Fragment Informatics and Computational Fragment‐Based Drug Design: An Overview and Update
Abstract
Fragment‐based drug design (FBDD) is a promising approach for the discovery and optimization of lead compounds. Despite its successes, FBDD also faces some internal limitations and challenges. FBDD requires a high quality of target protein and good solubility of fragments. Biophysical techniques for fragment screening necessitate expensive detection equipment and the strategies for evolving fragment hits to leads remain to be improved. Regardless, FBDD is necessary for investigating larger chemical space and can be applied to challenging biological targets. In this scenario, cheminformatics and computational chemistry can be used as alternative approaches that can significantly improve the efficiency and success rate of lead discovery and optimization. Cheminformatics and computational tools assist FBDD in a very flexible manner. Computational FBDD can be used independently or in parallel with experimental FBDD for efficiently generating and optimizing leads. Computational FBDD can also be integrated into each step of experimental FBDD and help to play a synergistic role by maximizing its performance. This review will provide critical analysis of the complementarity between computational and experimental FBDD and highlight recent advances in new algorithms and successful examples of their applications. In particular, fragment‐based cheminformatics tools, high‐throughput fragment docking, and fragment‐based de novo drug design will provide the focus of this review. We will also discuss the advantages and limitations of different methods and the trends in new developments that should inspire future research.
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By employing structure-based virtual screening, they identified a quinazolinone fragment, hit 4 (Kd = 467 ± 65 nM), with two optical isomers that acted on different "hot spots" of PDEδ.
“By employing structure-based virtual screening, they identified a quinazolinone fragment, hit 4 (Kd = 467 ± 65 nM), with two optical isomers that acted on different “hot spots” of PDEδ.349–352”
From Recent advances in targeting the “undruggable” proteins: from drug discovery to clinical trials (2023) · cites this paper as [351]
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- Authors
- Sheng, C., & Zhang, W.
- Affiliations
- Second Military Medical University (CN)
- Corresponding author
- Sheng, Chunquan
- Venue
- Medicinal Research Reviews
- Publisher
- Wiley
- Published
- 2012-03-19
- Volume
- 33
- Issue
- 3
- Pages
- 554-598
- ISSN
- 0198-6325 (ISSN-L)
- 1098-1128
- OpenAlex
- W1531916394
- Cited by
- 80
- References
- 261
- Citations by year
- 2026: 3 · 2025: 6 · 2024: 11 · 2023: 11 · 2022: 5 · 2021: 4 · 2020: 5 · 2019: 3 · 2018: 5 · 2017: 3 · 2016: 4 · 2015: 6 · 2014: 6 · 2013: 5 · 2012: 3
- FWCI
- 0.74
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Medicinal Research Reviews (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Chemical Synthesis and Analysis
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Degradation and Inhibitors
- Keywords
- fragment-based drug discovery, cheminformatics, fragment-based docking, lead compound identification, computer-aided drug design, fragment screening
- MeSH terms
- Drug Evaluation, Preclinical, Ligands, Receptors, Drug, Software, Structure-Activity Relationship, Drug Design, Computational Biology, Catalytic Domain, Cyclophilin A, Nitric Oxide Synthase Type I, Serotonin 5-HT1 Receptor Antagonists, 14-alpha Demethylase Inhibitors, Molecular Docking Simulation
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