ArticleAdvanced science (Weinheim, Baden-Wurttemberg, Germany)2021
ScaffComb: A Phenotype-Based Framework for Drug Combination Virtual Screening in Large-Scale Chemical Datasets.
Article in Advanced science (Weinheim, Baden-Wurttemberg, Germany), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Harnessing AI to fuse phenotypic signatures for drug target identification: progress in computational modeling.Briefings in bioinformatics · 2026Review
- Drug resistance in cancer: molecular mechanisms and emerging treatment strategies.Molecular biomedicine · 2025Review
- Discovery of novel VEGFR2 inhibitors against non-small cell lung cancer based on fingerprint-enhanced graph attention convolutional network.Journal of translational medicine · 2024Article
- Augmented drug combination dataset to improve the performance of machine learning models predicting synergistic anticancer effects.Scientific reports · 2024Article
- Augmented drug combination dataset to improve the performance of machine learning models predicting synergistic anticancer effects.Research square · 2023Article
- Review of Predicting Synergistic Drug Combinations.Life (Basel, Switzerland) · 2023Review
- SYNDEEP: a deep learning approach for the prediction of cancer drugs synergy.Scientific reports · 2023Article
- Recent Advances in Application of Computer-Aided Drug Design in Anti-Influenza A Virus Drug Discovery.International journal of molecular sciences · 2022Review
- ScaffComb: A Phenotype-Based Framework for Drug Combination Virtual Screening in Large-Scale Chemical Datasets.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2021Article
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Authors and funding
5 authors.
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Abstract
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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