AIMC Topic: Models, Chemical
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Automatic construction of molecular similarity networks for visual graph mining in chemical space of bioactive peptides: an unsupervised learning approach.
Scientific reports
Oct 22, 2020
The increasing interest in bioactive peptides with therapeutic potentials has been reflected in a large variety of biological databases published over the last years. However, the knowledge discovery process from these heterogeneous data sources is a...
Accelerated Reactivity Mechanism and Interpretable Machine Learning Model of -Sulfonylimines toward Fast Multicomponent Reactions.
Organic letters
Oct 19, 2020
We introduce chemical reactivity flowcharts to help chemists interpret reaction outcomes using statistically robust machine learning models trained on a small number of reactions. We developed fast sulfonylimine multicomponent reactions for understan...
Cautionary Guidelines for Machine Learning Studies with Combinatorial Datasets.
ACS combinatorial science
Oct 1, 2020
Regression modeling is becoming increasingly prevalent in organic chemistry as a tool for reaction outcome prediction and mechanistic interrogation. Frequently, to acquire the requisite amount of data for such studies, researchers employ combinatoria...
The Fuzziness in Molecular, Supramolecular, and Systems Chemistry.
Molecules (Basel, Switzerland)
Aug 10, 2020
The global challenges of the XXI century require a more in-depth analysis and investigation of complex systems [...].
Machine Estimation of Drug Melting Properties and Influence on Solubility Prediction.
Molecular pharmaceutics
Jun 19, 2020
There has been much recent interest in machine learning (ML) and molecular quantitative structure property relationships (QSPR). The present research evaluated modern ML-based methods implemented in commercial software (COSMOquick and Molecular Model...
The use of artificial neural network (ANN) for modeling adsorption of sunset yellow onto neodymium modified ordered mesoporous carbon.
Chemosphere
May 15, 2020
Discharging coloring products in water bodies has degraded water quality irreversibly over the past several decades. Order mesoporous carbon (OMC) was modified by embedding neodymium(III) chloride on the surface of OMC to enhance the adsorptive remov...
Dual graph convolutional neural network for predicting chemical networks.
BMC bioinformatics
Apr 23, 2020
BACKGROUND: Predicting of chemical compounds is one of the fundamental tasks in bioinformatics and chemoinformatics, because it contributes to various applications in metabolic engineering and drug discovery. The recent rapid growth of the amount of ...
Current and Future Roles of Artificial Intelligence in Medicinal Chemistry Synthesis.
Journal of medicinal chemistry
Apr 14, 2020
Artificial intelligence and machine learning have demonstrated their potential role in predictive chemistry and synthetic planning of small molecules; there are at least a few reports of companies employing synthetic planning into their overall appr...
How Sure Can We Be about ML Methods-Based Evaluation of Compound Activity: Incorporation of Information about Prediction Uncertainty Using Deep Learning Techniques.
Molecules (Basel, Switzerland)
Mar 23, 2020
A great variety of computational approaches support drug design processes, helping in selection of new potentially active compounds, and optimization of their physicochemical and ADMET properties. Machine learning is a group of methods that are able ...
A Deep Learning-Based Chemical System for QSAR Prediction.
IEEE journal of biomedical and health informatics
Feb 28, 2020
Research on quantitative structure-activity relationships (QSAR) provides an effective approach to determine new hits and promising lead compounds during drug discovery. In the past decades, various works have gained good performance for QSAR with th...
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