Harmony Search as a Powerful Tool for Feature Selection in QSPR Study of the Drugs Lipophilicity

Title:Harmony Search as a Powerful Tool for Feature Selection in QSPR Study of the Drugs Lipophilicity

Volume: 20 Issue: 4

Author(s): Behnoosh Bahadori and Morteza Atabati*

Affiliation:

• School of Chemistry, Damghan University, Damghan,Iran

Keywords: Lipophilicity, drugs, harmony search, QSPR, feature selection.

Abstract: Aims & Scope: Lipophilicity represents one of the most studied and most frequently used fundamental physicochemical properties. In the present work, harmony search (HS) algorithm is suggested to feature selection in quantitative structure-property relationship (QSPR) modeling to predict lipophilicity of neutral, acidic, basic and amphotheric drugs that were determined by UHPLC. Harmony search is a music-based metaheuristic optimization algorithm. It was affected by the observation that the aim of music is to search for a perfect state of harmony.

Materials & Methods: Semi-empirical quantum-chemical calculations at AM1 level were used to find the optimum 3D geometry of the studied molecules and variant descriptors (1497 descriptors) were calculated by the Dragon software. The selected descriptors by harmony search algorithm (9 descriptors) were applied for model development using multiple linear regression (MLR). In comparison with other feature selection methods such as genetic algorithm and simulated annealing, harmony search algorithm has better results. The root mean square error (RMSE) with and without leave-one out cross validation (LOOCV) were obtained 0.417 and 0.302, respectively.

Results & Conclusion: The results were compared with those obtained from the genetic algorithm and simulated annealing methods and it showed that the HS is a helpful tool for feature selection with fine performance.

Cite this article as:

Bahadori Behnoosh and Atabati Morteza *, Harmony Search as a Powerful Tool for Feature Selection in QSPR Study of the Drugs Lipophilicity, Combinatorial Chemistry & High Throughput Screening 2017; 20 (4) . https://dx.doi.org/10.2174/1386207320666170315123604

DOI https://dx.doi.org/10.2174/1386207320666170315123604	Print ISSN 1386-2073
Publisher Name Bentham Science Publisher	Online ISSN 1875-5402