Molecular Descriptor Subset Selection in Theoretical Peptide Quantitative Structure–Retention Relationship Model Development Using Nature-Inspired Optimization Algorithms

Abstract

In this work, performance of five nature-inspired optimization algorithms, genetic algorithm (GA), particle swarm optimization (PSO), artificial bee colony (ABC), firefly algorithm (FA), and flower pollination algorithm (FPA), was compared in molecular descriptor selection for development of quantitative structure-retention relationship (QSRR) models for 83 peptides that originate from eight model proteins. The matrix with 423 descriptors was used as input, and QSRR models based on selected descriptors were built using partial least squares (PLS), whereas root mean square error of prediction (RMSEP) was used as a fitness function for their selection. Three performance criteria, prediction accuracy, computational cost, and the number of selected descriptors, were used to evaluate the developed QSRR models. The results show that all five variable selection methods outperform interval PLS (iPLS), sparse PLS (sPLS), and the full PLS model, whereas GA is superior because of its lowest computational cost and higher accuracy (RMSEP of 5.534%) with a smaller number of variables (nine descriptors). The GA-QSRR model was validated initially through Y-randomization. In addition, it was successfully validated with an external testing set out of 102 peptides originating from Bacillus subtilis proteomes (RMSEP of 22.030%). Its applicability domain was defined, from which it was evident that the developed GA-QSRR exhibited strong robustness. All the sources of the model's error were identified, thus allowing for further application of the developed methodology in proteomics.

Interpretation of ANN‐based QSAR models for prediction of antioxidant activity of flavonoids

“…Finally, the model's chemical domain of applicability was defined to evaluate robustness and its functional prediction range. This was achieved using a Williams plot with a critical leverage ( h *) and three multiples of standard deviation of standardized residuals as warning limits.…”

“…Martínez‐Martínez et al have employed a genetic algorithm coupled with multiple linear regression to select topological and QM molecular descriptors for prediction of in‐vitro antioxidant activity of 3‐carboxycoumarin derivatives. Selected descriptors were further used to build a nonlinear back‐propagation ANN model.…”

A Comparative QSRR Study on Enantioseparation of Ethanol Ester Enantiomers in HPLC Using Multivariate Image Analysis, Quantum Mechanical and Structural Descriptors

“…Structural descriptors including various electronic, geometric, or steric properties of the molecule were computed and used to develop QSRR models …”

“…Structural descriptors including various electronic, geometric, or steric properties of the molecule were computed and used to develop QSRR models. 41,42 In this work, three types of descriptors, i.e., original molecular, quantum mechanical, and MIA, were used.…”

A Quantitative Structure-Property Relationship Model Based on Chaos-Enhanced Accelerated Particle Swarm Optimization Algorithm and Back Propagation Artificial Neural Network

“…The proposed hybrid intelligent model can be applied in engineering design and the prediction of physical and chemical properties.The establishment of the QSPR model mainly involves the following steps: acquisition of experimental data, construction and optimization of the molecular structure, calculation and screening of molecular descriptors, establishment and verification of the model, etc. First of all, the variable selection is important in many fields, such as spectroscopy [7,8], QSPR [9,10], and other fields [11,12]. The selection of molecular descriptors largely determines the quality of the QSPR model [13][14][15].…”