Genetic Algorithm-based Feature Selection Approach for Enhancing the Effectiveness of Similarity Searching in Ligand-based Virtual Screening

Abstract

: In the last years, similarity searching has gained wide popularity as a method for performing ligand-based virtual screening (LBVS). This screening technique functions by making a comparison of the target compound’s features with that of each compound’s features in the database of compounds. It is well known that none of the individual similarity measure could provide the best performances each time pertaining to active compound structure representing all types of activity classes. In the literature, we find several techniques and strategies that have been proposed to improve the overall effectiveness of ligand-based virtual screening approaches. In this paper, a genetic algorithm-based feature selection approach is put forward to improve similarity searching pertaining to ligand-based virtual screening. In this study, we demonstrated how genetic algorithms can be applied to enable optimisation of screening process’s performance by choosing the most relevant features. Three different benchmark datasets taken from the MDDR (drug data report database) are employed to examine and assess the performance of our proposed approach. The obtained results demonstrate superiority in performances compared with these obtained with Tanimoto coefficient, which is considered as the most performing coefficient of the domain of LBVS.

Convolutional Neural Network Model Based on 2D Fingerprint for Bioactivity Prediction

“…In their study, the multilabel-predicted chemical activity profiling was successfully accomplished by SVM classifiers, and they suggest that the proposed approach can forecast the biological activities of unidentified chemicals or signal negative consequences of drug candidates. In [ 11 , 31 ], the Bayesian belief network classifier was applied to predict the compound’s target activities. The authors applied a novel technique to extend previous work, based on a convolutional neural network that uses the 2D fingerprint representation to predict the possibly bioactive molecules.…”

“…The proposed prediction model was experimentally evaluated using multiple datasets. This study used three datasets ( Table 5 , Table 6 and Table 7 ), which were described earlier in [ 43 , 44 ] and used in several studies for validating the ligand-based virtual screening methods [ 7 , 11 , 24 , 31 , 45 , 46 ].…”

Assessing the Adequacy of Hemodialysis Patients via the Graph-Based Takagi-Sugeno-Kang Fuzzy System

“…In addition, the number of samples collected has not yet reached a certain scale. In future work, we will introduce other machine learning techniques such as sample filtering and feature selection [ 37 , 38 ] to deal with various types of noise. At the same time, further expanding the patient sample size is also the work of the next step.…”

A Self-Representation-Based Fuzzy SVM Model for Predicting Vascular Calcification of Hemodialysis Patients

“…Although our method has achieved a better accuracy, it still has the following disadvantage: (1) The sample size needs to be further increased to minimize the prediction bias. (2) There is no detailed analysis of the various factors of the patient [37,38]. (3) The interpretability of the model is not as good as that of the linear model.…”