AIMC Topic: Molecular Structure

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Efficient molecular encoders for virtual screening.

Drug discovery today. Technologies Oct 4, 2020
Molecular representations encoding molecular structure information play critical roles in molecular virtual screening (VS). In order to improve VS performance, an abundance of molecular encoders have been developed and tested by various VS challenges...
Deep Learning Molecular Structure Humans Drug Discovery
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Transfer learning enables the molecular transformer to predict regio- and stereoselective reactions on carbohydrates.

Nature communications Sep 25, 2020
Organic synthesis methodology enables the synthesis of complex molecules and materials used in all fields of science and technology and represents a vast body of accumulated knowledge optimally suited for deep learning. While most organic reactions i...
Molecular Structure Carbohydrates Oligosaccharides Machine Learning Glycosylation Carbohydrate Metabolism Chemistry Techniques, Synthetic
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A new diarylhexane and two new diarylpropanols from the roots of .

Natural product research Sep 16, 2020
A new diarylhexane, kneglobularone B () and two new diarylpropanols, kneglobularols A - B () along with seven known compounds () were isolated and characterized from the roots of It is the first time to find arylpropyl quinone () and isoflavone () i...
Molecular Structure Plant Extracts Plantaginaceae Myristicaceae Humans MCF-7 Cells Cell Line, Tumor Plant Roots
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Quantum machine learning using atom-in-molecule-based fragments selected on the fly.

Nature chemistry Sep 14, 2020
First-principles-based exploration of chemical space deepens our understanding of chemistry and might help with the design of new molecules, materials or experiments. Due to the computational cost of quantum chemistry methods and the immense number o...
Organic Chemicals Molecular Structure Quantum Theory Machine Learning
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GLORYx: Prediction of the Metabolites Resulting from Phase 1 and Phase 2 Biotransformations of Xenobiotics.

Chemical research in toxicology Aug 26, 2020
Predicting the structures of metabolites formed in humans can provide advantageous insights for the development of drugs and other compounds. Here we present GLORYx, which integrates machine learning-based site of metabolism (SoM) prediction with rea...
Humans Toxicity Tests Xenobiotics Molecular Structure Biotransformation Machine Learning
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Comprehensive Prediction of Molecular Recognition in a Combinatorial Chemical Space Using Machine Learning.

ACS combinatorial science Aug 17, 2020
In combinatorial chemical approaches, optimizing the composition and arrangement of building blocks toward a particular function has been done using a number of methods, including high throughput molecular screening, molecular evolution, and computat...
Machine Learning Molecular Structure Peptide Library High-Throughput Screening Assays Amino Acid Sequence Structure-Activity Relationship Combinatorial Chemistry Techniques Ligands Peptides Models, Molecular Protein Binding
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Discovery of Novel Inhibitors of a Critical Brain Enzyme Using a Homology Model and a Deep Convolutional Neural Network.

Journal of medicinal chemistry Jul 30, 2020
Rare neglected diseases may be neglected but are hardly rare, affecting hundreds of millions of people around the world. Here, we present a hit identification approach using AtomNet, the world's first deep convolutional neural network for structure-b...
Humans Drug Discovery Enzyme Inhibitors Streptomyces Deep Learning Acetyltransferases Molecular Structure
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Can One Hear the Shape of a Molecule (from its Coulomb Matrix Eigenvalues)?

Journal of chemical information and modeling Jul 29, 2020
Coulomb matrix eigenvalues (CMEs) are global 3D representations of molecular structure, which have been previously used to predict atomization energies, prioritize geometry searches, and interpret rotational spectra. The properties of the CME represe...
Isomerism Molecular Conformation Unsupervised Machine Learning Molecular Structure Algorithms
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Exploration of the mechanism of traditional Chinese medicine by AI approach using unsupervised machine learning for cellular functional similarity of compounds in heterogeneous networks, XiaoErFuPi granules as an example.

Pharmacological research Jul 17, 2020
'Polypharmacology' is usually used to describe the network-wide effect of a single compound, but traditional Chinese medicine (TCM) has a polypharmacological effect naturally based on the 'multi-components, multi-targets and multi-pathways' principle...
Gene Regulatory Networks Systems Biology Proteomics Proteome Duodenum Humans Gene Expression Profiling Transcriptome Molecular Structure Animals Protein Interaction Maps Dyspepsia Drugs, Chinese Herbal Rats, Sprague-Dawley Male Structure-Activity Relationship Signal Transduction Polypharmacology Cluster Analysis Medicine, Chinese Traditional Unsupervised Machine Learning
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Machine Learning Platform to Discover Novel Growth Inhibitors of Neisseria gonorrhoeae.

Pharmaceutical research Jul 13, 2020
PURPOSE: To advance fundamental biological and translational research with the bacterium Neisseria gonorrhoeae through the prediction of novel small molecule growth inhibitors via naïve Bayesian modeling methodology.
Gonorrhea Databases, Chemical Bayes Theorem Neisseria gonorrhoeae Anti-Bacterial Agents Drug Discovery Structure-Activity Relationship Molecular Structure Microbial Sensitivity Tests Machine Learning
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