From Big Data to Artificial Intelligence: chemoinformatics meets new challenges

Tetko, Igor V.; Engkvist, Ola

doi:10.1186/s13321-020-00475-y

Cited by 23 publications

(28 citation statements)

References 39 publications

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“…The vast potential for the development of new medicines from marine-based sources has been supported by several initiatives in the field of marine biotechnology, including “Blue Growth” of Horizon 2020, which aims to increase the use of marine resources for various biotechnological applications by optimization of cultivation conditions and enhanced sampling processes (Lauritano 2018 ). Furthermore, the analyses of “Big Data” in chemistry using artificial intelligence and machine learning are a novel promising trend (Tetko and Engkvist 2020 ). The application of computational methodologies such as chemoinformatics, combining structure-based (SB) and ligand-based (LB) approaches, allowed virtual screening of multiple natural products for computer-assisted drug discovery (Pereira and Aires-de-Sousa 2018 ).…”

Section: Mnp/maa Biotechnological Applicationsmentioning

confidence: 99%

Recent advances in the discovery of novel marine natural products and mycosporine-like amino acid UV-absorbing compounds

Rosic

2021

Appl Microbiol Biotechnol

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Bioactive compounds from marine environments represent a rich source of bioproducts for potential use in medicine and biotechnology. To discover and identify novel marine natural products (MNPs), evaluating diverse biological activities is critical. Increased sensitivity and specificity of omics technologies, especially next-generation high-throughput sequencing combined with liquid chromatography-mass spectrometry and nuclear magnetic resonance, are speeding up the discovery of novel bioactive compounds. Mycosporine-like amino acids (MAAs) isolated from many marine microorganisms are among highly promising MNPs characterized by ultraviolet radiation (UV) absorbing capacities and are recognized as a potential source of ecologically friendly sunscreens. MAAs absorb damaging UV radiation with maximum absorption in the range of 310–360 nm, including both UVA and UVB ranges. MAAs are also characterized by other biological activities such as anti-oxidant, anti-cancer, and anti-inflammatory activities. The application of modern omics approaches promoted some recent developments in our understanding of MAAs’ functional significance and diversity. This review will summarize the various modern tools that could be applied during the identification and characterization of MNPs, including MAAs, to further their innovative applications. Key points • New omics technologies are speeding up the discovery of novel bio-products • The vast diversity of bioactive capacities of marine natural products described • Marine microorganisms as a source of environmentally friendly sunscreens

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Section: Mnp/maa Biotechnological Applicationsmentioning

confidence: 99%

Recent advances in the discovery of novel marine natural products and mycosporine-like amino acid UV-absorbing compounds

Rosic

2021

Appl Microbiol Biotechnol

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“…The transfer of information or signals occurs between neurons and therefore leads to a complex network that learns with feedback mechanisms. The usage of Deep Learning-based methods has been a recent phenomenon in chemoinformatics (56,57). The present work utilizes novel Deep learning architectures in olfaction and therefore allows classification of chemicals into odorants and non-odorants and identification of putative cognate odorant receptors for the query odorants or vice versa.…”

Section: Methods and Implementation Deep Neural Network In Chemoinformaticsmentioning

confidence: 99%

OdoriFy: A conglomerate of artificial intelligence–driven prediction engines for olfactory decoding

Gupta

Mittal

Agrawal

et al. 2021

Journal of Biological Chemistry

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This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

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“…A chemical compound is represented by a chemical graph defined to be a tuple C = (H, α, β) of a simple, connected undirected graph H and functions α : V (H) → Λ and β : E(H) → [1,3]. The set of atoms and the set of bonds in the compound are represented by the vertex set V (H) and the edge set E(H), respectively.…”

Section: Modeling Of Chemical Compoundsmentioning

confidence: 99%

“…Background In recent years, molecular design has received a great deal of attention from various research fields such as chemoinformatics, bioinformatics, and materials informatics [1,2,3]. In particular, extensive studies have been done for molecular design using artificial neural networks (ANNs).…”

Section: Introductionmentioning

confidence: 99%

A Method for Inferring Polymers Based on Linear Regression and Integer Programming

Ido,

Cao,

Zhu

et al. 2021

Preprint

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A novel framework has recently been proposed for designing the molecular structure of chemical compounds with a desired chemical property using both artificial neural networks and mixed integer linear programming. In this paper, we design a new method for inferring a polymer based on the framework. For this, we introduce a new way of representing a polymer as a form of monomer and define new descriptors that feature the structure of polymers. We also use linear regression as a building block of constructing a prediction function in the framework. The results of our computational experiments reveal a set of chemical properties on polymers to which a prediction function constructed with linear regression performs well. We also observe that the proposed method can infer polymers with up to 50 non-hydrogen atoms in a monomer form.

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From Big Data to Artificial Intelligence: chemoinformatics meets new challenges

Cited by 23 publications

References 39 publications

Recent advances in the discovery of novel marine natural products and mycosporine-like amino acid UV-absorbing compounds

Recent advances in the discovery of novel marine natural products and mycosporine-like amino acid UV-absorbing compounds

OdoriFy: A conglomerate of artificial intelligence–driven prediction engines for olfactory decoding

A Method for Inferring Polymers Based on Linear Regression and Integer Programming

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