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Thermodynamics

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Neural Network for Principle of Least Action.

Journal of chemical information and modeling
The principle of least action is the cornerstone of classical mechanics, theory of relativity, quantum mechanics, and thermodynamics. Here, we describe how a neural network (NN) learns to find the trajectory for a Lennard-Jones (LJ) system that maint...

A Physics-Guided Neural Network for Predicting Protein-Ligand Binding Free Energy: From Host-Guest Systems to the PDBbind Database.

Biomolecules
Calculation of protein-ligand binding affinity is a cornerstone of drug discovery. Classic implicit solvent models, which have been widely used to accomplish this task, lack accuracy compared to experimental references. Emerging data-driven models, o...

Factor analysis of error in oxidation potential calculation: A machine learning study.

Journal of computational chemistry
The conductor-like polarizable continuum model (C-PCM), which is a low-cost solvation model, cannot treat characteristic interactions between the solvent and substructure(s) of the solute. Moreover, the error in a charged system is significant. Using...

QMugs, quantum mechanical properties of drug-like molecules.

Scientific data
Machine learning approaches in drug discovery, as well as in other areas of the chemical sciences, benefit from curated datasets of physical molecular properties. However, there currently is a lack of data collections featuring large bioactive molecu...

Machine Learning Applications for Chemical Reactions.

Chemistry, an Asian journal
Machine learning (ML) approaches have enabled rapid and efficient molecular property predictions as well as the design of new novel materials. In addition to great success for molecular problems, ML techniques are applied to various chemical reaction...

Finding or Creating a Living Organism? Past and Future Thought Experiments in Astrobiology Applied to Artificial Intelligence.

Acta biotheoretica
This is a digest of how various researchers in biology and astrobiology have explored questions of what defines living organisms-definitions based on functions or structures observed in organisms, or on systems terms, or on mathematical conceptions l...

Accelerating All-Atom Simulations and Gaining Mechanistic Understanding of Biophysical Systems through State Predictive Information Bottleneck.

Journal of chemical theory and computation
An effective implementation of enhanced sampling algorithms for molecular dynamics simulations requires knowledge of the approximate reaction coordinate describing the relevant mechanisms in the system. In this work, we focus on the recently develop...

Experimental Voltammetry Analyzed Using Artificial Intelligence: Thermodynamics and Kinetics of the Dissociation of Acetic Acid in Aqueous Solution.

Analytical chemistry
Artificial intelligence (AI) is used to quantitatively analyze the voltammetry of the reduction of acetic acid in aqueous solution generating thermodynamic and kinetic data. Specifically, the variation of the steady-state current for the reduction of...

Harnessing Deep Learning for Optimization of Lennard-Jones Parameters for the Polarizable Classical Drude Oscillator Force Field.

Journal of chemical theory and computation
The outcomes of computational chemistry and biology research, including drug design, are significantly influenced by the underlying force field (FF) used in molecular simulations. While improved FF accuracy may be achieved via inclusion of explicit t...

Accurate Sampling of Macromolecular Conformations Using Adaptive Deep Learning and Coarse-Grained Representation.

Journal of chemical information and modeling
Conformational sampling of protein structures is essential for understanding biochemical functions and for predicting thermodynamic properties such as free energies. Where previous approaches rely on sequential sampling procedures, recent development...