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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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Drug Discovery Today

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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Kinase inhibitors: the road ahead

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

A comparison of physiochemical property profiles of development and marketed oral drugs.

TL;DR: A study in which the distributions of physiochemical properties of oral drugs in different phases of clinical development are compared to those already marketed, showing that the mean molecular weight of orally administered drugs in development decreases on passing through each of the different clinical phases and gradually converges toward the mean Molecular weight of marketed oral drugs.
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Detailed Analysis of Scoring Functions for Virtual Screening

TL;DR: A comprehensive study of the performance of fast scoring functions for library docking using the program FlexX as the docking engine shows that a well-chosen combination of two of the tested scoring functions leads to a new, robust scoring scheme with superior performance in virtual screening.
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Gaussian Accelerated Molecular Dynamics: Unconstrained Enhanced Sampling and Free Energy Calculation.

TL;DR: The free energy profiles obtained from reweighting of the GaMD simulations allow us to identify distinct low-energy states of the biomolecules and characterize the protein-folding and ligand-binding pathways quantitatively.
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Managing the drug discovery/development interface

Tony Kennedy
- 01 Oct 1997 - 
TL;DR: Successful management of the research-to-development interface will require better quality decision making in the selection of entrants to development and in portfolio management.
Journal ArticleDOI

Assessing scoring functions for protein-ligand interactions.

TL;DR: Improved modeling of the protonation states leads to a better prediction of binding affinities in the case of the generalized Born and the Poisson continuum models used in conjunction with the CHARMm force field.
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