AIMC Topic: Transition Temperature

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Fe-based superconducting transition temperature modeling by machine learning: A computer science method.

PloS one
Searching for new high temperature superconductors has long been a key research issue. Fe-based superconductors attract researchers' attention due to their high transition temperature, strong irreversibility field, and excellent crystallographic symm...

Machine learning to determine optimal conditions for controlling the size of elastin-based particles.

Scientific reports
This paper evaluates the aggregation behavior of a potential drug and gene delivery system that combines branched polyethyleneimine (PEI), a positively-charged polyelectrolyte, and elastin-like polypeptide (ELP), a recombinant polymer that exhibits l...

Machine learning transition temperatures from 2D structure.

Journal of molecular graphics & modelling
A priori knowledge of physicochemical properties such as melting and boiling could expedite materials discovery. However, theoretical modeling from first principles poses a challenge for efficient virtual screening of potential candidates. As an alte...

Machine Estimation of Drug Melting Properties and Influence on Solubility Prediction.

Molecular pharmaceutics
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...

Machine-Learning-Based Predictive Modeling of Glass Transition Temperatures: A Case of Polyhydroxyalkanoate Homopolymers and Copolymers.

Journal of chemical information and modeling
Polyhydroxyalkanoate-based polymers-being ecofriendly, biosynthesizable, and economically viable and possessing a broad range of tunable properties-are currently being actively pursued as promising alternatives for petroleum-based plastics. The vast ...

DeepDDG: Predicting the Stability Change of Protein Point Mutations Using Neural Networks.

Journal of chemical information and modeling
Accurately predicting changes in protein stability due to mutations is important for protein engineering and for understanding the functional consequences of missense mutations in proteins. We have developed DeepDDG, a neural network-based method, fo...

Rapid microarray-based assay for detection of pyrazinamide resistant Mycobacterium tuberculosis.

Diagnostic microbiology and infectious disease
Pyrazinamide (PZA) is a key antibiotic for the treatment of drug susceptible tuberculosis. PZA-resistance is mainly mediated by mutations in the pncA gene; however the current gold standard is a phenotypic drug susceptibility test requiring a well-ad...

Convolutional Embedding of Attributed Molecular Graphs for Physical Property Prediction.

Journal of chemical information and modeling
The task of learning an expressive molecular representation is central to developing quantitative structure-activity and property relationships. Traditional approaches rely on group additivity rules, empirical measurements or parameters, or generatio...

In Silico Prediction of Physicochemical Properties of Environmental Chemicals Using Molecular Fingerprints and Machine Learning.

Journal of chemical information and modeling
There are little available toxicity data on the vast majority of chemicals in commerce. High-throughput screening (HTS) studies, such as those being carried out by the U.S. Environmental Protection Agency (EPA) ToxCast program in partnership with the...

Automated Classification and Cluster Visualization of Genotypes Derived from High Resolution Melt Curves.

PloS one
INTRODUCTION: High Resolution Melting (HRM) following PCR has been used to identify DNA genotypes. Fluorescent dyes bounded to double strand DNA lose their fluorescence with increasing temperature, yielding different signatures for different genotype...