Why Are Some Properties More Difficult To Predict than Others? A Study of QSPR Models of Solubility, Melting Point, and Log P

• Published in: Journal of chemical information and modeling 2008-01, Vol.48 (1), p.220-232
• Main Authors: Hughes, Laura D, Palmer, David S, Nigsch, Florian, Mitchell, John B. O
• Format: Article
• Language: English
• Subjects: Aqueous solutions; Artificial Intelligence; Errors; Melting; Models, Chemical; Molecules; Octanols - chemistry; Quantitative Structure-Activity Relationship; Solubility; Transition Temperature; Water - chemistry
• ISSN: 1549-9596; 1549-960X
• DOI: 10.1021/ci700307p

This paper attempts to elucidate differences in QSPR models of aqueous solubility (Log S), melting point (T m), and octanol−water partition coefficient (Log P), three properties of pharmaceutical interest. For all three properties, Support Vector Machine models using 2D and 3D descriptors calculated in the Molecular Operating Environment were the best models. Octanol−water partition coefficient was the easiest property to predict, as indicated by the RMSE of the external test set and the coefficient of determination (RMSE = 0.73, r 2 = 0.87). Melting point prediction, on the other hand, was the most difficult (RMSE = 52.8 °C, r 2 = 0.46), and Log S statistics were intermediate between melting point and Log P prediction (RMSE = 0.900, r 2 = 0.79). The data imply that for all three properties the lack of measured values at the extremes is a significant source of error. This source, however, does not entirely explain the poor melting point prediction, and we suggest that deficiencies in descriptors used in melting point prediction contribute significantly to the prediction errors.