{'fraction_non_alphanumeric': 0.015780245470485097, 'fraction_numerical': 0.0, 'mean_word_length': 5.294117647058823, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Genetic Algorithm-based Feature Selection Approach for Enhancing the Effectiveness of Similarity Searching in Ligand-based Virtual Screening



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
in the database of compounds. It is well known that none of the individual similarity measures
could provide the best performances each time pertaining to an 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 work, our main objective is to propose a features selection approach based on
genetic algorithm (FSGASS) to improve similarity searching pertaining to ligand-based virtual
screening.



Our contribution allows us to identify the most important and relevant characteristics of
chemical compounds and to minimize their number in their representations. This will allow the
reduction of features space, the elimination of redundancy, the reduction of training execution
time, and the increase of the performance of the screening process.



The obtained results demonstrate superiority in the performance compared with these
obtained with Tanimoto coefficient, which is considered as the most widely coefficient to quantify
the similarity in the domain of LBVS.



Our results show that significant improvements can be obtained by using molecular
similarity research methods at the basis of features selection.
