Multiple linear regression models for predicting the n‑octanol/water partition coefficients in the SAMPL7 blind challenge
| Autor: | Kenneth Lopez, Silvana Pinheiro, William J. Zamora |
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| Rok vydání: | 2021 |
| Předmět: |
Empirical methods
Mean absolute error Context (language use) Multiple linear regression model Article Organic molecules Drug Discovery Statistics Linear regression Computer Simulation Physical and Theoretical Chemistry Multiple linear regression N-sulfonamides Mathematics SAMPL7 blind challenge n-Octanol/water partition coefficients Water 1-Octanol Computer Science Applications Partition coefficient Models Chemical Solubility Linear Models Octanol water partition Quantum Theory Thermodynamics |
| Zdroj: | Journal of Computer-Aided Molecular Design |
| ISSN: | 1573-4951 0920-654X |
| DOI: | 10.1007/s10822-021-00409-2 |
| Popis: | A multiple linear regression model called MLR-3 is used for predicting the experimental n-octanol/water partition coefficient (log PN) of 22 N-sulfonamides proposed by the organizers of the SAMPL7 blind challenge. The MLR-3 method was trained with 82 molecules including drug-like sulfonamides and small organic molecules, which resembled the main functional groups present in the challenge dataset. Our model, submitted as “TFE-MLR”, presented a root-mean-square error of 0.58 and mean absolute error of 0.41 in log P units, accomplishing the highest accuracy, among empirical methods and also in all submissions based on the ranked ones. Overall, the results support the appropriateness of multiple linear regression approach MLR-3 for computing the n-octanol/water partition coefficient in sulfonamide-bearing compounds. In this context, the outstanding performance of empirical methodologies, where 75% of the ranked submissions achieved root-mean-square errors |
| Databáze: | OpenAIRE |
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