Expanding the fragrance chemical space for virtual screening

Ruddigkeit, Lars; Awale, Mahendra; Reymond, Jean‐Louis

doi:10.1186/1758-2946-6-27

Cited by 39 publications

(39 citation statements)

References 60 publications

(63 reference statements)

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“…Several recent in silico studies have effectively used virtual screening approach to discover novel lead structures Ruddigkeit et al 2014). In our study, virtual screening was carried out against tACE (Fig.…”

Section: Virtual Screeningmentioning

confidence: 99%

Identification of Angiotensin Converting Enzyme Inhibitor: An In Silico Perspective

Jalkute

Barage

Dhanavade

et al. 2014

Int J Pept Res Ther

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Angiotensin Converting Enzyme (ACE) regulates blood pressure and electrolyte balance by converting angiotensin I into angiotensin II, which acts as vasoconstrictor and aldosterone-stimulating peptide. ACE is also known to inactivate vasodilators, bradykinin and angiotensin 1-7 peptide. Thus, the down-regulation of ACE activity would be very beneficial in various cardiovascular diseases. In present in silico approach, virtual screening was performed to identify possible novel inhibitors of testis ACE (tACE) from ZINC database using Dockblaster. All screened compounds were filtered through Lipinski's rules and other properties. PyRx tool was then implemented to dock selected compounds. Finally, ligand ZINC48251687 re-docked using Autodock 4.2 with His 383, His 387 and Glu 411 as flexible residues of tACE. Molecular Dynamics simulation was then carried out to investigate the binding stability of the screened ligand in a dynamic environment. It has been observed that tACE-ligand complex was quite stable over the entire simulation run. We found that ligand was stabilized by strong hydrogen bonding interactions with Ala 354, Ala 356, Tyr 523 and Zn 2? of tACE. Thus, ZINC48251687 could be used as starting point for the development of new non-peptidic inhibitor of tACE and for designing drug against cardiovascular and related renal diseases.

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Section: Virtual Screeningmentioning

confidence: 99%

Identification of Angiotensin Converting Enzyme Inhibitor: An In Silico Perspective

Jalkute

Barage

Dhanavade

et al. 2014

Int J Pept Res Ther

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“…The remaining organic SMILES were de-duplicated to produce a set of unique SMILES. From this dataset, we extracted a representative set of 225k fragment-sized molecules typically explored in the pharmaceutical and olfactive industries [6,27]. Prior to training, the SMILES were either converted to the canonical form or augmented as detailed in the results.…”

Section: Preparation Of Datasets and Encodingmentioning

confidence: 99%

GEN: highly efficient SMILES explorer using autodidactic generative examination networks

et al. 2020

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Recurrent neural networks have been widely used to generate millions of de novo molecules in defined chemical spaces. Reported deep generative models are exclusively based on LSTM and/or GRU units and frequently trained using canonical SMILES. In this study, we introduce Generative Examination Networks (GEN) as a new approach to train deep generative networks for SMILES generation. In our GENs, we have used an architecture based on multiple concatenated bidirectional RNN units to enhance the validity of generated SMILES. GENs autonomously learn the target space in a few epochs and are stopped early using an independent online examination mechanism, measuring the quality of the generated set. Herein we have used online statistical quality control (SQC) on the percentage of valid molecular SMILES as examination measure to select the earliest available stable model weights. Very high levels of valid SMILES (95-98%) can be generated using multiple parallel encoding layers in combination with SMILES augmentation using unrestricted SMILES randomization. Our trained models combine an excellent novelty rate (85-90%) while generating SMILES with strong conservation of the property space (95-99%). In GENs, both the generative network and the examination mechanism are open to other architectures and quality criteria.

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“…It could help to detect chemical entities with novel chemical scaffolds and physicochemical properties ( e.g ., for compound library design), to compare different libraries or to identify regions of chemical space that possess certain pharmacological profile . Exemplary approaches such as principle component analysis (PCA), Generative Topographic Mapping (GTM), Kohonen networks, Diffusion Maps, and interactive maps obtained by projection of high‐dimensional descriptor spaces,, are promising techniques in this context. Such visualization methods can be also used to interpret structure‐activity relationships .…”

Section: Data Visualization and Exploration Of Chemical Spacementioning

confidence: 99%

BIGCHEM: Challenges and Opportunities for Big Data Analysis in Chemistry

Tetko

Engkvist

Koch

et al. 2016

Molecular Informatics

Self Cite

106

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The increasing volume of biomedical data in chemistry and life sciences requires the development of new methods and approaches for their handling. Here, we briefly discuss some challenges and opportunities of this fast growing area of research with a focus on those to be addressed within the BIGCHEM project. The article starts with a brief description of some available resources for “Big Data” in chemistry and a discussion of the importance of data quality. We then discuss challenges with visualization of millions of compounds by combining chemical and biological data, the expectations from mining the “Big Data” using advanced machine‐learning methods, and their applications in polypharmacology prediction and target de‐convolution in phenotypic screening. We show that the efficient exploration of billions of molecules requires the development of smart strategies. We also address the issue of secure information sharing without disclosing chemical structures, which is critical to enable bi‐party or multi‐party data sharing. Data sharing is important in the context of the recent trend of “open innovation” in pharmaceutical industry, which has led to not only more information sharing among academics and pharma industries but also the so‐called “precompetitive” collaboration between pharma companies. At the end we highlight the importance of education in “Big Data” for further progress of this area.

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Expanding the fragrance chemical space for virtual screening

Cited by 39 publications

References 60 publications

Identification of Angiotensin Converting Enzyme Inhibitor: An In Silico Perspective

Identification of Angiotensin Converting Enzyme Inhibitor: An In Silico Perspective

GEN: highly efficient SMILES explorer using autodidactic generative examination networks

BIGCHEM: Challenges and Opportunities for Big Data Analysis in Chemistry

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