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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Iterative Molecular Dynamics-Rosetta Membrane Protein Structure Refinement Guided by Cryo-EM Densities.
TL;DR: This study extended cryo-EM density-guided iterative Rosetta-MD to membrane proteins and was able to refine the predicted structures of these membrane proteins to atomic resolutions and showed that the resolution of the density maps determines the improvement and quality of the refined models.
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Deep Transferable Compound Representation across Domains and Tasks for Low Data Drug Discovery.
TL;DR: A new architecture by utilizing graph convolutional network and adversarial domain adaptation network to tackle small molecule-based drug discovery is proposed and the results showed the effectiveness of the proposed approach in transferring the related knowledge from source to target dataset.
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Oxetane-containing metabolites: origin, structures, and biological activities.
Vera A. Vil,Alexander O. Terent'ev,Abed Al Aziz Al Quntar,Tatyana A. Gloriozova,Nick Savidov,Valery M. Dembitsky,Valery M. Dembitsky +6 more
TL;DR: The biological activity of OCC that is produced by bacteria and Actinomycetes demonstrates antineoplastic, antiviral, and antifungal activity with confidence an angiogenesis stimulator, respiratory analeptic, and antiallergic activity.
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Performing an In Silico Repurposing of Existing Drugs by Combining Virtual Screening and Molecular Dynamics Simulation
TL;DR: In this chapter, this chapter tried to describe a method that combines structure-based virtual screening and molecular dynamics simulation which can find effective compounds among existing drugs that may affect on a specific molecular target.
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How to Achieve Better Results Using PASS-Based Virtual Screening: Case Study for Kinase Inhibitors
Pavel V. Pogodin,Alexey Lagunin,Anastasia V. Rudik,Dmitry Filimonov,Dmitry S. Druzhilovskiy,Mark C. Nicklaus,Vladimir Poroikov +6 more
TL;DR: Depending on the availability of data for a particular biological activity, one may choose the first or the second approach for creating ligand-based computational tools to achieve the best possible results in virtual screening.
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Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings
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