2010
DOI: 10.1007/s10822-010-9381-1
|View full text |Cite
|
Sign up to set email alerts
|

Trainable structure–activity relationship model for virtual screening of CYP3A4 inhibition

Abstract: A new structure-activity relationship model predicting the probability for a compound to inhibit human cytochrome P450 3A4 has been developed using data for >800 compounds from various literature sources and tested on PubChem screening data. Novel GALAS (Global, Adjusted Locally According to Similarity) modeling methodology has been used, which is a combination of baseline global QSAR model and local similarity based corrections. GALAS modeling method allows forecasting the reliability of prediction thus defin… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1
1
1
1

Citation Types

4
26
0

Year Published

2012
2012
2024
2024

Publication Types

Select...
4
3
2

Relationship

0
9

Authors

Journals

citations
Cited by 32 publications
(30 citation statements)
references
References 39 publications
(74 reference statements)
4
26
0
Order By: Relevance
“…Owing to recent structural and computer modeling advances, QSAR methodology was utilized in combination with the multiple pharmacophore hypothesis approach [112], advanced docking techniques (MetSite, GLUE, AutoDock and other) [113116], GALAS (Global Adjusted Locally According to Similarity) method [117, 118], structure-based comparative molecular field analysis [119], and NMR spectroscopy data [120]. Gaussian kernel weighted k -nearest neighbor models were also used for in silico prediction of CYP3A4 inhibitors [121].…”
Section: 3 Computational Approachesmentioning
confidence: 99%
“…Owing to recent structural and computer modeling advances, QSAR methodology was utilized in combination with the multiple pharmacophore hypothesis approach [112], advanced docking techniques (MetSite, GLUE, AutoDock and other) [113116], GALAS (Global Adjusted Locally According to Similarity) method [117, 118], structure-based comparative molecular field analysis [119], and NMR spectroscopy data [120]. Gaussian kernel weighted k -nearest neighbor models were also used for in silico prediction of CYP3A4 inhibitors [121].…”
Section: 3 Computational Approachesmentioning
confidence: 99%
“…For example, QSAR studies by Lewis et al (2006) showed that a linear relationship existed between lipophilicity and potency for 15 inhibitors/substrates of CYP2C9, including nonsteroidal anti-inflammatory drugs. Modeling by Didziapetris et al (2010) demonstrated that an increase in the size of the molecule with the incorporation of hydrophobic aliphatic or aromatic residues resulted in a higher probability for the compound to inhibit CYP3A4. Roy and Pratim Roy (2009) also demonstrated that logP appeared to be the most important factor affecting the inhibition potency of CYP3A4 inhibitors.…”
Section: Consideration Of Clinically Relevant Levels and Inhibition Pmentioning
confidence: 99%
“…All points considered, metabolites are, in general, unlikely to be more potent P450 inhibitors than their respective parent drugs. Several published quantitative structure-activity relationship models evaluating reversible inhibition of CYP2C and CYP3A families also supported the positive correlation between lipophilicity (logP) and potency for enzyme inhibition (Lewis et al, 2006;Didziapetris et al, 2010). In addition, empirical observations indicate that metabolites are likely to have affinity for the same binding sites as the parent (e.g., binding to the pharmacological target of the parent leading to "active metabolites") and if a metabolite has any affinity for P450 binding sites, the binding pattern tends to be very similar to the parent (Humphreys and Unger, 2006).…”
Section: Risk Assessment Of Contribution Of Metabolites To P450mentioning
confidence: 72%