AIMC Topic: Thermodynamics

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The ligand binding mechanism to purine nucleoside phosphorylase elucidated via molecular dynamics and machine learning.

Nature communications
The study of biomolecular interactions between a drug and its biological target is of paramount importance for the design of novel bioactive compounds. In this paper, we report on the use of molecular dynamics (MD) simulations and machine learning to...

Representing the potential-energy surface of protonated water clusters by high-dimensional neural network potentials.

Physical chemistry chemical physics : PCCP
Investigating the properties of protons in water is essential for understanding many chemical processes in aqueous solution. While important insights can in principle be gained by accurate and well-established methods like ab initio molecular dynamic...

Experimental analysis and mathematical prediction of Cd(II) removal by biosorption using support vector machines and genetic algorithms.

New biotechnology
We investigated the bioremoval of Cd(II) in batch mode, using dead and living biomass of Trichoderma viride. Kinetic studies revealed three distinct stages of the biosorption process. The pseudo-second order model and the Langmuir model described wel...

Design Space Exploration and Machine Learning Prediction of Hydrofluorocarbon Solubility in Ionic Liquids for Refrigerant Separation.

Journal of chemical information and modeling
Ionic liquids (ILs) are promising solvents for the separation of hydrofluorocarbon (HFC) mixtures due to their tunable solvation properties and negligible vapor pressure. We present a computational study of -32 and -125 solubility in over 341,000 ILs...

SMILES Token Additivity Model with Interpretability and Generalizability for Fuel Property Predictions.

Journal of chemical information and modeling
Deep learning models for the quantitative structure-property relationship (QSPR) have traditionally encountered challenges related to limited interpretability and generalizability. In this study, we present the simplified molecular input line entry s...

The Hidden Crux of Correctly Determining Octanol-Water Partition Coefficients.

Molecular pharmaceutics
The partitioning of molecules between an aqueous and an organic medium is of major interest for pharmaceutical development and the chemical industry. It characterizes the impact of substances to the environment and to humans, e.g., their accumulation...

A Unified Explanation for Drug Repurposing and Pharmacological Pleiotropy Based on Classical and Statistical Thermodynamics.

Pharmacology research & perspectives
Drug repurposing is an authentic, emerging, and growing aspect of drug development when the demand for new therapeutic solutions is high. Many repurposed drugs have been discovered by serendipity or a non-ordered process driven by chance and sharp ob...

Predicting rare DNA conformations via dynamical graphical models: a case study of the B→A transition.

Nucleic acids research
DNA exhibits local conformational preferences that affect its ability to adopt biologically relevant conformations, such as those required for binding proteins. Traditional methods, like Markov state models and molecular dynamics (MD) simulations, ha...

Advancing Amorphous Solid Dispersions Design: Insights into Dissolution Kinetics via Thermodynamic Descriptor and Machine Learning.

Molecular pharmaceutics
Amorphous solid dispersions (ASD) are an effective strategy for enhancing the solubility and bioavailability of poorly soluble drugs. However, designing and optimizing ASD formulations often rely on extensive dissolution experiments without sufficie...

ENsiRNA: A Multimodality Method for siRNA-mRNA and Modified siRNA Efficacy Prediction Based on Geometric Graph Neural Network.

Journal of molecular biology
With the rise of small interfering RNA (siRNA) as a therapeutic tool, effective siRNA design is crucial. Current methods often emphasize sequence-related features, overlooking structural information. To address this, we introduce ENsiRNA, a multimoda...