Dataset Information

Deprotometalation-Iodolysis and Direct Iodination of 1-Arylated 7-Azaindoles: Reactivity Studies and Molecule Properties.

ABSTRACT: Five protocols were first compared for the copper-catalyzed C-N bond formation between 7-azaindole and aryl/heteroaryl iodides/bromides. The 1-arylated 7-azaindoles thus obtained were subjected to deprotometalation-iodolysis sequences using lithium 2,2,6,6-tetramethylpiperidide as the base and the corresponding zinc diamide as an in situ trap. The reactivity of the substrate was discussed in light of the calculated atomic charges and the pKa values. The behavior of the 1-arylated 7-azaindoles in direct iodination was then studied, and the results explained by considering the HOMO orbital coefficients and the atomic charges. Finally, some of the iodides generated, generally original, were involved in the N-arylation of indole. While crystallographic data were collected for fifteen of the synthesized compounds, biological properties (antimicrobial, antifungal and antioxidant activity) were evaluated for others.

SUBMITTER: Ameur Messaoud MY

PROVIDER: S-EPMC8537530 | biostudies-literature | 2021 Oct

REPOSITORIES: biostudies-literature

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Publications

Deprotometalation-Iodolysis and Direct Iodination of 1-Arylated 7-Azaindoles: Reactivity Studies and Molecule Properties.

Ameur Messaoud Mohamed Yacine MY Bentabed-Ababsa Ghenia G Fajloun Ziad Z Hamze Monzer M Halauko Yury S YS Ivashkevich Oleg A OA Matulis Vadim E VE Roisnel Thierry T Dorcet Vincent V Mongin Florence F

Molecules (Basel, Switzerland) 20211019 20

Five protocols were first compared for the copper-catalyzed C-N bond formation between 7-azaindole and aryl/heteroaryl iodides/bromides. The 1-arylated 7-azaindoles thus obtained were subjected to deprotometalation-iodolysis sequences using lithium 2,2,6,6-tetramethylpiperidide as the base and the corresponding zinc diamide as an in situ trap. The reactivity of the substrate was discussed in light of the calculated atomic charges and the p<i>K</i><sub>a</sub> values. The behavior of the 1-arylat ...[more]

PMID: 34684895

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Project description:Scientists rely on high-throughput screening tools to identify promising small-molecule compounds for the development of biochemical probes and drugs. This study focuses on the identification of promiscuous bioactive compounds, which are compounds that appear active in many high-throughput screening experiments against diverse targets but are often false-positives which may not be easily developed into successful probes. These compounds can exhibit bioactivity due to nonspecific, intractable mechanisms of action and/or by interference with specific assay technology readouts. Such "frequent hitters" are now commonly identified using substructure filters, including pan assay interference compounds (PAINS). Herein, we show that mechanistic modeling of small-molecule reactivity using deep learning can improve upon PAINS filters when modeling promiscuous bioactivity in PubChem assays. Without training on high-throughput screening data, a deep learning model of small-molecule reactivity achieves a sensitivity and specificity of 18.5% and 95.5%, respectively, in identifying promiscuous bioactive compounds. This performance is similar to PAINS filters, which achieve a sensitivity of 20.3% at the same specificity. Importantly, such reactivity modeling is complementary to PAINS filters. When PAINS filters and reactivity models are combined, the resulting model outperforms either method alone, achieving a sensitivity of 24% at the same specificity. However, as a probabilistic model, the sensitivity and specificity of the deep learning model can be tuned by adjusting the threshold. Moreover, for a subset of PAINS filters, this reactivity model can help discriminate between promiscuous and nonpromiscuous bioactive compounds even among compounds matching those filters. Critically, the reactivity model provides mechanistic hypotheses for assay interference by predicting the precise atoms involved in compound reactivity. Overall, our analysis suggests that deep learning approaches to modeling promiscuous compound bioactivity may provide a complementary approach to current methods for identifying promiscuous compounds.

Dataset Information

Deprotometalation-Iodolysis and Direct Iodination of 1-Arylated 7-Azaindoles: Reactivity Studies and Molecule Properties.

Publications

Deprotometalation-Iodolysis and Direct Iodination of 1-Arylated 7-Azaindoles: Reactivity Studies and Molecule Properties.

Similar Datasets

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