AIMC Topic: Thermodynamics

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Integrating Bonded and Nonbonded Potentials in the Knowledge-Based Scoring Function for Protein Structure Prediction.

Journal of chemical information and modeling May 13, 2019
An accurate energy scoring function is crucial for protein structure prediction. Given the increasing number of experimentally determined structures, knowledge-based approaches have been widely used to develop scoring functions for protein structure ...
Proteins Knowledge Bases Thermodynamics Computational Biology
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A highly effective, recyclable, and novel host-guest nanocomposite for Triclosan removal: A comprehensive modeling and optimization-based adsorption study.

Journal of colloid and interface science May 3, 2019
In this research paper, response surface methodology (RSM), generalized regression neural network (GRNN), and Adaptive Neuro-Fuzzy Inference System (ANFIS) were employed to develop prediction models for Triclosan (TCS) removal by a novel inclusion co...
Adsorption beta-Cyclodextrins Regression Analysis Kinetics Surface Properties Polyethylene Glycols Thermodynamics Triclosan Nanocomposites Anti-Bacterial Agents Particle Size Neural Networks, Computer Nanotubes, Carbon Water Pollutants, Chemical
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Quantitative Prediction of the Landscape of T Cell Epitope Immunogenicity in Sequence Space.

Frontiers in immunology Apr 16, 2019
Immunodominant T cell epitopes preferentially targeted in multiple individuals are the critical element of successful vaccines and targeted immunotherapies. However, the underlying principles of this "convergence" of adaptive immunity among different...
Computational Biology Datasets as Topic Adaptive Immunity Computer Simulation Epitopes, T-Lymphocyte Antigen Presentation Machine Learning Humans Thermodynamics T-Lymphocytes Receptors, Antigen, T-Cell Immunodominant Epitopes Major Histocompatibility Complex
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Machine Learning Prediction of H Adsorption Energies on Ag Alloys.

Journal of chemical information and modeling Apr 2, 2019
Adsorption energies on surfaces are excellent descriptors of their chemical properties, including their catalytic performance. High-throughput adsorption energy predictions can therefore help accelerate first-principles catalyst design. To this end, ...
Machine Learning Adsorption Molecular Conformation Hydrogen Alloys Models, Molecular Silver Thermodynamics
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Solvent-Specific Featurization for Predicting Free Energies of Solvation through Machine Learning.

Journal of chemical information and modeling Mar 18, 2019
A featurization algorithm based on functional class fingerprints has been implemented within the DeepChem machine learning framework. It is based on descriptors more appropriate for solvation, taking into account intermolecular properties, and has be...
Cheminformatics Solvents Thermodynamics Machine Learning
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Improved Method of Structure-Based Virtual Screening via Interaction-Energy-Based Learning.

Journal of chemical information and modeling Mar 18, 2019
Virtual screening is a promising method for obtaining novel hit compounds in drug discovery. It aims to enrich potentially active compounds from a large chemical library for further biological experiments. However, the accuracy of current virtual scr...
Cheminformatics Protein Conformation Thermodynamics Molecular Docking Simulation User-Computer Interface Drug Evaluation, Preclinical Ligands Proteins Machine Learning
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Integrated information in the thermodynamic limit.

Neural networks : the official journal of the International Neural Network Society Mar 11, 2019
The capacity to integrate information is a prominent feature of biological, neural, and cognitive processes. Integrated Information Theory (IIT) provides mathematical tools for quantifying the level of integration in a system, but its computational c...
Humans Neural Networks, Computer Information Theory Thermodynamics
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Solvation Free Energy Calculations with Quantum Mechanics/Molecular Mechanics and Machine Learning Models.

The journal of physical chemistry. B Jan 15, 2019
For exploration of chemical and biological systems, the combined quantum mechanics and molecular mechanics (QM/MM) and machine learning (ML) models have been developed recently to achieve high accuracy and efficiency for molecular dynamics (MD) simul...
Solvents Machine Learning Thermodynamics Molecular Dynamics Simulation Quantum Theory
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Toward Building Protein Force Fields by Residue-Based Systematic Molecular Fragmentation and Neural Network.

Journal of chemical theory and computation Jan 7, 2019
Accurate force fields are crucial for molecular dynamics investigation of complex biological systems. Building accurate protein force fields from quantum mechanical (QM) calculations is challenging due to the complexity of proteins and high computati...
Neural Networks, Computer Peptides Models, Chemical Amino Acids Proteins Dipeptides Molecular Dynamics Simulation Quantum Theory Static Electricity Thermodynamics
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Prediction of Membrane Permeation of Drug Molecules by Combining an Implicit Membrane Model with Machine Learning.

Journal of chemical information and modeling Dec 27, 2018
Lipid membrane permeation of drug molecules was investigated with Heterogeneous Dielectric Generalized Born (HDGB)-based models using solubility-diffusion theory and machine learning. Free energy profiles were obtained for neutral molecules by the st...
Thermodynamics Drug Evaluation, Preclinical Cell Membrane Permeability Machine Learning Models, Biological
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