AIMC Topic: Models, Chemical

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Differentiable biology: using deep learning for biophysics-based and data-driven modeling of molecular mechanisms.

Nature methods
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, ...

Multitask machine learning models for predicting lipophilicity (logP) in the SAMPL7 challenge.

Journal of computer-aided molecular design
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...

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
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...

Leveraging high-throughput screening data, deep neural networks, and conditional generative adversarial networks to advance predictive toxicology.

PLoS computational biology
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...

Predicting aptamer sequences that interact with target proteins using an aptamer-protein interaction classifier and a Monte Carlo tree search approach.

PloS one
Oligonucleotide-based aptamers, which have a three-dimensional structure with a single-stranded fragment, feature various characteristics with respect to size, toxicity, and permeability. Accordingly, aptamers are advantageous in terms of diagnosis a...

Development of artificial intelligence models for well groundwater quality simulation: Different modeling scenarios.

PloS one
Groundwater is one of the most important freshwater resources, especially in arid and semi-arid regions where the annual amounts of precipitation are small with frequent drought durations. Information on qualitative parameters of these valuable resou...

On the Evaluation of Rhamnolipid Biosurfactant Adsorption Performance on Amberlite XAD-2 Using Machine Learning Techniques.

BioMed research international
Biosurfactants are a series of organic compounds that are composed of two parts, hydrophobic and hydrophilic, and since they have properties such as less toxicity and biodegradation, they are widely used in the food industry. Important applications i...

Machine learning guided aptamer refinement and discovery.

Nature communications
Aptamers are single-stranded nucleic acid ligands that bind to target molecules with high affinity and specificity. They are typically discovered by searching large libraries for sequences with desirable binding properties. These libraries, however, ...

Kinetic modeling and quasi-economic analysis of fermentative glycolipopeptide biosurfactant production in a medium co-optimized by statistical and neural network approaches.

Preparative biochemistry & biotechnology
This study presents the kinetics of production of a glycolipopeptide biosurfactant in a medium previously co-optimized by response surface and neural network methods to gain some insight into its volumetric and specific productivities for possible sc...

Learning Atomic Interactions through Solvation Free Energy Prediction Using Graph Neural Networks.

Journal of chemical information and modeling
Solvation free energy is a fundamental property that influences various chemical and biological processes, such as reaction rates, protein folding, drug binding, and bioavailability of drugs. In this work, we present a deep learning method based on g...