ArticleJournal of molecular modeling2003
Markovian chemicals "in silico" design (MARCH-INSIDE), a promising approach for computer-aided molecular design I: discovery of anticancer compounds.
Article in Journal of molecular modeling, 2003. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed, 86 citations in OpenAlex.
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- Perturbation-Theory Machine Learning (PTML) Multilabel Model of the ChEMBL Dataset of Preclinical Assays for Antisarcoma Compounds.ACS omega · 2020Article
- Tensor Algebra-based Geometrical (3D) Biomacro-Molecular Descriptors for Protein Research: Theory, Applications and Comparison with other Methods.Scientific reports · 2019Article
- A web server for analysis, comparison and prediction of protein ligand binding sites.Biology direct · 2016Article
- Prediction of anticancer molecules using hybrid model developed on molecules screened against NCI-60 cancer cell lines.BMC cancer · 2016Article
- A survey of quantitative descriptions of molecular structure.Current topics in medicinal chemistry · 2012Article
- Predicting monoamine oxidase inhibitory activity through ligand-based models.Current topics in medicinal chemistry · 2012Review
- Scoring function for DNA-drug docking of anticancer and antiparasitic compounds based on spectral moments of 2D lattice graphs for molecular dynamics trajectories.European journal of medicinal chemistry · 2009Article
- Databases and QSAR for cancer research.Cancer informatics · 2007Article
- Markovian chemicals "in silico" design (MARCH-INSIDE), a promising approach for computer-aided molecular design III: 2.5D indices for the discovery of antibacterials.Journal of molecular modeling · 2005Article
- Bond-based global and local (bond, group and bond-type) quadratic indices and their applications to computer-aided molecular design. 1. QSPR studies of diverse sets of organic chemicals.Journal of computer-aided molecular designArticle
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Authors and funding
15 authors at 3 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
A simple stochastic approach, designed to model the movement of electrons throughout chemical bonds, is introduced. This model makes use of a Markov matrix to codify useful structural information in QSAR. The self-return probabilities of this matrix throughout time ((SR)pi(k)) are then used as molecular descriptors. Firstly, a calculation of (SR)pi(k) is made for a large series of anticancer and non-anticancer chemicals. Then, k-Means Cluster Analysis allows us to split the data series into clusters and ensure a representative design of training and predicting series. Next, we develop a classification function through Linear Discriminant Analysis (LDA). This QSAR discriminates between anticancer compounds and non-active compounds with a correct global classification of 90.5% in the training series. The model also correctly classified 86.07% of the compounds in the predicting series. This classification function is then used to perform a virtual screening of a combinatorial library of coumarins. In this connection, the biological assay of some furocoumarins, selected by virtual screening using the present model, gives good results. In particular, a tetracyclic derivative of 5-methoxypsoralen (5-MOP) has an IC50 against HL-60 tumoral line around 6 to 10 times lower than those for 8-MOP and 5-MOP (reference drugs), respectively. Finally, application of Iso-contribution Zone Analysis (IZA) provides structural interpretation of the biological activity predicted with this QSAR.
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