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ACFIS 2.0: an improved web-server for fragment-based drug discovery via a dynamic screening strategy.
- Source :
-
Nucleic acids research [Nucleic Acids Res] 2023 Jul 05; Vol. 51 (W1), pp. W25-W32. - Publication Year :
- 2023
-
Abstract
- Drug discovery, which plays a vital role in maintaining human health, is a persistent challenge. Fragment-based drug discovery (FBDD) is one of the strategies for the discovery of novel candidate compounds. Computational tools in FBDD could help to identify potential drug leads in a cost-efficient and time-saving manner. The Auto Core Fragment in silico Screening (ACFIS) server is a well-established and effective online tool for FBDD. However, the accurate prediction of protein-fragment binding mode and affinity is still a major challenge for FBDD due to weak binding affinity. Here, we present an updated version (ACFIS 2.0), that incorporates a dynamic fragment growing strategy to consider protein flexibility. The major improvements of ACFIS 2.0 include (i) increased accuracy of hit compound identification (from 75.4% to 88.5% using the same test set), (ii) improved rationality of the protein-fragment binding mode, (iii) increased structural diversity due to expanded fragment libraries and (iv) inclusion of more comprehensive functionality for predicting molecular properties. Three successful cases of drug lead discovery using ACFIS 2.0 are described, including drugs leads to treat Parkinson's disease, cancer, and major depressive disorder. These cases demonstrate the utility of this web-based server. ACFIS 2.0 is freely available at http://chemyang.ccnu.edu.cn/ccb/server/ACFIS2/.<br /> (© The Author(s) 2023. Published by Oxford University Press on behalf of Nucleic Acids Research.)
- Subjects :
- Humans
Depressive Disorder, Major drug therapy
Proteins chemistry
Neoplasms drug therapy
Parkinson Disease drug therapy
Internet
Drug Discovery instrumentation
Drug Discovery methods
Data Visualization
Computer Simulation
Drug Evaluation, Preclinical instrumentation
Drug Evaluation, Preclinical methods
Subjects
Details
- Language :
- English
- ISSN :
- 1362-4962
- Volume :
- 51
- Issue :
- W1
- Database :
- MEDLINE
- Journal :
- Nucleic acids research
- Publication Type :
- Academic Journal
- Accession number :
- 37158247
- Full Text :
- https://doi.org/10.1093/nar/gkad348