Computational methods in drug discovery.
TLDR
An overview of computational methods used in different facets of drug discovery and highlight some of the recent successes is presented, both structure-based and ligand-based drug discovery methods are discussed.Abstract:
The process for drug discovery and development is challenging, time consuming and expensive. Computer-aided drug discovery (CADD) tools can act as a virtual shortcut, assisting in the expedition of this long process and potentially reducing the cost of research and development. Today CADD has become an effective and indispensable tool in therapeutic development. The human genome project has made available a substantial amount of sequence data that can be used in various drug discovery projects. Additionally, increasing knowledge of biological structures, as well as increasing computer power have made it possible to use computational methods effectively in various phases of the drug discovery and development pipeline. The importance of in silico tools is greater than ever before and has advanced pharmaceutical research. Here we present an overview of computational methods used in different facets of drug discovery and highlight some of the recent successes. In this review, both structure-based and ligand-based drug discovery methods are discussed. Advances in virtual high-throughput screening, protein structure prediction methods, protein-ligand docking, pharmacophore modeling and QSAR techniques are reviewed.read more
Citations
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References
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Journal ArticleDOI
Virtual High-Throughput Screening To Identify Novel Activin Antagonists
Jie Zhu,Rama K. Mishra,Gary E. Schiltz,Yogeshwar Makanji,Karl A. Scheidt,Andrew P. Mazar,Teresa K. Woodruff +6 more
TL;DR: This approach is the first to identify a first-in-class small-molecule antagonist of activin binding to ALK4, which opens a completely new approach to inhibiting the activity of TGFβ receptor superfamily members.
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LigMerge: A Fast Algorithm to Generate Models of Novel Potential Ligands from Sets of Known Binders
TL;DR: The novel algorithm LigMerge identifies the maximum (largest) common substructure of two three‐dimensional ligand models, superimposes these two substructures, and systematically mixes and matches the distinct fragments attached to the common subst structure at each common atom, thereby generating multiple compound models related to the known inhibitors that can be evaluated using computer docking prior to synthesis and experimental testing.
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Identification of novel compounds against an R294K substitution of influenza A (H7N9) virus using ensemble based drug virtual screening.
TL;DR: Virtual screening targeting the Neuraminidase (NA) protein against natural compounds of traditional Chinese medicine database (TCM) and ZINC natural products to identify novel inhibitors combat resistant strains of influenza virus H7N9 is performed.
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Development of novel proteasome inhibitors based on phthalazinone scaffold.
TL;DR: As 6c was the most potent inhibitor of proteasome, the structure-activity relationship (SAR) of 6c analogs was examined and electron positive triphenylphosphine group was first introduced in the structure of prote asome inhibitors and potent inhibition was achieved.
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In silico screening for Plasmodium falciparum enoyl-ACP reductase inhibitors.
TL;DR: A combined computational and experimental study to identify novel inhibitors of enoyl-acyl carrier protein reductase (PfENR) in the fatty acid biosynthesis pathway and identifies five low-micromolar pyrimidine dione inhibitors of PfENR.
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