Artificial intelligence in chemistry and drug design

Brown, Nathan; Ertl, Peter; Lewis, Richard A.; Luksch, Torsten; Reker, Daniel; Schneider, Nadine

doi:10.1007/s10822-020-00317-x

Cited by 122 publications

(79 citation statements)

References 87 publications

(98 reference statements)

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“…Quantitative structure-activity relationship (QSAR) study is a very significant ligand-based molecular modelling technique that easily recognised the effect of structural and physico-chemical features of ligands on the biological activity [29] , [30] . Not only that, it offers prediction of particular compounds to their biological activities of interest.…”

Section: Introductionmentioning

confidence: 99%

Chemical-informatics approach to COVID-19 drug discovery: Exploration of important fragments and data mining based prediction of some hits from natural origins as main protease (Mpro) inhibitors

Ghosh

Amin

Gayen

et al. 2021

Journal of Molecular Structure

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Highlight It is challenging to identify effective SARS-CoV-2 main protease inhibitor urgently. This study involves classification QSAR based data mining of diverse SARS-CoV Mpro inhibitors. Important molecular features regulating the Mpro inhibitory properties are identified. Prediction of recently reported natural origin based virtual hits is reported.

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Section: Introductionmentioning

confidence: 99%

Chemical-informatics approach to COVID-19 drug discovery: Exploration of important fragments and data mining based prediction of some hits from natural origins as main protease (Mpro) inhibitors

Ghosh

Amin

Gayen

et al. 2021

Journal of Molecular Structure

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“… b The subject was reviewed and critically accessed in a number of recent publications ( Schneider, 2018 ; Schwaller and Laino, 2019 ; Brown et al., 2020 ; Lemonick, 2020 ). …”

Section: Resultsmentioning

confidence: 99%

Generating Multibillion Chemical Space of Readily Accessible Screening Compounds

Grygorenko

Radchenko

Dziuba³

et al. 2020

iScience

154

121

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Summary An approach to the generation of ultra-large chemical libraries of readily accessible (“REAL”) compounds is described. The strategy is based on the use of two- or three-step three-component reaction sequences and available starting materials with pre-validated chemical reactivity. After the preliminary parallel experiments, the methods with at least ∼80% synthesis success rate (such as acylation – deprotection – acylation of monoprotected diamines or amide formation – click reaction with functionalized azides) can be selected and used to generate the target chemical space. It is shown that by using only on the two aforementioned reaction sequences, a nearly 29-billion compound library is easily obtained. According to the predicted physico-chemical descriptor values, the generated chemical space contains large fractions of both drug-like and “beyond rule-of-five” members, whereas the strictest lead-likeness criteria (the so-called Churcher's rules) are met by the lesser part, which still exceeds 22 million.

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“…Cheminformatics is an old discipline that preceded the current interest in ML and AI by several decades, as shown by the subject of the 2000 Beilstein Bozen Symposium [ 61 ]. A recent discussion of AI and ML in chemistry and drug design [ 62 ] traces the beginning back to the classical 1964 papers of Hansch and Fujita [ 63 ] and Free and Wilson [ 64 ]. All that has really changed recently is the amount of data that is available and the upsurge of deep-learning algorithms, which date back to the late 1960’s [ 65 ] but were preceded in chemistry by far simpler back-propagation neural nets [ 66 ] and made their first impact around 2015 [ 67 ].…”

Section: Discussionmentioning

confidence: 99%

Models of necessity

Clark¹,

Hicks²

2020

Beilstein J. Org. Chem.

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The way chemists represent chemical structures as two-dimensional sketches made up of atoms and bonds, simplifying the complex three-dimensional molecules comprising nuclei and electrons of the quantum mechanical description, is the everyday language of chemistry. This language uses models, particularly of bonding, that are not contained in the quantum mechanical description of chemical systems, but has been used to derive machine-readable formats for storing and manipulating chemical structures in digital computers. This language is fuzzy and varies from chemist to chemist but has been astonishingly successful and perhaps contributes with its fuzziness to the success of chemistry. It is this creative imagination of chemical structures that has been fundamental to the cognition of chemistry and has allowed thought experiments to take place. Within the everyday language, the model nature of these concepts is not always clear to practicing chemists, so that controversial discussions about the merits of alternative models often arise. However, the extensive use of artificial intelligence (AI) and machine learning (ML) in chemistry, with the aim of being able to make reliable predictions, will require that these models be extended to cover all relevant properties and characteristics of chemical systems. This, in turn, imposes conditions such as completeness, compactness, computational efficiency and non-redundancy on the extensions to the almost universal Lewis and VSEPR bonding models. Thus, AI and ML are likely to be important in rationalizing, extending and standardizing chemical bonding models. This will not affect the everyday language of chemistry but may help to understand the unique basis of chemical language.

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Artificial intelligence in chemistry and drug design

Cited by 122 publications

References 87 publications

Chemical-informatics approach to COVID-19 drug discovery: Exploration of important fragments and data mining based prediction of some hits from natural origins as main protease (Mpro) inhibitors

Chemical-informatics approach to COVID-19 drug discovery: Exploration of important fragments and data mining based prediction of some hits from natural origins as main protease (Mpro) inhibitors

Generating Multibillion Chemical Space of Readily Accessible Screening Compounds

Models of necessity

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