Predicting the Likelihood of Molecules to Act as Modulators of Protein-Protein Interactions.

Клики: 88
ID: 277800
2023
Метрики качества и эффективности статьи
Общее качество
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
Оценка качества ИИ
Не проанализировано
Readership in this journal
Emerging

Ranked #25 of 26 articles by views in Journal of chemical information and modeling

Most read Least read

Bar heights use a square-root scale.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Sign in to mint
Аннотация
Targeting protein-protein interactions (PPIs) by small molecule modulators (iPPIs) is an attractive strategy for drug therapy, and some iPPIs have already been introduced into the clinic. Blocking PPIs is however considered to be a more difficult task than inhibiting enzymes or antagonizing receptor activity. In this paper, we examine whether it is possible to predict the likelihood of molecules to act as iPPIs. Using our in-house iterative stochastic elimination (ISE) algorithm, we constructed two classification models that successfully distinguish between iPPIs from the iPPI-DB database and decoy molecules from either the Enamine HTS collection (ISE 1) or the ZINC database (ISE 2). External test sets of iPPIs taken from the TIMBAL database and decoys from Enamine HTS or ZINC were screened by the models: the area under the curve for the receiver operating characteristic curve was 0.85-0.89, and the Enrichment Factor increased from an initial 1 to as much as 66 for ISE 1 and 57 for ISE 2. Screening of the Enamine HTS and ZINC data sets through both models results in a library of ∼1.3 million molecules that pass either one of the models. This library is enriched with iPPI candidates that are structurally different from known iPPIs, and thus, it is useful for target-specific screenings and should accelerate the discovery of iPPI drug candidates. The entire library is available in Table S6.
Ссылочный ключ
wolk2023predictingjournal Используйте этот ключ для автоцитирования в рукописи при использовании SciMatic Manuscript Manager или Thesis Manager
Авторы Wolk, Omri;Goldblum, Amiram;
Журнал Journal of chemical information and modeling
Год 2023
DOI
10.1021/acs.jcim.2c00920
URL
Ключевые слова Ключевые слова не найдены

Цитирования

Цитирования не найдены. Чтобы добавить цитирование, свяжитесь с администратором по адресу info@scimatic.org

Комментарии

Комментариев пока нет. Будьте первым, кто прокомментирует эту статью.