QSAR without borders

Muratov, Eugene; Bajorath, Jürgen; Sheridan, Robert P.; Tetko, Igor V.; Filimonov, Dmitry; Poroikov, Vladimir; Oprea, Tudor I.; Baskin, Igor I.; Varnek, Alexandre; Roitberg, Adrián E.; Isayev, Olexandr; Curtalolo, Stefano; Fourches, Denis; Cohen, Yoram; Aspuru‐Guzik, Alán; Winkler, David A.; Agrafiotis, Dimitris; Cherkasov, Artem; Tropsha, Alexander

doi:10.1039/d0cs00098a

Cited by 563 publications

(522 citation statements)

References 333 publications

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“…Accurate prediction of chemical reactions is an important goal both in academic and industrial research. [1][2][3] Recently, machine learning approaches have had tremendous success in quantitative prediction of reaction yields based on data from high-throughput experimentation 4,5 and enantioselectivities based on carefully selected universal training sets. 6 At the same time, traditional quantitative structure-reactivity relationship (QSRR) methods based on linear regression have seen a renaissance with interpretable, holistic models that can generalize across reaction types.…”

Section: Introductionmentioning

confidence: 99%

Machine learning meets mechanistic modelling for accurate prediction of experimental activation energies

et al. 2021

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Section: Introductionmentioning

confidence: 99%

Machine learning meets mechanistic modelling for accurate prediction of experimental activation energies

et al. 2021

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“…QSAR models developed by us earlier were used for selection of drugs 35,48 that could be repurposed as combinations and exclusion of potential drug-drug interactions and side effects. 52 All the models were developed according to the best practices of QSAR modeling 32,53,54 with a special attention paid for data curation [55][56][57] and rigorous external validation. 58 Mixture-specific descriptors and validation techniques 59 specially developed for modeling of drug combinations were utilized for modeling of drug-drug interactions.…”

Section: Introductionmentioning

confidence: 99%

Discovery of Synergistic and Antagonistic Drug Combinations against SARS-CoV-2 In Vitro

Bobrowski¹,

Chen

Eastman

et al. 2020

Preprint

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AbstractCOVID-19 is undoubtedly the most impactful viral disease of the current century, afflicting millions worldwide. As yet, there is not an approved vaccine, as well as limited options from existing drugs for treating this disease. We hypothesized that combining drugs with independent mechanisms of action could result in synergy against SARS-CoV-2. Using in silico approaches, we prioritized 73 combinations of 32 drugs with potential activity against SARS-CoV-2 and then tested them in vitro. Overall, we identified 16 synergistic and 8 antagonistic combinations, 4 of which were both synergistic and antagonistic in a dose-dependent manner. Among the 16 synergistic cases, combinations of nitazoxanide with three other compounds (remdesivir, amodiaquine and umifenovir) were the most notable, all exhibiting significant synergy against SARS-CoV-2. The combination of nitazoxanide, an FDA-approved drug, and remdesivir, FDA emergency use authorization for the treatment of COVID-19, demonstrate a strong synergistic interaction. Notably, the combination of remdesivir and hydroxychloroquine demonstrated strong antagonism. Overall, our results emphasize the importance of both drug repurposing and preclinical testing of drug combinations for potential therapeutic use against SARS-CoV-2 infections.

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“…One of the elements of this approach is to describe the structure of chemical compounds as a matrix of various physicochemical, topological, electronic, and other quantitative characteristics (descriptors). Taking into account the complexity of a molecular structure, we can state that the search for new descriptors with certain information is a challenging problem [24]. Note that, in QSAR/QSPR studies, some descriptors are used to describe the "structure" of chemical compounds (independent variables) and others act as "properties" (dependent variable).…”

Section: Resultsmentioning

confidence: 99%

Memory Effect in the Spatial Series Based on Diamond and Graphite Crystals

et al. 2020

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To study the relation between the structure of a compound and its properties is one of the fundamental trends in chemistry and materials science. A classic example is the well-known influence of the structures of diamond and graphite on their physicochemical properties, in particular, hardness. However, some other properties of these allotropic modifications of carbon, e.g., fractal properties, are poorly understood. In this work, the spatial series (interatomic distance histograms) calculated using the crystal structures of diamond and graphite are investigated. Hurst exponents H are estimated using detrended fluctuation analysis and power spectral density. The values of H are found to be 0.27–0.32 and 0.37–0.42 for diamond and graphite, respectively. The calculated data suggest that the spatial series have long memory with a negative correlation between the terms of the series; that is, they are antipersistent.

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QSAR without borders

Abstract: Word cloud summary of diverse topics associated with QSAR modeling that are discussed in this review.

Cited by 563 publications

References 333 publications

Machine learning meets mechanistic modelling for accurate prediction of experimental activation energies

Machine learning meets mechanistic modelling for accurate prediction of experimental activation energies

Discovery of Synergistic and Antagonistic Drug Combinations against SARS-CoV-2 In Vitro

Memory Effect in the Spatial Series Based on Diamond and Graphite Crystals

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