Discriminating between Drugs and Nondrugs by Prediction of Activity Spectra for Substances (PASS)

Anzali, Soheila; Barnickel, Gerhard; Cezanne, Bertram; Krug, Michael; Филимонов, Д. А.; Poroikov, Vladimir

doi:10.1021/jm0010670

Cited by 133 publications

(94 citation statements)

References 16 publications

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“…Substructural analysis was studied in considerable detail by workers at the National Institutes of Health in an extended programme to develop novel anti-cancer agents [47][48][49], and also by workers at Lederle [29] and Sheffield [50][51][52]. However, it is only in the last few years that this general approach has become widely used [53][54][55][56][57][58][59][60][61][62].…”

Section: Substructural Analysis Naive Bayesian Classifiers and Groupmentioning

confidence: 99%

Evaluation of machine-learning methods for ligand-based virtual screening

Chen

Harrison

Papadatos

et al. 2007

J Comput Aided Mol Des

113

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Abstract. Machine-learning methods can be used for virtual screening by analysing the structural characteristics of molecules of known (in)activity, and we here discuss the use of kernel discrimination and naive Bayesian classifier methods for this purpose. We report a kernel method that allows the processing of molecules represented by binary, integer and real-valued descriptors, and show that it is little different in screening performance from a previously described kernel that had been developed specifically for the analysis of binary fingerprint representations of molecular structure. We then evaluate the performance of a naive Bayesian classifier when the training-set contains only a very few active molecules. In such cases, a simpler approach based on group fusion would appear to provide superior screening performance, especially when structurally heterogeneous datasets are to be processed.

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Section: Substructural Analysis Naive Bayesian Classifiers and Groupmentioning

confidence: 99%

Evaluation of machine-learning methods for ligand-based virtual screening

Chen

Harrison

Papadatos

et al. 2007

J Comput Aided Mol Des

113

View full text Add to dashboard Cite

show abstract

“…These compounds were then subjected to the analysis of ADMET properties using ADMET module of DS. From the obtained results, 12 of the total compounds that satisfied all the ADMET properties were selected and checked for the possible side effects and toxicity using Osiris Property Explorer (http://www.organicchemistry.org/prog/peo/) and PASS software [43,44]. The pharmacological properties of these compounds as desired for a drug molecule were predicted by Molinspiration (http:// www.molinspiration.com/cgi-bin/properties).…”

Section: Lead Screening and Toxicity Prediction Analysismentioning

confidence: 99%

Synthesis and in silico evaluation of 1N-methyl-1S-methyl-2-nitroethylene (NMSM) derivatives against Alzheimer disease: to understand their interacting mechanism with acetylcholinesterase

et al. 2012

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Anomalous action of human acetylcholinesterase (hAChE) in Alzheimer's disease (AD) was restrained by various AChE inhibitors, of which the specific and potent lead candidate Donepezil is used for treating the disease AD. Besides the specificity, the observed undesirable side effects caused by Donepezil invoked the quest for new lead molecules with the increased potency and specificity for AChE. The present study elucidates the potency of six 1N-methyl-1S-methyl-2-nitroethylene (NMSM) derivatives to form a specific interaction with the peripheral anionic site and catalytic anionic subsite residues of hAChE. The NMSMs were prepared in good yield from 1,1-di(methylsulfanyl)-2-nitroethylene and primary amine (or) amino acid esters. In silico interaction analysis reveals specific and potent interactions between hAChE and selected ligand molecules. The sitespecific interactions formed between these molecules also results in a conformational change in the orientation of active site residues of hAChE, which prevents them from being accessed by beta-amyloid protein (Aβ), which is a causative agent for amyloid plaque formation and acetylcholine (ACh). In silico interaction analysis between the ligand-bounded hAChE with Aß and ACh confirms this observation. The variation in the conformation of hAChE associated with the decreased ability of Aβ and ACh to access the respective functional residues of hAChE induced by the novel NMSMs favors their selection for in vivo analysis to present themselves as new members of hAChE inhibitors.

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“…A test-set molecule is then scored by summing (or otherwise combining) the weights of those bits that are set in its fingerprint, the resulting score representing the overall probability that the molecule will be active. Substructural analysis was studied in considerable detail by Hodes in a National Institutes of Health project to develop novel anti-cancer agents [7][8][9][10] , but it is only quite recently that the approach has become widely used [11][12][13][14][15] . An operational example of the use of substructural analysis is the PASS (for Prediction of Activity Spectra for Substances) system developed by the Poroikov group 11,16,17 .…”

Section: Introductionmentioning

confidence: 99%