Background: 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.

Objective: 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.

Methods: 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.

Results: 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.

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

背景：在过去的几年中，相似性搜索作为执行基于配体的虚拟筛选（LBVS）的方法而获得了广泛的普及。该筛选技术通过将目标化合物的特征与化合物数据库中每种化合物的特征进行比较来发挥作用。众所周知，每次相似性度量都不能代表代表所有类型活性类别的活性化合物结构的最佳性能。在文献中，我们发现了已提出的几种技术和策略，以提高基于配体的虚拟筛选方法的整体有效性。

目的：在这项工作中，我们的主要目的是提出一种基于遗传算法（FSGASS）的特征选择方法，以改善与基于配体的虚拟筛选有关的相似性搜索。

方法：我们的贡献使我们能够确定化合物的最重要和最相关的特征，并最大程度地减少其表示形式。这样可以减少特征空间，消除冗余，减少训练执行时间，并提高筛选过程的性能。

结果：获得的结果表明，与使用谷本系数获得的结果相比，该结果优越，后者被认为是量化LBVS域相似性的最广泛的系数。

结论：我们的结果表明，在特征选择的基础上，使用分子相似性研究方法可以获得显着改善。