From Activity Cliffs to Promiscuity Cliffs

Bajorath, Jürgen

doi:10.4155/fsoa-2017-0065

Cited by 3 publications

(1 citation statement)

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“…In addition, as scaffolds typically represent the core structures of compounds in analog series and chemical reactions, recently developed scaffold‐based methods [ 23 ] allow us to screen drug libraries in terms of molecular scaffolds, which reduces the searching space. [ 23 , 24 ] Therefore, this study proposes a deep learning model that integrates phenotypic information into molecular scaffolds for drug screening.…”

Section: Introductionmentioning

confidence: 99%

ScaffComb: A Phenotype‐Based Framework for Drug Combination Virtual Screening in Large‐Scale Chemical Datasets

Chen

Zeng

et al. 2021

Advanced Science

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Combinational therapy is used for a long time in cancer treatment to overcome drug resistance related to monotherapy. Increased pharmacological data and the rapid development of deep learning methods have enabled the construction of models to predict and screen drug pairs. However, the size of drug libraries is restricted to hundreds to thousands of compounds. The ScaffComb framework, which aims to bridge the gaps in the virtual screening of drug combinations in large‐scale databases, is proposed here. Inspired by phenotype‐based drug design, ScaffComb integrates phenotypic information into molecular scaffolds, which can be used to screen the drug library and identify potent drug combinations. First, ScaffComb is validated using the US food and drug administration dataset and known drug combinations are successfully reidentified. Then, ScaffComb is applied to screen the ZINC and ChEMBL databases, which yield novel drug combinations and reveal an ability to discover new synergistic mechanisms. To our knowledge, ScaffComb is the first method to use phenotype‐based virtual screening of drug combinations in large‐scale chemical datasets.

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Section: Introductionmentioning

confidence: 99%