Advantages of Relative versus Absolute Data for the Development of Quantitative Structure–Activity Relationship Classification Models

Ruiz, Irene Luque; Gómez‐Nieto, Miguel Ángel

doi:10.1021/acs.jcim.7b00492

Cited by 13 publications

(2 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Some of the features include characterization and topological indices. Therefore, molecular descriptors are highly important in pharmaceutical sciences and chemistry 4 .…”

Section: Methodsmentioning

confidence: 99%

“…A drug is an organic molecule that can inhibit the effects of a disease. The main points for drug design and discovery are: (1) structure optimization 3 , (2) establishment of the quantitative structure-activity relationship (QSAR) 4 , and (3) docking of the ligand into a receptor denovo design of ligands 5 . Thus, drug design and discovery aim to develop new medicines based on the knowledge about a biological target 6 .…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Hybrid Harris hawks optimization with cuckoo search for drug design and discovery in chemoinformatics

Houssein

Hosney²,

Elhoseny

et al. 2020

Sci Rep

View full text Add to dashboard Cite

one of the major drawbacks of cheminformatics is a large amount of information present in the datasets. In the majority of cases, this information contains redundant instances that affect the analysis of similarity measurements with respect to drug design and discovery. therefore, using classical methods such as the protein bank database and quantum mechanical calculations are insufficient owing to the dimensionality of search spaces. In this paper, we introduce a hybrid metaheuristic algorithm called cHHo-cS, which combines Harris hawks optimizer (HHo) with two operators: cuckoo search (cS) and chaotic maps. the role of cS is to control the main position vectors of the HHo algorithm to maintain the balance between exploitation and exploration phases, while the chaotic maps are used to update the control energy parameters to avoid falling into local optimum and premature convergence. feature selection (fS) is a tool that permits to reduce the dimensionality of the dataset by removing redundant and non desired information, then fS is very helpful in cheminformatics. FS methods employ a classifier that permits to identify the best subset of features. the support vector machines (SVMs) are then used by the proposed cHHo-cS as an objective function for the classification process in FS. The CHHO-CS-SVM is tested in the selection of appropriate chemical descriptors and compound activities. Various datasets are used to validate the efficiency of the proposed CHHO-CS-SVM approach including ten from the UCI machine learning repository. Additionally, two chemical datasets (i.e., quantitative structure-activity relation biodegradation and monoamine oxidase) were utilized for selecting the most significant chemical descriptors and chemical compounds activities. the extensive experimental and statistical analyses exhibit that the suggested CHHO-CS method accomplished much-preferred trade-off solutions over the competitor algorithms including the HHO, CS, particle swarm optimization, moth-flame optimization, grey wolf optimizer, Salp swarm algorithm, and sine-cosine algorithm surfaced in the literature. the experimental results proved that the complexity associated with cheminformatics can be handled using chaotic maps and hybridizing the meta-heuristic methods. The prediction and analysis of molecules are essential tasks in cheminformatics, which use methods from mathematics and computer science to enhance their performance. The implementation of these methods depends on databases. The processes that generate most of the affectations are the storage and retrieval of molecular structures and properties (e.g., pharmacogenomics data). Typically, the behavior of the compounds can be investigated using molecular analysis. The molecular analysis helps to develop and test molecules for decreasing the effects of specific diseases 1. One drawback associated with cheminformatics is the exponential increment of the search space owing to features in the dataset 2. However, cheminformatics is still being widely used in drug design, where ...

show abstract

“…Some of the features include characterization and topological indices. Therefore, molecular descriptors are highly important in pharmaceutical sciences and chemistry 4 .…”

Section: Methodsmentioning

confidence: 99%