Dimensionality of big data sets explored by Cluj descriptors

Lungu, Claudiu N.; Ersali, Sara; Szefler, Beata; Pîrvan-Moldovan, Atena; Basak, Subhash C.; Diudea, Mircea V.

doi:10.24193/subbchem.2017.3.16

Cited by 8 publications

(11 citation statements)

References 5 publications

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“…Thus, the following studies [206][207][208][209][210][211][212][213][214][215][216][217][218][219][220] set out a new trend in scientific research "drug delivery", which consists of three thematic components:…”

Section: Drug Delivery Nanostructuresmentioning

confidence: 99%

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Nanotechnology, from quantum mechanical calculations up to drug delivery

Szefler¹

2018

IJN

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There are several reasons why nanotechnology is currently considered as the leader among the most intensively developing research trends. Nanomatter often exhibits new properties, other than those of the morphology of a continuous solid. Also, new phenomena appear at the nanoscale, which are unknown in the case of microcrystalline objects. For this reason, nanomaterials have already found numerous applications that are described in this review. However, among intensively developed various branches of nanotechnology, nanomedicine and pharmacology stand out particularly, which opens new possibilities for the development of these disciplines, gives great hope for the creation of new drugs in which toxicological properties are reduced to a minimum, reduces the doses of medicines, offers targeted treatment and increases diagnostic possibilities. Nanotechnology is the source of a great revolution in medicine. It gives great hope for better and faster treatment of many diseases and gives hope for a better tomorrow. However, the creation of new “nanodrugs” requires a special understanding of the properties of nanoparticles. This article is a review work which determines and describes the way of creating new nanodrugs from ab initio calculations by docking and molecular dynamic applications up to a new medicinal product, as a proposal for the personalized medicine, in the early future.

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Section: Drug Delivery Nanostructuresmentioning

confidence: 99%

“…• the properties of fullerenes described above, [8][9][10][11][12][13][14][15][16][17][18][19][20]206 • design, [206][207][208][209][210][212][213][214] • docking of drugs and molecular dynamics. [215][216][217][218][219]…”

Section: Drug Delivery Nanostructuresmentioning

confidence: 99%

Nanotechnology, from quantum mechanical calculations up to drug delivery

Szefler¹

2018

IJN

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“…Proteins with low internal stability are readily adsorbed on surfaces of all types, even though electrostatic repulsions exist; however, intrinsically stable proteins undergo substantial adsorption on hydrophilic surfaces only in the presence of electrostatic interactions. In general, the stronger the bond with the surface of nanomaterial large molecules, the broader the contact surface and flexible structure, with high shape complementarity of the adsorption surface [26][27][28].…”

Section: Discussionmentioning

confidence: 99%

Protein Interaction with Dendrimer Monolayers: Energy and Surface Topology

Lungu

Grudziński

Olteanu³

et al. 2020

Symmetry

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Protein interaction with polymers layers is a keystone in designing bio-nano devices. Polyamidoamines (PAMAMs) are well-known polymers. Zero aromatic core dendrimers (ZAC) are molecules with no proven toxic effect in cultured cells. When coating nanodevices with enzymatic systems, active sites are disturbed by an interaction with the biosystem surface. Computational methods were used in order to simulate, characterize, and quantify protein-polymer interaction. Protein corona, i.e., surface proteins disposed on a viral membrane or nanodevice outer surface, are crucial in interactions with a potential pharmacological target or receptor. Corona symmetry has been observed in the Middle East respiratory syndrome-related coronavirus (MERS-CoV), severe acute respiratory syndrome coronavirus (SARS-CoV), and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). As a protein alpha 1 antitrypsin's a crystallographic structure was chosen. Protein-mono dendrimer layer systems were generated using in silico methods in order to simulate their interaction. Interactions were quantified using topological and quantum mechanical strategies. Results showed that PAMAM and ZAC interact differently with alpha 1 antitrypsin. Energy and topological surfaces of protein vary accordingly with the dendrimer monolayer. Topological surfaces have a higher sensibility in describing the interactions.

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“…High dimensional QSAR spaces described by computed molecular descriptors and machine learning methods in model building have been explored by various authors . For the models developed from the set 2 of 578 descriptors calculated by TopoCluj and Schrodinger, PSA, sum of topological distances between O..O, E state topological parameter, ALOGP, and topological charge index of order 5 emerge as important variables.…”

Section: Discussionmentioning

confidence: 99%

“…We use two collections of molecular descriptors for our QSAR model building exercise. The first set of descriptors, calculated and utilized by the Cluj team of Diudea and collaborators were calculated by the softwares Schrodinger and TopoCluj . Detailed references about these descriptors are given in the Supplementary Table S2.…”

Section: Methodsmentioning

confidence: 99%

Finding Needles in a Haystack: Determining Key Molecular Descriptors Associated with the Blood‐brain Barrier Entry of Chemical Compounds Using Machine Learning

Majumdar

Basak

Lungu

et al. 2019

Molecular Informatics

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In this paper we used two sets of calculated molecular descriptors to predict blood-brain barrier (BBB) entry of a collection of 415 chemicals. The set of 579 descriptors were calculated by Schrodinger and TopoCluj software. Polly and Triplet software were used to calculate the second set of 198 descriptors. Following this, modelling and a two-deep, repeated external validation method was used for QSAR formulation. Results show that both sets of descriptors individually and their combination give models of reasonable prediction accuracy. We also uncover the effectiveness of a variable selection approach, by showing that for one of our descriptor sets, the top 5 % predictors in terms of random forest variable importance are able to provide a better performing model than the model with all predictors. The top influential descriptors indicate important aspects of molecular structural features that govern BBB entry of chemicals.Keywords: blood-brain barrier · molecular descriptors · variable selection · machine learning · random forest · two-deep cross validation · quantitative structure-activity relationship (QSAR)[a] S. Majumdar

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Dimensionality of big data sets explored by Cluj descriptors

Cited by 8 publications

References 5 publications

Nanotechnology, from quantum mechanical calculations up to drug delivery

Nanotechnology, from quantum mechanical calculations up to drug delivery

Protein Interaction with Dendrimer Monolayers: Energy and Surface Topology

Finding Needles in a Haystack: Determining Key Molecular Descriptors Associated with the Blood‐brain Barrier Entry of Chemical Compounds Using Machine Learning

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