ArticleJournal of computer-aided molecular design2020
SAMPL6 logP challenge: machine learning and quantum mechanical approaches.
Article in Journal of computer-aided molecular design, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
4 citing papers in PubMed.
- Integrating multiscale and machine learning approaches towards the SAMPL9 logPhysical chemistry chemical physics : PCCP · 2024Article
- Multiple linear regression models for predicting the n‑octanol/water partition coefficients in the SAMPL7 blind challenge.Journal of computer-aided molecular design · 2021Article
- Predicting partition coefficients for the SAMPL7 physical property challenge using the ClassicalGSG method.Journal of computer-aided molecular design · 2021Article
- Assessing the accuracy of octanol-water partition coefficient predictions in the SAMPL6 Part II log P Challenge.Journal of computer-aided molecular design · 2020Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Two different types of approaches: (a) approaches that combine quantitative structure activity relationships, quantum mechanical electronic structure methods, and machine-learning and, (b) electronic structure vertical solvation approaches, were used to predict the logP coefficients of 11 molecules as part of the SAMPL6 logP blind prediction challenge. Using electronic structures optimized with density functional theory (DFT), several molecular descriptors were calculated for each molecule, including van der Waals areas and volumes, HOMO/LUMO energies, dipole moments, polarizabilities, and electrophilic and nucleophilic superdelocalizabilities. A multilinear regression model and a partial least squares model were used to train a set of 97 molecules. As well, descriptors were generated using the molecular operating environment and used to create additional machine learning models. Electronic structure vertical solvation approaches considered include DFT and the domain-based local pair natural orbital methods combined with the solvated variant of the correlation consistent composite approach.
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Registered trials
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