Open source application for small molecule pKa predictions
Creators: name: Baltruschat, Marcel
name: Bushiri, David
name: Tapavicza, Enrico
name: Czodrowski, Paul
Publisher: name: Zenodo
Year: 2021
Subjects: pKa prediction
machine learning
computational chemistry

description: The acid-base dissociation constant (p<em>K</em><sub>a</sub>) of a drug has a far-reaching influence on pharmacokinetics by altering the solubility, membrane permeability and protein binding affinity of the drug [1,2]. To the best of our knowledge, there is no publicly available, open source and license-free p<em>K</em><sub>a</sub> prediction tool that can reach the quality of commercial tools. Our goal is to develop a highly accurate p<em>K</em><sub>a</sub> prediction tool based on a mixture of freely accessible and commercial data which is however still free to use for everyone. To do so, we identified multiple freely available experimental p<em>K</em><sub>a</sub> datasets, including data from <em>DataWarrior</em>, <em>ChEMBL</em> and various scientific publications. Additionally, we have access to several commercial data sets, for example from <em>Novartis</em> [3] and <em>OpenEye</em> [4]. We started with monoprotic molecules obtained from <em>ChEMBL</em> and <em>DataWarrior</em> to evaluate the dataset quality and our modelling concepts. After preprocessing 5994 unique structures with a p<em>K</em><sub>a</sub> value between 2 and 12 were used for machine learning. We tested seven different machine learning configurations including four different basic regressors together with six unique descriptor/fingerprint sets resulting in a total number of 42 trained and 5-fold cross-validated models. Additionally, we evaluated the models with two external test datasets. The results have been published in March 2020 [5]. Furthermore, we investigated how Graph Convolutional Networks and QM-based approaches can be used to further improve prediction quality. To be able to predict the p<em>K</em><sub>a</sub> values of multiprotic molecules, two major problems had to be solved: Localization of the titratable groups without licensed software and the once-only assignment of the experimental values to the corresponding groups for all datasets. For the localization part we evaluated the results of <em>Marvin</em> [6] and <em>Dimorphite-DL</em> [7] to compile a list of 24 SMARTS pattern that catch almost 90% of all groups in our combined dataset of over 17000 unique molecules. Finally, the Marvin [6] predictions were used to assign the experimental values to the corresponding group while removing outliers. The resulting data set can be used as a starting point for machine learning in a following step. All data and code can be found at <em>https://github.com/czodrowskilab</em> [1] Charifson, P. S., &amp; Walters, W. P. (2014). Acidic and Basic Drugs in Medicinal Chemistry: A Perspective. <em>Journal of Medicinal Chemistry</em> [2] Manallack, D. T. (2007). The pKa Distribution of Drugs: Application to Drug Discovery. <em>Perspectives in Medicinal Chemistry</em> [3] Richard A. Lewis, Stephane Rodde, Novartis Pharma AG, Basel, Switzerland [4] pKa COMPLETE_DATABASE v1.13: OpenEye Scientific Software, Santa Fe, NM. [5] Baltruschat M and Czodrowski P. Machine learning meets pKa [version 2; peer review: 2 approved]. <em>F1000Research</em> 2020, 9(Chem Inf Sci):113 [6] Marvin 20.1.0, 2020, ChemAxon Ltd, <em>http://www.chemaxon.com</em> [7] Ropp PJ, Kaminsky JC, Yablonski S, Durrant JD (2019) Dimorphite-DL: An open-source program for numerating the ionization states of drug-like small molecules. <em>Journal of Cheminformatics</em>