# An Improved Artificial Bee Colony for Feature Selection in QSAR
## 1 Introduction  
Quantitative structure-activity relationship (QSAR) plays a vital role in drug design and discovery [1]. It aims to build the relationship between molecular structure properties of chemical compounds and their corresponding biological activities [2]. In QSAR modeling, the structure properties of the chemical compounds are encoded by a variety of features (molecular descriptors) such as topological, constitutional, thermodynamic parameters. QSAR models can be defined as regression or classification models by using different computational strategies [3]. The features are related with biological activities by using statistical methods or artificial intelligence approaches, such as Multiple Linear Regression (MLR) [4], Support Vector Regression (SVR) [5], Boosted Tree [6], and Partial Least Squares (PLS) regression [7], etc. In particular, machine learning methods have become extensively used in this field during the last few years [8; 9; 10; 11; 12; 13; 14; 15]. These methods effectively improve the accuracy of QSAR modeling to a certain extent. However, several computational issues must be addressed when QSAR models are inferred by machine learning methods. One of these problems is to address the complexity of data sets for selection of appropriate features important for defining a particularQSAR model. Specifically, not all features are related to the activity, and the redundant or irrelevant features may cause over-fitting or weak correlation [16]. The optimal feature subset with only related and non-redundant features increases the accuracy of prediction and the interpretability of the QSAR model. Thus, feature selection (FS) which selects an optimal subset of all features is a vital pre-processing step in QSAR studies to increase the interpretability and improve the prediction accuracy [17]. In principle, feature selection is an NP-hard combination problem. For a search space with \(D\) dimensions, the number of subsets to search is \(2^{D}\). In other words, the search space increases exponentially as the dimension of the given problem grows, hence it is intractable for limited computational resources. Evolutionary Computation (EC) techniques are optimization methods inspired by scientific understanding of natural or social behavior, which can be regarded as search procedures at some abstraction level [18]. In general, these algorithms can be classified as either Evolutionary Algorithms (EAs) or Swarm Intelligence (SI) algorithms [19]. EAs start by randomly generating a set of candidate solutions, iteratively combine these solutions, and implement survival of the fittest until an acceptable solution is achieved. The classic EAs include Genetic Algorithm (GA) [20], Differential Evolution (DE) [21], Biogeography-Based Optimization (BBO) [22], and Genetic Programming (GP) [23], etc. SI algorithms start with a set of individuals, and a new set of individuals is created based on historical information and related information in each iteration. A considerable number of new SI algorithms have emerged, such as Ant Colony Algorithm (ACO) [24], Bat Algorithm (BA) [25], Firefly Algorithm (FA) [26], Cuckoo Search (CS) [27], Coyote Optimization Algorithm (COA) [28], and Social Network Optimization (SNO) [29]. SI is a relatively new category of evolutionary computation comparing with EAs and other single-solution based approaches and has paid sufficient attention to feature selection due to its potential global search ability. In particular, the interaction between features can be considered in the screening process, which breaks through the shortcomings of traditional feature selection algorithms. The surveys [30; 31] have presented the proven usage of SI algorithms for FS. The Artificial Bee Colony (ABC) [32] algorithm, which simulates the intelligent foraging behavior of a honeybee swarm, is one of the most well-known SI algorithms. [PERSON] et al. concluded that, although ABC uses fewer control parameters, it performs better than or at least comparable to other typical SI algorithms [33]. [PERSON] et al.