ArticleNucleic acids research2023
ACFIS 2.0: an improved web-server for fragment-based drug discovery via a dynamic screening strategy.
Article in Nucleic acids research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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8 citing papers in PubMed, 18 citations in OpenAlex.
- APPi: A Multiscale Qualitative-Quantitative Insecticide-Likeness Evaluation Platform and Application.Plant biotechnology journal · 2025Article
- Overcoming Clusterin-Induced Chemoresistance in Cancer: A Computational Study Using a Fragment-Based Drug Discovery Approach.Biology · 2025Article
- Perspectives on Applications ofMolecules (Basel, Switzerland) · 2024Review
- Structure-guided discovery of novel dUTPase inhibitors with anti-Journal of enzyme inhibition and medicinal chemistry · 2024Article
- Revolutionizing Molecular Design for Innovative Therapeutic Applications through Artificial Intelligence.Molecules (Basel, Switzerland) · 2024Review
- Stabilization of dimeric PYR/PYL/RCAR family members relieves abscisic acid-induced inhibition of seed germination.Nature communications · 2024Article
- Fragment-based drug discovery for disorders of the central nervous system: designing better drugs piece by piece.Frontiers in chemistry · 2024Review
- Type II bacterial toxin-antitoxins: hypotheses, facts, and the newfound plethora of the PezAT system.FEMS microbiology reviews · 2023Article
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Authors and funding
5 authors at 1 institution in 1 country.
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No grant is acknowledged in the PubMed record.
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/.
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