AIMC Topic: Solvents

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Predicting RNA solvent accessibility from multi-scale context feature via multi-shot neural network.

Analytical biochemistry Jul 7, 2022
Knowledge of RNA solvent accessibility has recently become attractive due to the increasing awareness of its importance for key biological process. Accurately predicting the solvent accessibility of RNA is crucial for understanding its 3D structure a...
Neural Networks, Computer Nucleotides RNA Solvents
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A Physics-Guided Neural Network for Predicting Protein-Ligand Binding Free Energy: From Host-Guest Systems to the PDBbind Database.

Biomolecules Jun 29, 2022
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...
Neural Networks, Computer Protein Binding Proteins Ligands Solvents Thermodynamics Physics
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Factor analysis of error in oxidation potential calculation: A machine learning study.

Journal of computational chemistry Jun 28, 2022
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...
Machine Learning Factor Analysis, Statistical Models, Molecular Solvents Solutions Thermodynamics
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Reconstruction of Nuclear Ensemble Approach Electronic Spectra Using Probabilistic Machine Learning.

Journal of chemical theory and computation Apr 28, 2022
The theoretical prediction of molecular electronic spectra by means of quantum mechanical (QM) computations is fundamental to gain a deep insight into many photophysical and photochemical processes. A computational strategy that is attracting signifi...
Machine Learning Quantum Theory Electronics Solvents
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Beyond Woodward-Fieser Rules: Design Principles of Property-Oriented Chromophores Based on Explainable Deep Learning Optical Spectroscopy.

Journal of chemical information and modeling Apr 27, 2022
An adequate understanding of molecular structure-property relationships is important for developing new molecules with desired properties. Although deep learning optical spectroscopy (DLOS) has been successfully applied to predict the optical and pho...
Deep Learning Molecular Structure Solvents Spectrum Analysis Chemical Phenomena
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Harnessing Deep Learning for Optimization of Lennard-Jones Parameters for the Polarizable Classical Drude Oscillator Force Field.

Journal of chemical theory and computation Apr 1, 2022
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...
Drug Design Deep Learning Solvents Thermodynamics
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Prediction of Maximum Absorption Wavelength Using Deep Neural Networks.

Journal of chemical information and modeling Mar 15, 2022
Fluorescent molecules are important tools in biological detection, and numerous efforts have been made to develop compounds to meet the desired photophysical properties. For example, tuning the wavelength allows an appropriate penetration depth with ...
Humans Neural Networks, Computer Machine Learning Fluorescent Dyes Solvents
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Implicitly perturbed Hamiltonian as a class of versatile and general-purpose molecular representations for machine learning.

Nature communications Mar 10, 2022
Unraveling challenging problems by machine learning has recently become a hot topic in many scientific disciplines. For developing rigorous machine-learning models to study problems of interest in molecular sciences, translating molecular structures ...
Machine Learning Solvents
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Machine Learning May Sometimes Simply Capture Literature Popularity Trends: A Case Study of Heterocyclic Suzuki-Miyaura Coupling.

Journal of the American Chemical Society Mar 8, 2022
Applications of machine learning (ML) to synthetic chemistry rely on the assumption that large numbers of literature-reported examples should enable construction of accurate and predictive models of chemical reactivity. This paper demonstrates that a...
Neural Networks, Computer Machine Learning Algorithms Solvents
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Group Contribution and Machine Learning Approaches to Predict Abraham Solute Parameters, Solvation Free Energy, and Solvation Enthalpy.

Journal of chemical information and modeling Jan 19, 2022
We present a group contribution method (SoluteGC) and a machine learning model (SoluteML) to predict the Abraham solute parameters, as well as a machine learning model (DirectML) to predict solvation free energy and enthalpy at 298 K. The proposed gr...
Neural Networks, Computer Machine Learning Solvents Solutions Thermodynamics Entropy
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