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Comparison of logP and logD correction models trained with public and proprietary data sets

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Abstract

In drug discovery, partition and distribution coefficients, logP and logD for octanol/water, are widely used as metrics of the lipophilicity of molecules, which in turn have a strong influence on the bioactivity and bioavailability of potential drugs. There are a variety of established methods, mostly fragment or atom-based, to calculate logP while logD prediction generally relies on calculated logP and pKa for the estimation of neutral and ionized populations at a given pH. Algorithms such as ClogP have limitations generally leading to systematic errors for chemically related molecules while pKa estimation is generally more difficult due to the interplay of electronic, inductive and conjugation effects for ionizable moieties. We propose an integrated machine learning QSAR modeling approach to predict logD by training the model with experimental data while using ClogP and pKa predicted by commercial software as model descriptors. By optimizing the loss function for the ClogD calculated by the software, we build a correction model that incorporates both descriptors from the software and available experimental logD data. Additionally, we calculate logP from the logD model using the software predicted pKa’s. Here, we have trained models using publicly or commercial available logD data to show that this approach can improve on commercial software predictions of lipophilicity. When applied to other logD data sets, this approach extends the domain of applicability of logD and logP predictions over commercial software. Performance of these models favorably compare with models built with a larger set of proprietary logD data.

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Data availability

The ChEMBL logD data, as described in the methods section, is included in the supplemental material, along predictions. We also provide as supplemental material a detailed list of descriptors used in model training and an analysis of the property space for the main three sets used in this work. Due to licensing limitations, the BioByte logD data sets are not available in this publication. Moka calculations for the various sets are subjected to licensing limitations and are only shown for two examples in the results section. Genentech’s internal logD data sets are not available for publication.

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Acknowledgements

At Genentech: Huy Nguyen, Fabio Broccatelli, and Hao Zheng.

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  1. Discovery Chemistry, Genentech Inc, 1 DNA Way, South San Francisco, CA, 94080, USA

    Ignacio Aliagas, Alberto Gobbi, Man-Ling Lee & Benjamin D. Sellers

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  1. Ignacio Aliagas
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Aliagas, I., Gobbi, A., Lee, ML. et al. Comparison of logP and logD correction models trained with public and proprietary data sets. J Comput Aided Mol Des 36, 253–262 (2022). https://doi.org/10.1007/s10822-022-00450-9

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