ArticleThe journal of physical chemistry. B2017
Bayesian Model Averaging for Ensemble-Based Estimates of Solvation-Free Energies.
Article in The journal of physical chemistry. B, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- An Immune-Gene-Based Classifier Predicts Prognosis in Patients With Cervical Squamous Cell Carcinoma.Frontiers in molecular biosciences · 2021Article
- Validation of Free Energy Methods in AMBER.Journal of chemical information and modeling · 2020Article
- Atomic Radius and Charge Parameter Uncertainty in Biomolecular Solvation Energy Calculations.Journal of chemical theory and computation · 2018Article
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Authors and funding
6 authors.
Funding
Abstract
This paper applies the Bayesian Model Averaging statistical ensemble technique to estimate small molecule solvation free energies. There is a wide range of methods available for predicting solvation free energies, ranging from empirical statistical models to ab initio quantum mechanical approaches. Each of these methods is based on a set of conceptual assumptions that can affect predictive accuracy and transferability. Using an iterative statistical process, we have selected and combined solvation energy estimates using an ensemble of 17 diverse methods from the fourth Statistical Assessment of Modeling of Proteins and Ligands (SAMPL) blind prediction study to form a single, aggregated solvation energy estimate. Methods that possess minimal or redundant information are pruned from the ensemble and the evaluation process repeats until aggregate predictive performance can no longer be improved. We show that this process results in a final aggregate estimate that outperforms all individual methods by reducing estimate errors by as much as 91% to 1.2 kcal mol
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