New QSPR study for the prediction of aqueous solubility of drug-like compounds

• Published in: Bioorganic & medicinal chemistry Vol. 16; no. 17; pp. 7944 - 7955
• Main Authors: Duchowicz, Pablo R., Talevi, Alan, Bruno-Blanch, Luis E., Castro, Eduardo A.
• Format: Journal Article
• Language: English
• Published: Oxford Elsevier Ltd 01.09.2008; Elsevier Science
• Subjects: Aqueous solubility; Biological and medical sciences; Computer Simulation; Databases, Factual; DRAGON molecular descriptors; Drug Design; General pharmacology; Group contribution method; Linear Models; Medical sciences; Molecular Structure; Organic Chemicals - chemistry; Pharmaceutical Preparations - chemistry; Pharmacology. Drug treatments; Physicochemical properties. Structure-activity relationships; Predictive Value of Tests; QSPR theory; Quantitative Structure-Activity Relationship; Replacement method variable subset selection; Reproducibility of Results; Solubility; Stereoisomerism; Technology, Pharmaceutical - methods; Water - chemistry; Group contribution method; QSPR theory; DRAGON molecular descriptors; Replacement method variable subset selection; Aqueous solubility; Drug; Prediction; Theoretical study; Modeling; Molecular model; Water solubility; Property structure relationship; Physicochemical properties
• ISSN: 0968-0896; 1464-3391
• DOI: 10.1016/j.bmc.2008.07.067

QSPR analysis on 166 drug-like compounds based on linear combinations of novel indices derived from Lipinski’s ‘rule of five’ and Dragon descriptors. Results are compared with previously reported ones. Solubility has become one of the key physicochemical screens at early stages of the drug development process. Solubility prediction through Quantitative Structure–Property Relationships (QSPR) modeling is a growing area of modern pharmaceutical research, being compatible with both High Throughput Screening technologies and limited compound availability characteristic of early stages of drug development. We resort to the QSPR theory for analyzing the aqueous solubility exhibited by 145 diverse drug-like organic compounds (0.781 being the average Tanimoto distances between all possible pairs of compounds in the training set). An accurate and generally applicable model is derived, consisting on a linear regression equation that involves three DRAGON molecular descriptors selected from more than a thousand available. Alternatively, we apply the linear QSPR to other 21 commonly employed validation compounds, leading to solubility estimations that compare fairly well with the performance achieved by previously reported Group Contribution Methods.