Machine learning in prediction of intrinsic aqueous solubility of drug‐like compounds: Generalization, complexity, or predictive ability?

Abstract

We present a collection of publicly available intrinsic aqueous solubility data of 829 drug-like compounds. Four different machine learning algorithms (random forests [RF], LightGBM, partial least squares, and least absolute shrinkage and selection operator [LASSO]) coupled with multistage permutation importance for feature selection and Bayesian hyperparameter optimization were used for the prediction of solubility based on chemical structural information. Our results show that LASSO yielded the best predictive ability on an external test set with a root mean square error (RMSE) (test) of 0.70 log points, an R 2 (test) of 0.80, and 105 features. Taking into account the number of descriptors as well, an RF model achieves the best balance between complexity and predictive ability with an RMSE(test) of 0.72 log points, an R 2 (test) of 0.78, and with only 17 features. On a more aggressive test set (principal component analysis [PCA]-based split), better generalization was observed for the RFmodel. We propose a ranking score for choosing the best model, as test set performance is only one of the factors in creating an applicable model. The ranking score is a weighted combination of generalization, number of features, and test performance. Out of the two best learners, a consensus model was built exhibiting the best predictive ability and generalization with RMSE(test) of 0.67 log points and a R 2 (test) of 0.81.

Predicting Treatment Outcomes Using Explainable Machine Learning in Children with Asthma

“…Our comparison shows that an algorithm relying on a linear combination of variables fails on average against ensemble algorithms. The low performance of the linear model was also observed in our prior and other works regarding both regression [41,46] and classification tasks [30,44,47]. Besides failing due to nonlinear relationships and boundaries between the classes, linear models can also suffer from the utilization of irrelevant features in models and complex cancellation effects [30].…”

“…Additionally, MEF50-based outcomes were also predicted by hsCRP (Table 6), which is a marker of subtly elevated systemic inflammation in asthma [40]. The evidence shows that increased hsCRP is associated with more severe asthma outcomes [41]. This, in addition to the fact that the model predicting MEF50-related response performed better in almost all parameters (except specificity) compared to the FEV1-related response (see Table 5) and the fact that oversampling further improved the model's power in predicting true responders and non-responders (see Figure 2) for MEF50, highlights the importance of the distal airways in children with asthma [42].…”

“…There is an ongoing discussion whether to use black box models in modeling scenarios [38][39][40][41], such as the used cases presented here as black box models, and their decisions cannot be easily understood. For this reason, we compared the model results of the ensemblers to logistic regression, which was treated in the same manner as the ensemble models, i.e., split 100 times randomly and optimized during cross-validation in The responders are assigned as R-LOAC (responders by level of asthma control), while the nonresponders are assigned as NR-LOAC (non-responders by level of asthma control).…”

Solubility of Sulfamethazine in the Binary Mixture of Acetonitrile + Methanol from 278.15 to 318.15 K: Measurement, Dissolution Thermodynamics, Preferential Solvation, and Correlation

“…Furthermore, solubility is also an important factor in other processes such as dosage, pre-formulation, crystallization, purification, and quantification [1,2]. The importance of drug solubility has led to the development of one of the most important lines of research in the pharmaceutical industry, which consists in the development of mathematical models and has evolved towards the incursion of artificial intelligence in the development of algorithms that predict the solubility of drugs in different solvents [3][4][5][6][7][8][9].…”

Novel Solubility Prediction Models: Molecular Fingerprints and Physicochemical Features vs Graph Convolutional Neural Networks

“…Recent works on predicting solubility through machine learning utilized molecular fingerprints in their work. 20 , 24 − 26 Previous studies use machine learning/deep learning (ML/DL) methods including random forest (RF), support vector regression (SVR), LightGBM, LASSO and so on, 20 , 24 , 26 Naïve-Bayes based models, 25 and also deep learning to predict solubility. While the importance of solubility prediction has been emphasized, various studies have been on solubility prediction has been reported.…”