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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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TL;DR: A wide range of new lead finding and lead optimization opportunities result from novel screening methods by NMR, which are the topic of this review article.
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TL;DR: An overview of the novel targets, biological processes and disease areas that kinase-targeting small molecules are being developed against, highlight the associated challenges and assess the strategies and technologies that are enabling efficient generation of highly optimized kinase inhibitors are provided.
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Structure-Based Virtual Screening: From Classical to Artificial Intelligence.

TL;DR: An overview of the challenges involved in the use of CADD to performSBVS, the areas where CADD tools support SBVS, a comparison between the most commonly used tools, and the techniques currently used in an attempt to reduce the time and cost in the drug development process are presented.
References
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Journal ArticleDOI

In silico approaches to prediction of aqueous and DMSO solubility of drug-like compounds: trends, problems and solutions.

TL;DR: A critical discussion of in silico models for the prediction of DMSO and water solubility of drug-like compounds used for virtual screening and main tendencies in the field, "booming" approaches and unsolved problems are provided.
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Simulation-Based Approaches for Determining Membrane Permeability of Small Compounds.

TL;DR: Assessment of the efficiency and accuracy of several methods for the calculation of the permeability of a model DMPC bilayer to urea, benzoic acid, and codeine shows that Pm can be reliably estimated from only a few data points, leading to recommendations for calculating Pm from simulations.
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Prediction of Protein–Ligand Binding Poses via a Combination of Induced Fit Docking and Metadynamics Simulations

TL;DR: A metadynamics plus IFD strategy for accurate and reliable prediction of the structures of protein-ligand complexes at a practically useful computational cost and significantly increases the accuracy of the underlying IFD protocol across a large data set comprising 42 different ligand-receptor systems.
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LigBuilder 2: A Practical de Novo Drug Design Approach

TL;DR: A cavity detection procedure is implemented to detect the positions and shapes of the binding sites on the surface of a given protein structure and to quantitatively estimate drugability in LigBuilder 2.0.
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Comparative Performance of Several Flexible Docking Programs and Scoring Functions: Enrichment Studies for a Diverse Set of Pharmaceutically Relevant Targets

TL;DR: The Glide XP methodology is shown to consistently yield enrichments superior to the two alternative methods, while GOLD outperforms DOCK on average and shows that docking into multiple receptor structures can decrease the docking error in screening a diverse set of active compounds.
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