Successful “In Silico” Design of New Efficient Uranyl Binders

Varnek, Alexandre; Fourches, Denis; Solov'ev, Vitaly P.; Klimchuk, Olga; Ouadi, Ali; Billard, Isabelle

doi:10.1080/07366290701415820

Cited by 27 publications

(29 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Fragment descriptors, representing counts of all the present atoms-and-bonds sequences of at least three and at most six atoms, were generated with the ISIDA [24,25] software. They will be further on labeled as 'F3-6'.…”

Section: Descriptor Space Definitionmentioning

confidence: 99%

Local neighborhood behavior in a combinatorial library context

Horvath

Koch²,

Schneider³

et al. 2011

J Comput Aided Mol Des

View full text Add to dashboard Cite

This article revisits a particular aspect of the molecular similarity principle-the Neighborhood Behavior (NB) concept. Earlier, the NB optimality criterion was introduced to select descriptor spaces, combining a given descriptor set and a similarity metric, which optimally comply with the similarity principle. Here, we focus on a "local" analysis based on the neighborhood of individual bioactive compounds. The defined NB-score measures similarity-based virtual screening success when using individual actives as queries. Systematic studies of local NB have been performed on a large combinatorial library of compounds with reported IC (50) values for five proteases, involving more than 140 descriptor/metric combinations of various fragment- and pharmacophore-based descriptors and different similarity metrics. Although, for each descriptor/metrics combination, the NB-score heavily depends on the query compound, on the average, 2D pharmacophore-based descriptors outperformed their 3D counterparts.

show abstract

Section: Descriptor Space Definitionmentioning

confidence: 99%

Local neighborhood behavior in a combinatorial library context

Horvath

Koch²,

Schneider³

et al. 2011

J Comput Aided Mol Des

View full text Add to dashboard Cite

show abstract

“…These are based on a wide range of fragmentation schemes, ranging from detection of predefined groups (MACCS [23] keys, DayLight fingerprints [24] ) to open-ended detection of all fragments of a specified type (pairs, [25] linear or branched fragments of specified minimal and maximal sizes). The latter include the ISIDA fragment counts [26][27][28][29][30] which feature sequences and ramified substructures, called augmented atoms, including or not bond type information, according to the users' needs.…”

Section: Introductionmentioning

confidence: 99%

ISIDA Property‐Labelled Fragment Descriptors

Ruggiu

Marcou

Varnek

et al. 2010

Molecular Informatics

124

145

View full text Add to dashboard Cite

ISIDA Property-Labelled Fragment Descriptors (IPLF) were introduced as a general framework to numerically encode molecular structures in chemoinformatics, as counts of specific subgraphs in which atom vertices are coloured with respect to some local property/feature. Combining various colouring strategies of the molecular graph - notably pH-dependent pharmacophore and electrostatic potential-based flagging - with several fragmentation schemes, the different subtypes of IPLFs may range from classical atom pair and sequence counts, to monitoring population levels of branched fragments or feature multiplets. The pH-dependent feature flagging, pursued at the level of each significantly populated microspecies involved in the proteolytic equilibrium, may furthermore add some competitive advantage over classical descriptors, even when the chosen fragmentation scheme is one of the state-of-the-art pattern extraction procedures (feature sequence or pair counts, etc.) in chemoinformatics. The implemented fragmentation schemes support counting (1) linear feature sequences, (2) feature pairs, (3) circular feature fragments a.k.a. "augmented atoms" or (4) feature trees. Fuzzy rendering - optionally allowing nonterminal fragment atoms to be counted as wildcards, ignoring their specific colours/features - ensures for a seamless transition between the "strict" counts (sequences or circular fragments) and the "fuzzy" multiplet counts (pairs or trees). Also, bond information may be represented or ignored, thus leaving the user a vast choice in terms of the level of resolution at which chemical information should be extracted into the descriptors. Selected IPLF subsets were - tree descriptors, in particular - successfully tested in both neighbourhood behaviour and QSAR modelling challenges, with very promising results. They showed excellent results in similarity-based virtual screening for analogue protease inhibitors, and generated highly predictive octanol-water partition coefficient and hERG channel inhibition models.

show abstract

“…It has been demonstrated that prediction performance of the models built on SMF is, at least, as good as that for the models involving molecular descriptors of different types, [48][49][50] E-state counts and E-state indices [40] and pharmacophore descriptors. [51] In our studies for alkaline-earth [33] and some heavy metal ions, [43] the root-mean squared error (RMSE) of predictions is comparable with experimental systematic errors.…”

Section: Introductionmentioning

confidence: 99%

New Approach for Accurate QSPR Modeling of Metal Complexation: Application to Stability Constants of Complexes of Lanthanide Ions Ln3+, Ag+, Zn2+, Cd2+ and Hg2+ with Organic Ligands in Water

Соловьев¹,

Tsivadze²,

Varnek³

2012

MHC

View full text Add to dashboard Cite

In this paper, we propose a new definition of models applicability domain (AD) based on the selection of sufficient portion of individual QSPR models to be accepted for property prediction. Efficiency of this approach has been demonstrated in ensemble modeling of the stability constants logK of the 1:1 complexes of 17 lanthanide and transition metal ions (M) with various organic ligands (L) ) metal ions with sets of diverse organic molecules in aqueous solution at 298 K and an ionic strength 0.1 M. The models have been validated by external 5-fold crossvalidation procedure. The root mean squared error (RMSE) of predictions is similar to systematic errors in experimental data. This is twice smaller compared to earlier reported models for which "quorum control" AD has not been applied. Methods Data SetsThe experimental stability constant (logK) ) metal ions with diverse organic ligands in water were selected from the IUPAC Stability Constants Database (SC DB) (version 5.33, Academic Software) [15] at standard temperature 298 K and an ionic strength I = 0.1 M. Some logK values (around 15 %) were corrected to specified temperature and an ionic strength using the procedures included in SC DB.2D structures of ligands, names of metal ions and corresponding logK values resulted from searching in SC DB were converted into Structure -Data Files (SDF) served as an input in the MLR module of the ISIDA (In Silico Design and Data Analysis) /QSPR program.[53] The data manager EdiSDF [35,46,54] was used to prepare data sets containing finally from 52 (Hg

show abstract

Successful “In Silico” Design of New Efficient Uranyl Binders

Cited by 27 publications

References 21 publications

Local neighborhood behavior in a combinatorial library context

Local neighborhood behavior in a combinatorial library context

ISIDA Property‐Labelled Fragment Descriptors

New Approach for Accurate QSPR Modeling of Metal Complexation: Application to Stability Constants of Complexes of Lanthanide Ions Ln3+, Ag+, Zn2+, Cd2+ and Hg2+ with Organic Ligands in Water

Contact Info

Product

Resources

About