AIMC Topic: Models, Chemical

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Membrane separation of antibiotics predicted with the back propagation neural network.

Journal of environmental science and health. Part A, Toxic/hazardous substances & environmental engineering Apr 18, 2023
Antibiotics and antibiotic resistance genes (ARGs) have been frequently detected in the aquatic environment and are regarded as emerging pollutants. The prediction models for the removal effect of four target antibiotics by membrane separation techno...
Sulfamethoxazole Models, Chemical Tetracycline Neural Networks, Computer Anti-Bacterial Agents
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Machine learning-assisted data filtering and QSAR models for prediction of chemical acute toxicity on rat and mouse.

Journal of hazardous materials Apr 1, 2023
Machine learning (ML) methods provide a new opportunity to build quantitative structure-activity relationship (QSAR) models for predicting chemicals' toxicity based on large toxicity data sets, but they are limited in insufficient model robustness du...
Machine Learning Models, Chemical Quantitative Structure-Activity Relationship Animals Rats Mice
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RetroComposer: Composing Templates for Template-Based Retrosynthesis Prediction.

Biomolecules Sep 19, 2022
The main target of retrosynthesis is to recursively decompose desired molecules into available building blocks. Existing template-based retrosynthesis methods follow a template selection stereotype and suffer from limited training templates, which pr...
Models, Chemical Chemistry Techniques, Synthetic Machine Learning
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Formula Graph Self-Attention Network for Representation-Domain Independent Materials Discovery.

Advanced science (Weinheim, Baden-Wurttemberg, Germany) Apr 27, 2022
The success of machine learning (ML) in materials property prediction depends heavily on how the materials are represented for learning. Two dominant families of material descriptors exist, one that encodes crystal structure in the representation and...
Neural Networks, Computer Models, Chemical Machine Learning Structure-Activity Relationship
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DSResSol: A Sequence-Based Solubility Predictor Created with Dilated Squeeze Excitation Residual Networks.

International journal of molecular sciences Dec 17, 2021
Protein solubility is an important thermodynamic parameter that is critical for the characterization of a protein's function, and a key determinant for the production yield of a protein in both the research setting and within industrial (e.g., pharma...
Proteins Models, Chemical Amino Acid Sequence Solubility Deep Learning Computational Biology
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DeepCME: A deep learning framework for computing solution statistics of the chemical master equation.

PLoS computational biology Dec 8, 2021
Stochastic models of biomolecular reaction networks are commonly employed in systems and synthetic biology to study the effects of stochastic fluctuations emanating from reactions involving species with low copy-numbers. For such models, the Kolmogor...
Computational Biology Models, Chemical Computer Simulation Deep Learning Stochastic Processes Probability Statistics as Topic
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Differentiable biology: using deep learning for biophysics-based and data-driven modeling of molecular mechanisms.

Nature methods Oct 4, 2021
Deep learning using neural networks relies on a class of machine-learnable models constructed using 'differentiable programs'. These programs can combine mathematical equations specific to a particular domain of natural science with general-purpose, ...
Biophysics Computational Chemistry Proteins Models, Chemical Pattern Recognition, Automated Protein Conformation Deep Learning Neural Networks, Computer Computational Biology
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Multitask machine learning models for predicting lipophilicity (logP) in the SAMPL7 challenge.

Journal of computer-aided molecular design Jul 17, 2021
Accurate prediction of lipophilicity-logP-based on molecular structures is a well-established field. Predictions of logP are often used to drive forward drug discovery projects. Driven by the SAMPL7 challenge, in this manuscript we describe the steps...
Models, Statistical Neural Networks, Computer Models, Chemical Solvents Machine Learning Quantum Theory Thermodynamics Drug Discovery Solubility
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Solvent Screening for Solubility Enhancement of Theophylline in Neat, Binary and Ternary NADES Solvents: New Measurements and Ensemble Machine Learning.

International journal of molecular sciences Jul 8, 2021
Theophylline, a typical representative of active pharmaceutical ingredients, was selected to study the characteristics of experimental and theoretical solubility measured at 25 °C in a broad range of solvents, including neat, binary mixtures and tern...
Choline Models, Chemical Machine Learning Theophylline Solvents Solubility Water
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Leveraging high-throughput screening data, deep neural networks, and conditional generative adversarial networks to advance predictive toxicology.

PLoS computational biology Jul 2, 2021
There are currently 85,000 chemicals registered with the Environmental Protection Agency (EPA) under the Toxic Substances Control Act, but only a small fraction have measured toxicological data. To address this gap, high-throughput screening (HTS) an...
Zebrafish High-Throughput Screening Assays Toxicology Toxicity Tests Computational Biology Models, Chemical Neural Networks, Computer Animals Embryo, Nonmammalian
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