AIMC Topic: Thermodynamics

Clear Filters Showing 191 to 200 of 333 articles

Machine learning transition temperatures from 2D structure.

Journal of molecular graphics & modelling Jan 29, 2021
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...
Software Quantitative Structure-Activity Relationship Thermodynamics Transition Temperature Machine Learning
View on PubMed DOI

Classification and prediction of protein-protein interaction interface using machine learning algorithm.

Scientific reports Jan 19, 2021
Structural insight of the protein-protein interaction (PPI) interface can provide knowledge about the kinetics, thermodynamics and molecular functions of the complex while elucidating its role in diseases and further enabling it as a potential therap...
Support Vector Machine Proteins Protein Interaction Maps Protein Binding Molecular Docking Simulation Binding Sites Thermodynamics Protein Interaction Mapping Databases, Protein Models, Molecular
View on PubMed DOI

Viscosity of Ionic Liquids: Application of the Eyring's Theory and a Committee Machine Intelligent System.

Molecules (Basel, Switzerland) Dec 31, 2020
Accurate determination of the physicochemical characteristics of ionic liquids (ILs), especially viscosity, at widespread operating conditions is of a vital role for various fields. In this study, the viscosity of pure ILs is modeled using three appr...
Bayes Theorem Ionic Liquids Temperature Support Vector Machine Thermodynamics Solvents Viscosity Artificial Intelligence Algorithms
View on PubMed DOI

Recent advances in constraint and machine learning-based metabolic modeling by leveraging stoichiometric balances, thermodynamic feasibility and kinetic law formalisms.

Metabolic engineering Dec 10, 2020
Understanding the governing principles behind organisms' metabolism and growth underpins their effective deployment as bioproduction chassis. A central objective of metabolic modeling is predicting how metabolism and growth are affected by both exter...
Machine Learning Kinetics Thermodynamics Feasibility Studies Models, Biological
View on PubMed DOI

Predicting changes in protein thermodynamic stability upon point mutation with deep 3D convolutional neural networks.

PLoS computational biology Nov 30, 2020
Predicting mutation-induced changes in protein thermodynamic stability (ΔΔG) is of great interest in protein engineering, variant interpretation, and protein biophysics. We introduce ThermoNet, a deep, 3D-convolutional neural network (3D-CNN) designe...
Proteins Protein Stability Point Mutation Thermodynamics Imaging, Three-Dimensional Computational Biology Neural Networks, Computer
View on PubMed DOI

Efficient Machine-Learning-Aided Screening of Hydrogen Adsorption on Bimetallic Nanoclusters.

ACS combinatorial science Nov 4, 2020
Nanoclusters add an additional dimension in which to look for promising catalyst candidates, since catalytic activity of materials often changes at the nanoscale. However, the large search space of relevant atomic sites exacerbates the challenge for ...
Machine Learning Density Functional Theory Copper Nanoparticles Adsorption Cobalt Hydrogen Thermodynamics
View on PubMed DOI

A deep learning approach to programmable RNA switches.

Nature communications Oct 7, 2020
Engineered RNA elements are programmable tools capable of detecting small molecules, proteins, and nucleic acids. Predicting the behavior of these synthetic biology components remains a challenge, a situation that could be addressed through enhanced ...
Datasets as Topic Synthetic Biology Thermodynamics Genetic Engineering Genome, Viral Transcription Factors Kinetics Humans Deep Learning Riboswitch
View on PubMed DOI

Characterizing chromatin folding coordinate and landscape with deep learning.

PLoS computational biology Sep 28, 2020
Genome organization is critical for setting up the spatial environment of gene transcription, and substantial progress has been made towards its high-resolution characterization. The underlying molecular mechanism for its establishment is much less u...
Models, Biological Thermodynamics Computer Simulation Protein Conformation Genome Histone Code Deep Learning Chromatin
View on PubMed DOI

Predicting Small Molecule Transfer Free Energies by Combining Molecular Dynamics Simulations and Deep Learning.

Journal of chemical information and modeling Sep 1, 2020
Accurately predicting small molecule partitioning and hydrophobicity is critical in the drug discovery process. There are many heterogeneous chemical environments within a cell and entire human body. For example, drugs must be able to cross the hydro...
Humans Entropy Hydrophobic and Hydrophilic Interactions Thermodynamics Deep Learning Molecular Dynamics Simulation
View on PubMed DOI

Combining Molecular Dynamics and Machine Learning to Predict Self-Solvation Free Energies and Limiting Activity Coefficients.

Journal of chemical information and modeling Sep 1, 2020
Computational prediction of limiting activity coefficients is of great relevance for process design. For highly nonideal mixtures including molecules with directed interactions, methods that maintain the molecular character of the solvent are most pr...
Molecular Dynamics Simulation Solvents Thermodynamics Machine Learning
View on PubMed DOI
Popular Topics
Recent Journals