A Comparative Analysis of Public Ligand Databases Based on Molecular Descriptors

Winck, Ana T.; Quevedo, Christian Vahl; Machado, Karina; Souza, Osmar Norberto de; Ruiz, Duncan D.

doi:10.1007/978-3-642-31927-3_14

Cited by 2 publications

(3 citation statements)

References 31 publications

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“…Besides the molecular docking tool, the VS strategy involves the use of ligand databases. As reviewed in [ 18 ] the most important public compounds database is ChemBank [ 19 ], ChemDB [ 20 ], NCI Database [ 21 ], PubChem [ 22 ], and ZINC [ 4 ]. In this work we choose to use the ZINC database since it is a free database of commercially available compounds that contains over 22 million purchasable compounds ready for molecular docking.…”

Section: Methodsmentioning

confidence: 99%

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An Infrastructure to Mine Molecular Descriptors for Ligand Selection on Virtual Screening

Seus

Perazzo

Winck

et al. 2014

BioMed Research International

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The receptor-ligand interaction evaluation is one important step in rational drug design. The databases that provide the structures of the ligands are growing on a daily basis. This makes it impossible to test all the ligands for a target receptor. Hence, a ligand selection before testing the ligands is needed. One possible approach is to evaluate a set of molecular descriptors. With the aim of describing the characteristics of promising compounds for a specific receptor we introduce a data warehouse-based infrastructure to mine molecular descriptors for virtual screening (VS). We performed experiments that consider as target the receptor HIV-1 protease and different compounds for this protein. A set of 9 molecular descriptors are taken as the predictive attributes and the free energy of binding is taken as a target attribute. By applying the J48 algorithm over the data we obtain decision tree models that achieved up to 84% of accuracy. The models indicate which molecular descriptors and their respective values are relevant to influence good FEB results. Using their rules we performed ligand selection on ZINC database. Our results show important reduction in ligands selection to be applied in VS experiments; for instance, the best selection model picked only 0.21% of the total amount of drug-like ligands.

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Section: Methodsmentioning

confidence: 99%

“…In [ 18 ] it is possible to find a comparison among the public ligand databases. A set of 10 features were evaluated for each of the public ligand databases mentioned before, with the aim identifying which of them is the most comprehensive and suitable for VS.…”

Section: Methodsmentioning

confidence: 99%

An Infrastructure to Mine Molecular Descriptors for Ligand Selection on Virtual Screening

Seus

Perazzo

Winck

et al. 2014

BioMed Research International

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“…In this work we choose to use the ZINC [3] database since it is a free database of commercially-available compounds that contains over 22 million purchasable compounds ready to molecular docking. As revised in [9], Zinc appears as the most suitable public database for virtual screening.…”

Section: Virtual Screening and Molecu-lar Dockingmentioning

confidence: 99%

A data warehouse as an infrastructure to mine molecular descriptors for virtual screening

Perazzo

Winck

Machado

2013

Proceedings of the 28th Annual ACM Symposium on Applied Computing

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In a Rational Drug Design (RDD) one important step is the receptor-ligand interaction evaluation through molecular docking simulations. How it is a way impossible to test all available compounds for a target receptor, there is a need to select the most promising. One possible approach for such selection is to consider characteristics like a set of molecular properties called molecular descriptors. Aiming at describing these characteristics, we introduce a Data Warehouse (DW) model that integrates molecular descriptors from different public databases of compounds, as well as relates them with Virtual Screening (VS) experiments data. With the proposed DW we are able to produce proper data sets for classification mining experiments. We performed a case study with a VS considering as receptor the HIV-1 Protease receptor and 76 compounds. The data sets produced from our DW are composed by 7 molecular descriptors as the predictive attributes, and as a target attribute the discretized Free Energy of Binding (FEB) value between the ligands and the target receptor. By performing C4.5 algorithm over the generated data sets, we got decision-trees models that indicates which molecular descriptors and their respective values are relevant to influence on good FEB results.

show abstract

A Comparative Analysis of Public Ligand Databases Based on Molecular Descriptors

Cited by 2 publications

References 31 publications

An Infrastructure to Mine Molecular Descriptors for Ligand Selection on Virtual Screening

An Infrastructure to Mine Molecular Descriptors for Ligand Selection on Virtual Screening

A data warehouse as an infrastructure to mine molecular descriptors for virtual screening

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