Multitask machine learning models for predicting lipophilicity (logP) in the SAMPL7 challenge

Abstract

Accurate prediction of lipophilicity-logP-based on molecular structures is a well-established field. Predictions of logP are often used to drive forward drug discovery projects. Driven by the SAMPL7 challenge, in this manuscript we describe the steps that were taken to construct a novel machine learning model that can predict and generalize well. This model is based on the recently described Directed-Message Passing Neural Networks (D-MPNNs). Further enhancements included: both the inclusion of additional datasets from ChEMBL (RMSE improvement of 0.03), and the addition of helper tasks (RMSE improvement of 0.04). To the best of our knowledge, the concept of adding predictions from other models (Simulations Plus logP and logD@pH7.4, respectively) as helper tasks is novel and could be applied in a broader context. The final model that we constructed and used to participate in the challenge ranked 2/17 ranked submissions with an RMSE of 0.66, and an MAE of 0.48 (submission: Chemprop). On other datasets the model also works well, especially retrospectively applied to the SAMPL6 challenge where it would have ranked number one out of all submissions (RMSE of 0.35). Despite the fact that our model works well, we conclude with suggestions that are expected to improve the model even further.

Keywords: D-MPNN; Multitask machine learning; SAMPL7; logP prediction.

Fig. 1

Scatter plot of the performance of the final model (Experimental log P versus Predicted logP) on the test set. On the top a distribution histogram of the predictions is shown and on the right a distribution histogram of the experimental values. The shaded area (very close to the identity line) represents the 95% confidence interval for the regression estimate

Fig. 2

Scatter plot of the performance of the final model (Experimental log P versus Predicted logP) on the SAMPL7 molecules. The compounds discussed in the text and shown in Table 3 are labeled. On the top a distribution histogram of the predictions is shown and on the right a distribution histogram of the experimental values. The shaded area represents the 95% confidence interval for the regression estimate