Journal
ADVANCED SCIENCE
Volume 8, Issue 24, Pages -Publisher
WILEY
DOI: 10.1002/advs.202102092
Keywords
deep learning; drug combination; scaffold; virtual screening
Categories
Funding
- State Key Research Development Program of China [2017YFA0505503]
- National Natural Science Foundation of China [81890991]
- Chinese Academy of Sciences (CAS) Interdisciplinary Innovation Team [JCTD-2020-04]
- Beijing Municipal Natural Science Foundation [Z200021]
Ask authors/readers for more resources
This study introduces a new method, ScaffComb, which integrates phenotype information and molecular scaffolds for drug combination screening in large-scale databases, resulting in the discovery of novel drug combinations and the identification of new synergistic mechanisms.
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.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available