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Thermodynamics

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Transferable Ring Corrections for Predicting Enthalpy of Formation of Cyclic Compounds.

Journal of chemical information and modeling
Computational predictions of the thermodynamic properties of molecules and materials play a central role in contemporary reaction prediction and kinetic modeling. Due to the lack of experimental data and computational cost of high-level quantum chemi...

Stereoelectronic effects in stabilizing protein-N-glycan interactions revealed by experiment and machine learning.

Nature chemistry
The energetics of protein-carbohydrate interactions, central to many life processes, cannot yet be manipulated predictably. This is mostly due to an incomplete quantitative understanding of the enthalpic and entropic basis of these interactions in aq...

Artificial neural network modelling for biodecolorization of Basic Violet 03 from aqueous solution by biochar derived from agro-bio waste of groundnut hull: Kinetics and thermodynamics.

Chemosphere
In this study, Levenberg Marquardt back propagation algorithm was used to train the Artificial Neural Network (ANN) and to predict the adsorptive removal of cationic dye Basic Violet 03 (BV03) by biochar derived from biowaste of groundnut hull. The e...

Transferable Multilevel Attention Neural Network for Accurate Prediction of Quantum Chemistry Properties via Multitask Learning.

Journal of chemical information and modeling
The development of efficient models for predicting specific properties through machine learning is of great importance for the innovation of chemistry and material science. However, predicting global electronic structure properties like Frontier mole...

RNA secondary structure prediction using deep learning with thermodynamic integration.

Nature communications
Accurate predictions of RNA secondary structures can help uncover the roles of functional non-coding RNAs. Although machine learning-based models have achieved high performance in terms of prediction accuracy, overfitting is a common risk for such hi...

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...

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...

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

Scientific reports
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...

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

Molecules (Basel, Switzerland)
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...