AIMC Topic: Organic Chemicals

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Retip: Retention Time Prediction for Compound Annotation in Untargeted Metabolomics.

Analytical chemistry May 21, 2020
Unidentified peaks remain a major problem in untargeted metabolomics by LC-MS/MS. Confidence in peak annotations increases by combining MS/MS matching and retention time. We here show how retention times can be predicted from molecular structures. Tw...
Tandem Mass Spectrometry Machine Learning Time Factors Metabolomics Chromatography, Liquid Organic Chemicals Humans
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Learning Molecular Representations for Medicinal Chemistry.

Journal of medicinal chemistry May 15, 2020
The accurate modeling and prediction of small molecule properties and bioactivities depend on the critical choice of molecular representation. Decades of informatics-driven research have relied on expert-designed molecular descriptors to establish qu...
Organic Chemicals Models, Molecular Molecular Structure Deep Learning Cheminformatics Quantitative Structure-Activity Relationship Chemistry, Pharmaceutical
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Improvement in ADMET Prediction with Multitask Deep Featurization.

Journal of medicinal chemistry May 12, 2020
The absorption, distribution, metabolism, elimination, and toxicity (ADMET) properties of drug candidates are important for their efficacy and safety as therapeutics. Predicting ADMET properties has therefore been of great interest to the computation...
Animals Deep Learning Humans Datasets as Topic Organic Chemicals Supervised Machine Learning Chemistry, Pharmaceutical Computational Chemistry
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Automated De Novo Design in Medicinal Chemistry: Which Types of Chemistry Does a Generative Neural Network Learn?

Journal of medicinal chemistry Mar 20, 2020
Artificial intelligence offers promising solutions for property prediction, compound design, and retrosynthetic planning, which are expected to significantly accelerate the search for pharmacologically relevant molecules. Here, we investigate aspects...
Databases, Chemical Factor Xa Inhibitors Organic Chemicals Datasets as Topic Chemistry, Pharmaceutical Neural Networks, Computer Drug Design
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Convolutional Neural Networks for the Design and Analysis of Non-Fullerene Acceptors.

Journal of chemical information and modeling Nov 21, 2019
Convolutional neural network (CNN) is employed to construct generative and prediction models for the design and analysis of non-fullerene acceptors (NFAs) in organic solar cells. It is demonstrated that the dilated causal CNN can be trained as a good...
Solar Energy Thermodynamics Electric Power Supplies Drug Design Organic Chemicals Neural Networks, Computer
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Interpretation of Compound Activity Predictions from Complex Machine Learning Models Using Local Approximations and Shapley Values.

Journal of medicinal chemistry Sep 26, 2019
In qualitative or quantitative studies of structure-activity relationships (SARs), machine learning (ML) models are trained to recognize structural patterns that differentiate between active and inactive compounds. Understanding model decisions is ch...
Support Vector Machine Deep Learning Organic Chemicals
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Practical High-Quality Electrostatic Potential Surfaces for Drug Discovery Using a Graph-Convolutional Deep Neural Network.

Journal of medicinal chemistry Sep 25, 2019
Inspecting protein and ligand electrostatic potential (ESP) surfaces in order to optimize electrostatic complementarity is a key activity in drug design. These ESP surfaces need to reflect the true electrostatic nature of the molecules, which typical...
Drug Discovery Protein Binding Organic Chemicals Deep Learning Hydrogen Bonding Datasets as Topic X-Linked Inhibitor of Apoptosis Protein Static Electricity Factor Xa Quantum Theory Ligands
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Pushing the Boundaries of Molecular Representation for Drug Discovery with the Graph Attention Mechanism.

Journal of medicinal chemistry Aug 27, 2019
Hunting for chemicals with favorable pharmacological, toxicological, and pharmacokinetic properties remains a formidable challenge for drug discovery. Deep learning provides us with powerful tools to build predictive models that are appropriate for t...
Solubility Organic Chemicals Drug Discovery Proof of Concept Study Databases, Chemical Models, Molecular Deep Learning Datasets as Topic
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Energy-Geometry Dependency of Molecular Structures: A Multistep Machine Learning Approach.

ACS combinatorial science Aug 21, 2019
There is growing interest in estimating quantum observables while circumventing expensive computational overhead for facile in silico materials screening. Machine learning (ML) methods are implemented to perform such calculations in shorter times. He...
Inorganic Chemicals Machine Learning Thermodynamics Molecular Structure Computer Simulation Ions Small Molecule Libraries Models, Chemical Organic Chemicals Databases, Chemical Quantum Theory
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Embedded Atom Neural Network Potentials: Efficient and Accurate Machine Learning with a Physically Inspired Representation.

The journal of physical chemistry letters Aug 14, 2019
We propose a simple, but efficient and accurate, machine learning (ML) model for developing a high-dimensional potential energy surface. This so-called embedded atom neural network (EANN) approach is inspired by the well-known empirical embedded atom...
Thermodynamics Machine Learning Organic Chemicals Neural Networks, Computer
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