Dataset profile
The two available at OCHEM models predict Melting Point (MP) of organic chemical compounds. The MP is one of the important physico-chemical properties, which is frequently used in drug discovery to determine aqueous solubility of chemical compounds. The data fro for MP were collected
octanol/water partition coefficient (logP) and solubility in water (logS). Both these parameters are important important for drug discovery. The model is further development of ALOGPS 2.1 program [Tetko, I. V.; Tanchuk, V. Y. Application of associative neural networks for prediction of lipophilicity in ALOGPS 2.1 program, J. Chem. Inf. Comput. Sci., 2002, 42, 1136-45 and Tetko, et al Estimation of aqueous solubility of chemical compounds using E-state indices, J. Chem. Inf. Comput. Sci., 2001, 41, 1488-93] which is available at Virtual Computational Laboratory (VCCLAB) site. This program was assessed in several benchmarking studies and was top-ranked for prediction of in house Pfizer and Nycomed [Mannhold et al, Calculation of molecular lipophilicity: State-of-the-art and comparison of log P methods on more than 96,000 compounds. J Pharm Sci. 2009 Mar;98(3):861-93. doi: 10.1002/jps.21494.].
The data for logP and logS were taken from these two previous publications as well as were merged with those collected at OCHEM web site. The training sets included 16647 and 6778 compounds for logP and logS properties, respectively. The data were filtered from the outliers using an automatic p-value based filtering feature of OCHEM (article in preparation). Considering high inter-dependency of both properties, there were modeled simultaneously, using multi-learning feature of OCHEM [Varnek et al, Inductive transfer of knowledge: application of multi-task learning and feature net approaches to model tissue-air partition coefficients. J Chem Inf Model. 2009 Jan;49(1):133-44. doi: 10.1021/ci8002914] to increase the applicability domain of the models.in OCHEM database as well as were provided by Dr. Luc Patiny from ChemExper database.
Data preprocessing
All chemical structures were processed using OCHEM cleaning and standardization protocols. A specific care was used to eliminate salts and mixtures, which can could dramatically change MP of molecules. The detection and elimination of outliers was done using
Descriptors
This The first model (2D) was built using a combination of ALOGPS 2.1 model predictions and EState descriptors (electrotopological EState indices). The EState indices were calculated using a program developed by Dr. Tanchuk, which was also used to develop ALOGPS 2.1 model.
The second model (3D) was built using DRAGON descriptors, which was provided by Prof. Todeschini and Talete Srl. For this model we generated 3D conformations of molecules using CORINA software, which is distributed by Molecular Networks GmbH.
Validation
The model was built using 5-fold cross validation. The dataset of 277 compounds compiled by [Bergstrom et al ]
Statistical parameters
Prediction accuracy
The basic prediction accuracy parameters according to the 5-fold cross-validation procedure are:
| Property | |||||
|---|---|---|---|---|---|
| # records | RMSE | MAE | R2 | r2 (Coefficient of determination) | |
 logP | 16912 | 0.42 | 0.30 | 0.95 | 0.95 |
 logS | 8102 | 0.70 | 0.52 | 0.90 | 0.90 |
Applicability domain
The prediction accuracy is estimated using ASNN-STD. This distance to model was shown to provide the best assessment of the accuracy of prediction as described in [Tetko et al, Critical assessment of QSAR models of environmental toxicity against Tetrahymena pyriformis: focusing on applicability domain and overfitting by variable selection, J Chem Inf Model. 2008 Sep;48(9):1733-46. doi: 10.1021/ci800151m].