De novo design and optimization of Aurora A kinase inhibitors

Rodrigues, Tiago; Roudnicky, Filip; Koch, Christian P.; Kudoh, Takayuki; Reker, Daniel; Detmar, Michael; Schneider, Gisbert

doi:10.1039/c2sc21842a

Cited by 26 publications

(19 citation statements)

References 32 publications

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“…However, its development was abandoned during clinical trials because of QT interval prolongation. Rodrigues et al [27] applied the ligand-based de novo design program DOGS (Design Of Genuine Structures [28] )i na na ttempt to identify an ew chemical scaffold that would share the same activity but without the cardiac liability.T he intellectual property (IP) space around this area is quite tight so this represented apotentially challenging scaffold hop.…”

Section: Aurora Kinase Ainhibitorsmentioning

confidence: 99%

Automated De Novo Drug Design: Are We Nearly There Yet?

2019

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Medicinal chemistry and, in particular, drug design have often been perceived as more of an art than a science. The many unknowns of human disease and the sheer complexity of chemical space render decision making in medicinal chemistry exceptionally demanding. Computational models can assist the medicinal chemist in this endeavour. Provided here is an overview of recent examples of automated de novo molecular design, a discussion of the concepts and computational approaches involved, and the daring prediction of some of the possibilities and limitations of drug design using machine intelligence.

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Section: Aurora Kinase Ainhibitorsmentioning

confidence: 99%

Automated De Novo Drug Design: Are We Nearly There Yet?

2019

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“…Although receptor-based design operates on a model of a macromolecular binding site, ligand-based methods are either explicitly or implicitly based on the chemical similarity principle (2) without requiring a receptor model (3). Instead, the latter typically uses some measure of chemical or pharmacophore feature similarity to a reference ligand as a fitness function, which aims to generate NCEs as template mimetics via scaffold hopping (4)(5)(6)(7)(8). We report the development, implementation, and successful prospective application of an innovative computational technique for the target profiling of de novo-designed molecules.…”

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confidence: 99%

Identifying the macromolecular targets of de novo-designed chemical entities through self-organizing map consensus

Reker

Rodrigues

Schneider

2014

Proc. Natl. Acad. Sci. U.S.A.

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De novo molecular design and in silico prediction of polypharmacological profiles are emerging research topics that will profoundly affect the future of drug discovery and chemical biology. The goal is to identify the macromolecular targets of new chemical agents. Although several computational tools for predicting such targets are publicly available, none of these methods was explicitly designed to predict target engagement by de novo-designed molecules. Here we present the development and practical application of a unique technique, self-organizing map-based prediction of drug equivalence relationships (SPiDER), that merges the concepts of self-organizing maps, consensus scoring, and statistical analysis to successfully identify targets for both known drugs and computer-generated molecular scaffolds. We discovered a potential off-target liability of fenofibraterelated compounds, and in a comprehensive prospective application, we identified a multitarget-modulating profile of de novo designed molecules. These results demonstrate that SPiDER may be used to identify innovative compounds in chemical biology and in the early stages of drug discovery, and help investigate the potential side effects of drugs and their repurposing options.drug design | target prediction | polypharmacology | machine learning | chemical similarity C omputer-assisted de novo molecular design has evolved as a popular source of ideas to combat the perceived lack of new chemical entities (NCEs) in chemical biology and drug discovery (1). We demonstrate that automated de novo design delivers readily synthesizable NCEs with desirable activity profiles. Although receptor-based design operates on a model of a macromolecular binding site, ligand-based methods are either explicitly or implicitly based on the chemical similarity principle (2) without requiring a receptor model (3). Instead, the latter typically uses some measure of chemical or pharmacophore feature similarity to a reference ligand as a fitness function, which aims to generate NCEs as template mimetics via scaffold hopping (4-8). We report the development, implementation, and successful prospective application of an innovative computational technique for the target profiling of de novo-designed molecules. The approach combines the concepts of self-organizing maps (SOMs) (9) for macromolecular target prediction (10), consensus scoring (11), and a statistical evaluation that provides confidence estimates for the predictions.Predicting polypharmacological activities is a topic relevant to chemical biology and drug discovery, not only to take advantage of inherent drug promiscuity but also to decrease lead compound attrition caused by unfavorable off-target modulation

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“…The last one is with Thr217 which is a nonconserved residue between Aurora‐A and ‐B and reported to be responsible for selectivity 62. VH4 has similarity by sulfonyl amide group with Compound 2b, 4a, 4b 41. Though there are similar compounds to our hits, we identified several other promising hits which can be potential drug candidate.…”

Section: Resultsmentioning

confidence: 64%

Identification of Aurora‐A Inhibitors by Ligand and Structure‐Based Virtual Screening

Morshed

2014

Molecular Informatics

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Aurora kinase A has been identified as one of the most attractive targets for cancer therapy because of its critical role in the regulation of the cell cycle. In order to identify active compounds with structural diversity we performed virtual screening. 3D-QSAR pharmacophore models were developed and the best model was used as a query for screening the databases. Ligand and structure-based virtual screening protocol was conducted sequentially by applying the common feature pharmacophore and molecular docking to discover potent Aurora-A inhibitors. A total of eighty-eight compounds were selected for the in vitro activities against various human cancer cell lines (DU145 and HT29). Considering the activity data, we have identified seven compounds to be considered for the next step, among which four compounds had high inhibition rate (above 50 %) at 10 µM with GI50 lower than 10 µM. Based on the cell line and enzyme assay (Aurora-A & B) result, these four compounds were used as template/query molecule for similarity search. The best result was obtained for similarity hit SH3. It had IC50 of 0.578 and 11.77 µM for Aurora-A and B respectively, which implies 20-fold selectivity over Aurora-B. The hits obtained from this screening scheme could be potential drug candidates after further optimization.

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De novo design and optimization of Aurora A kinase inhibitors

Cited by 26 publications

References 32 publications

Automated De Novo Drug Design: Are We Nearly There Yet?

Automated De Novo Drug Design: Are We Nearly There Yet?

Identifying the macromolecular targets of de novo-designed chemical entities through self-organizing map consensus

Identification of Aurora‐A Inhibitors by Ligand and Structure‐Based Virtual Screening

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