AIMC Topic: Thermodynamics

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A Practical Guide to Transition State Analysis in Biomolecular Simulations with TS-DAR.

The journal of physical chemistry. B
Conformational changes essential for protein function involve transitions through multiple short-lived, high-energy states within the complex free energy landscape. While existing methods, such as Markov State Models and non-Markovian approaches buil...

Predicting drug solubility in supercritical carbon dioxide green solvent using machine learning models based on thermodynamic properties.

Scientific reports
Reliable prediction of drug solubility in supercritical carbon dioxide (scCO₂) is crucial for the efficient design of pharmaceutical processes, including particle engineering and supercritical fluid-based extraction. Given that experimental determina...

AI-designed PNA-peptide chimera overcomes suboptimal binding for dual inhibition of viral RdRp.

European journal of medicinal chemistry
The chimera combining the peptide nucleic acids (PNAs) and peptides represent a promising bifunctional strategy by concurrently binding with protein catalytic pocket and its associated RNA template, effectively disrupting protein's function. Conventi...

Accurate Simulations of Water and Aqueous Solutions through Fine-Tuned Dispersion-Corrected Density Functional Theory and Machine-Learning Interatomic Potentials.

Journal of chemical information and modeling
Dispersion-corrected density functional theory (DFT-D) is widely employed to model large molecular systems at an affordable computational cost and to develop machine-learning interatomic potentials (MLIPs), enabling reliable molecular dynamics (MD) s...

Low-Cost, High-Accuracy Reactivity Modeling: Integrating Genetic Algorithms and Machine Learning with Multilevel DFT Calculations.

Journal of chemical information and modeling
Accurate prediction of Gibbs activation energies (Δ) for Diels-Alder (DA) reactions remains a critical challenge in computational chemistry, as conventional density functional theory (DFT) methods often fail to consistently achieve chemical accuracy ...

TorchANI-Amber: Bridging Neural Network Potentials and Classical Biomolecular Simulations.

The journal of physical chemistry. B
In this work we introduce TorchANI-Amber, an interface for routine molecular dynamics simulations of biomolecular systems using ANI-style machine learning potentials. TochANI-Amber incorporates the ANI neural network potentials into the Amber softwar...

Automating Deep Learning-Based Generation and Evaluation of De Novo Chemical Reaction with ChemRxnSAGE.

Journal of chemical information and modeling
The generation and evaluation of chemical reactions remain challenging with limited comprehensive studies addressing these issues. We introduce the ical Reaction () ystematic ssessment of eneration and valuation () framework, an adaptable end-to-end ...

Thermodynamic Microenvironment Engineering in Mesoporous Nanoreactors to Enhance Biocatalysis for AI-Empowered Ultrasensitive Pathogen Detection.

Analytical chemistry
Harmonizing enzyme-support microenvironments to govern thermodynamic interaction landscapes presents a critical yet underexplored frontier in nanobiocatalysis for pathogen detection. Herein, we architecturally engineer mesoporous resorcinol formaldeh...

On Free Energy Calculations in Drug Discovery.

Accounts of chemical research
ConspectusThis Account discusses recent progress and challenges in binding free energy computations, focusing on two classes of enhanced sampling techniques: alchemical transformations and path-based methods. Binding free energy is a crucial metric i...

MEMO-Stab2: Multi-View Sequence-Based Deep Learning Framework for Predicting Mutation-Induced Stability Changes in Transmembrane Proteins.

Journal of chemical information and modeling
Accurately predicting the impact of point mutations on protein thermodynamic stability is essential for understanding structure-function relationships and guiding protein design. This challenge is particularly acute for transmembrane proteins (TMPs),...