
Primaquine (PQ) is a commonly used drug that can prevent the transmission of Plasmodium falciparum malaria, however toxicity limits its use. We prepared five groups of PQ derivatives: amides 1a-k, ureas 2a-k, semicarbazides 3a,b, acylsemicarbazides 4...
Primaquine Antimalarials Malaria, Falciparum Dose-Response Relationship, Drug Erythrocytes Cell Line Antineoplastic Agents Amides Rats Drug Screening Assays, Antitumor Humans Cell Proliferation Plasmodium falciparum Structure-Activity Relationship Machine Learning Molecular Structure Animals
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Mol2vec: Unsupervised Machine Learning Approach with Chemical Intuition.

Journal of chemical information and modeling Jan 10, 2018
Inspired by natural language processing techniques, we here introduce Mol2vec, which is an unsupervised machine learning approach to learn vector representations of molecular substructures. Like the Word2vec models, where vectors of closely related w...
Algorithms Molecular Structure Reproducibility of Results Proteins Models, Chemical Datasets as Topic Unsupervised Machine Learning Protein Conformation Natural Language Processing
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