Calculation of molecular lipophilicity: state of the art and comparison of methods on more than 96000 compounds

By Raimund Mannhold, Gennadiy Poda, Claude Ostermann, Igor V. Tetko. Journal of Pharmacy and Science, 2009

604 citations recorded by the source corpus.

We first review the state-of-the-art in development of log P prediction approaches falling in two major categories: substructure-based and property-based methods. Then, we compare the predictive power of representative methods for one public (N = 266) and two in house datasets from Nycomed (N = 882) and Pfizer (N = 95809). A total of 30 and 18 methods were tested for public and industrial datasets, respectively. Accuracy of models declined with the number of nonhydrogen atoms. The Arithmetic Average Model (AAM), which predicts the same value (the arithmetic mean) for all compounds, was used as a baseline model for comparison. Methods with Root Mean Squared Error (RMSE) greater than RMSE produced by the AAM were considered as unacceptable. The majority of analyzed methods produced reasonable results for the public dataset but only seven methods were successful on the both in house datasets. We proposed a simple equation based on the number of carbon atoms, NC, and the number of hetero atoms, NHET: log P = 1.46(+/-0.02) + 0.11(+/-0.001) NC-0.11(+/-0.001) NHET. This equation outperformed a large number of programs benchmarked in this study. Factors influencing the accuracy of log P predictions were elucidated and discussed.

Subfields: Computational Theory and Mathematics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/632be3226e285e8940075eef2a4899db91be9c15

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Citations in the corpus

The influence of lipophilicity in drug discovery and design (2012, methodology): “The use of confidence in log P prediction would allow higher resolution and discrimination with regard to selection of reliable and non-reliable predictions, thus increasing design efficiency [35].”
Lipophilicity in drug discovery (2010, methodology): “There exist a plethora of different computational methods for prediction of logP and logD [10].”
Online chemical modeling environment (OCHEM): web platform for data storage, model development and publishing of chemical information (2011, methodology): “For example, twofold differences (1 and 2 log units) were observed using two different implementations of the MLOGP algorithm [5] for predictions of logP for 95,809 Pﬁzer compounds [6].”
MetFrag relaunched: incorporating strategies beyond in silico fragmentation (2016, neutral): “[31] for an overview on different methods).”
Guidelines for the welfare and use of animals in cancer research (2010, background): “Chemical and physical properties In silico predictions or in vitro measurements of logP (Mannhold et al, 2009), logD (Bruneau and McElroy, 2006; Dohta et al, 2007), pKa (Lee et al, 2007) and solubility (Colclough et al, 2008; Du-Cuny et al, 2008) Metabolism Mouse, rat, human liver microsomes,…”
An in silico skin absorption model for fragrance materials. (2014, positive): “It has been suggested that using a consensus model based on the average prediction of different models could yield more accurate results than individual models (Mannhold et al., 2009; Tetko et al., 2009).”