Journal Article

Open-source QSAR models for pKa prediction using multiple machine learning approaches

Sprankle, Catherine S., Williams, Antony J., Korotcov, Alexandru, Mansouri, Kamel, Allen, David, Casey, Warren M., Kleinstreuer, Nicole C., Grulke, Chris M., Cariello, Neal F., Tkachenko, Valery

2019

Overview

Background The logarithmic acid dissociation constant pKa reflects the ionization of a chemical, which affects lipophilicity, solubility, protein binding, and ability to pass through the plasma membrane. Thus, pKa affects chemical absorption, distribution, metabolism, excretion, and toxicity properties. Multiple proprietary software packages exist for the prediction of pKa, but to the best of our knowledge no free and open-source programs exist for this purpose. Using a freely available data set and three machine learning approaches, we developed open-source models for pKa prediction. Methods The experimental strongest acidic and strongest basic pKa values in water for 7912 chemicals were obtained from DataWarrior, a freely available software package. Chemical structures were curated and standardized for quantitative structure–activity relationship (QSAR) modeling using KNIME, and a subset comprising 79% of the initial set was used for modeling. To evaluate different approaches to modeling, several datasets were constructed based on different processing of chemical structures with acidic and/or basic pKas. Continuous molecular descriptors, binary fingerprints, and fragment counts were generated using PaDEL, and pKa prediction models were created using three machine learning methods, (1) support vector machines (SVM) combined with k-nearest neighbors (kNN), (2) extreme gradient boosting (XGB) and (3) deep neural networks (DNN). Results The three methods delivered comparable performances on the training and test sets with a root-mean-squared error (RMSE) around 1.5 and a coefficient of determination (R 2 ) around 0.80. Two commercial pKa predictors from ACD/Labs and ChemAxon were used to benchmark the three best models developed in this work, and performance of our models compared favorably to the commercial products. Conclusions This work provides multiple QSAR models to predict the strongest acidic and strongest basic pKas of chemicals, built using publicly available data, and provided as free and open-source software on GitHub.

• Publisher: Springer International Publishing,BioMed Central Ltd,Springer Nature B.V,BMC
• Subject: Artificial intelligence / Artificial neural networks / Benchmarking / Cell membranes / Chemical 2D descriptors / Chemical fingerprints / Cheminformatics / Chemistry / Chemistry and Materials Science / Computational Biology/Bioinformatics / Computational chemistry / Computer Applications in Chemistry / Computer programs / DataWarrior / Dissociation reactions / Documentation and Information in Chemistry / Excretion / Freeware / Ionization / Learning algorithms / Lipophilicity / Machine learning / Methods / Modelling / Neural networks / Open source software / Organic chemistry / pKa prediction / Prediction models / Protein binding / QSAR / Research Article / Root-mean-square errors / Software / Software packages / Source programs / Structure-activity relationships / Structure-activity relationships (Pharmacology) / Support vector machines / Test sets / Theoretical and Computational Chemistry / Toxicity