REACH and in silico methods: an attractive opportunity for medicinal chemists

Nicolotti, Orazio; Benfenati, Emilio; Carotti, Angelo; Gadaleta, Domenico; Gissi, Andrea; Mangiatordi, Giuseppe Felice; Novellino, Ettore

doi:10.1016/j.drudis.2014.06.027

Cited by 71 publications

(40 citation statements)

References 44 publications

(42 reference statements)

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“…the structure-based alignment and the docking-based alignment have been employed to actualize this goal. Undeniably, Comparative Molecular Field Analysis (CoMFA) is a popular 3D-QSAR approach that utilizes statistical correlation techniques for the analysis of the quantitative relationship between the biological activity of a set of chemicals with a special alignment, and their three-dimensional electronic and steric properties (Verma et al, 2010;Cherkasov et al, 2014;Nicolotti et al, 2014). And the other 3D-QSAR approach is Comparative Molecular Similarity Indices Analysis (CoMSIA), which insinuates moving from field descriptors based on well-established and commonly established potentials (Lennard-Jones and Coulomb potentials) to some arbitrary descriptors considering the spatial similarity or dissimilarity of ligands (Klebe, 1998;Doucet and Panaye, 2010).…”

Section: D-qsar Studiesmentioning

confidence: 99%

Biomolecular recognition of antagonists by α7 nicotinic acetylcholine receptor: Antagonistic mechanism and structure–activity relationships studies

Peng

Ding

2015

European Journal of Pharmaceutical Sciences

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Section: D-qsar Studiesmentioning

confidence: 99%

Biomolecular recognition of antagonists by α7 nicotinic acetylcholine receptor: Antagonistic mechanism and structure–activity relationships studies

Peng

Ding

2015

European Journal of Pharmaceutical Sciences

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“…Given the increasing applicability of chemogenomics to uncover untested ligand-target pairs, many different exciting applications come to mind. For example, environmental agencies may consider applying computational chemogenomics as a way to generate hypotheses about the effect of pollutants generated during manufacturing processes [89]. In another application, deorphanization of natural products used in cancer therapy [90,91] can provide the starting CPIs to initiate an actively learned chemogenomic model which generates testable hypotheses of new drug-target interactions in uncharacterized drugs for specific cancer cell lines, such that the results of tested hypotheses are fed back into the model for subsequent hypothesis generation.…”

Section: Implications and Future Directionsmentioning

confidence: 99%

Active Learning for Computational Chemogenomics

et al. 2017

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Aim: Computational chemogenomics models the compound–protein interaction space, typically for drug discovery, where existing methods predominantly either incorporate increasing numbers of bioactivity samples or focus on specific subfamilies of proteins and ligands. As an alternative to modeling entire large datasets at once, active learning adaptively incorporates a minimum of informative examples for modeling, yielding compact but high quality models. Results/methodology: We assessed active learning for protein/target family-wide chemogenomic modeling by replicate experiment. Results demonstrate that small yet highly predictive models can be extracted from only 10–25% of large bioactivity datasets, irrespective of molecule descriptors used. Conclusion: Chemogenomic active learning identifies small subsets of ligand–target interactions in a large screening database that lead to knowledge discovery and highly predictive models.

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“…Such a methodology, today applied also for regulatory purposes 32 , is often based on the application of a multivariate equation that relates molecular features to the analyzed activity 33 . Herein, the multi-regression analyses (MRA) were used in the QSAR model derivation.…”

Section: D-qsar Studies: Descriptors Regression Analysis and Statismentioning

confidence: 99%

Studies on the antiplatelet and antithrombotic profile of anti-inflammatory coumarin derivatives

Kontogiorgis

Nicolotti

Mangiatordi

et al. 2015

Journal of Enzyme Inhibition and Medicinal Chemistry

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The interest towards coumarin-based structures stems from their polypharmacological profile.Herein, we present a series of Mannich bases and 7-azomethine-linked coumarin derivatives exhibiting antiplatelet and antithrombotic activities, in addition to the already known antiinflammatory and antioxidant activities. Among others, compounds 15 and 16 were found to be the most potent and selective inhibitors of platelet aggregation whereas compound 3 also proved to be the most potent in the clot retraction assay. Structure-activity relationship studies were conducted to elucidate the molecular determinants responsible for the herein observed activities. The chance of inhibiting cyclooxygenase-1 was also investigated for evaluating the platelet aggregation induced by arachidonic acid. Taken together, these results suggest that the investigation of other targets connected to the antiplatelet activity, such as phosphodiesterase-3 (PDE3), could be a viable strategy to shed light on the polypharmacological profile of coumarin-based compounds. Docking simulations towards PDE3 were also carried out.

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REACH and in silico methods: an attractive opportunity for medicinal chemists

Cited by 71 publications

References 44 publications

Biomolecular recognition of antagonists by α7 nicotinic acetylcholine receptor: Antagonistic mechanism and structure–activity relationships studies

Biomolecular recognition of antagonists by α7 nicotinic acetylcholine receptor: Antagonistic mechanism and structure–activity relationships studies

Active Learning for Computational Chemogenomics

Studies on the antiplatelet and antithrombotic profile of anti-inflammatory coumarin derivatives

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