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Open AccessJournal ArticleDOI

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.

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Citations
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Journal ArticleDOI

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.
Journal ArticleDOI

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.
Journal ArticleDOI

Oxetane-containing metabolites: origin, structures, and biological activities.

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.
Book ChapterDOI

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.
Journal ArticleDOI

How to Achieve Better Results Using PASS-Based Virtual Screening: Case Study for Kinase Inhibitors

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.
References
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Journal ArticleDOI

AutoDock Vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading

TL;DR: AutoDock Vina achieves an approximately two orders of magnitude speed‐up compared with the molecular docking software previously developed in the lab, while also significantly improving the accuracy of the binding mode predictions, judging by tests on the training set used in AutoDock 4 development.

疟原虫var基因转换速率变化导致抗原变异[英]/Paul H, Robert P, Christodoulou Z, et al//Proc Natl Acad Sci U S A

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TL;DR: PfPMP1)与感染红细胞、树突状组胞以及胎盘的单个或多个受体作用,在黏附及免疫逃避中起关键的作�ly.
Journal ArticleDOI

CHARMM: A program for macromolecular energy, minimization, and dynamics calculations

TL;DR: The CHARMM (Chemistry at Harvard Macromolecular Mechanics) as discussed by the authors is a computer program that uses empirical energy functions to model macromolescular systems, and it can read or model build structures, energy minimize them by first- or second-derivative techniques, perform a normal mode or molecular dynamics simulation, and analyze the structural, equilibrium, and dynamic properties determined in these calculations.
Journal ArticleDOI

Scalable molecular dynamics with NAMD

TL;DR: NAMD as discussed by the authors is a parallel molecular dynamics code designed for high-performance simulation of large biomolecular systems that scales to hundreds of processors on high-end parallel platforms, as well as tens of processors in low-cost commodity clusters, and also runs on individual desktop and laptop computers.
Journal ArticleDOI

Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings

TL;DR: Experimental and computational approaches to estimate solubility and permeability in discovery and development settings are described in this article, where the rule of 5 is used to predict poor absorption or permeability when there are more than 5 H-bond donors, 10 Hbond acceptors, and the calculated Log P (CLogP) is greater than 5 (or MlogP > 415).
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