Dimensionally reduced machine learning model for predicting single component octanol–water partition coefficients

Abstract

MF-LOGP, a new method for determining a single component octanol–water partition coefficients ($$LogP$$ LogP ) is presented which uses molecular formula as the only input. Octanol–water partition coefficients are useful in many applications, ranging from environmental fate and drug delivery. Currently, partition coefficients are either experimentally measured or predicted as a function of structural fragments, topological descriptors, or thermodynamic properties known or calculated from precise molecular structures. The MF-LOGP method presented here differs from classical methods as it does not require any structural information and uses molecular formula as the sole model input. MF-LOGP is therefore useful for situations in which the structure is unknown or where the use of a low dimensional, easily automatable, and computationally inexpensive calculations is required. MF-LOGP is a random forest algorithm that is trained and tested on 15,377 data points, using 10 features derived from the molecular formula to make $$LogP$$ LogP predictions. Using an independent validation set of 2713 data points, MF-LOGP was found to have an average $$RMSE$$ RMSE = 0.77 ± 0.007, $$MAE$$ MAE = 0.52 ± 0.003, and $${R}^{2}$$ R 2 = 0.83 ± 0.003. This performance fell within the spectrum of performances reported in the published literature for conventional higher dimensional models ($$RMSE$$ RMSE = 0.42–1.54, $$MAE$$ MAE = 0.09–1.07, and $${R}^{2}$$ R 2 = 0.32–0.95). Compared with existing models, MF-LOGP requires a maximum of ten features and no structural information, thereby providing a practical and yet predictive tool. The development of MF-LOGP provides the groundwork for development of more physical prediction models leveraging big data analytical methods or complex multicomponent mixtures. Graphical Abstract

Predicting Partition Coefficients in Organic Biphasic Systems Using COSMO-RS

“…Regarding the RMSD found, there is a lack of consensus about under what deviation may be admissible. For instance, Kenney et al 24 reported RMSD = 1 as a good value for octanol−water partition coefficient predictions. Instead, Vilas-Boas et al claimed that acceptable RMSD values should be lower than 0.5.…”

Prediction of toluene/water partition coefficients in the SAMPL9 blind challenge: assessment of machine learning and IEF-PCM/MST continuum solvation models

“…73 On the other hand, more complex ML methods such as RFR have been used to describe the quantitative structure-property relationships with lipophilicity. 74,75 Following these studies, MLR and RFR are used here for predicting the log P tol/w values of 252 neutral compounds that encompass a diverse range of physicochemical properties (Fig. 3).…”

Prediction of Toluene/Water Partition Coefficient in the SAMPL9 Blind Challenge: Assessment of Machine Learning and IEF-PCM/MST Continuum Solvation Models

“…hydrophobic area and chain descriptors [73], and QM-based electronic properties [74]. On the other hand, more complex ML methods such as RFR have been used to describe the quantitative structure-property relationships with lipophilicity [75,76]. Following these studies, MLR and RFR are used here for predicting the logPtol/w values of 252 neutral compounds that encompass a diverse range of physicochemical properties (Fig.…”