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计算机辅助药物发现

Computer-aided drug discovery.

作者信息

Bajorath Jürgen

机构信息

Department of Life Science Informatics, Rheinische Friedrich-Wilhelms-Universität, Dahlmannstr. 2, Bonn, D-53113, Germany.

出版信息

F1000Res. 2015 Aug 26;4. doi: 10.12688/f1000research.6653.1. eCollection 2015.

Abstract

Computational approaches are an integral part of interdisciplinary drug discovery research. Understanding the science behind computational tools, their opportunities, and limitations is essential to make a true impact on drug discovery at different levels. If applied in a scientifically meaningful way, computational methods improve the ability to identify and evaluate potential drug molecules, but there remain weaknesses in the methods that preclude naïve applications. Herein, current trends in computer-aided drug discovery are reviewed, and selected computational areas are discussed. Approaches are highlighted that aid in the identification and optimization of new drug candidates. Emphasis is put on the presentation and discussion of computational concepts and methods, rather than case studies or application examples. As such, this contribution aims to provide an overview of the current methodological spectrum of computational drug discovery for a broad audience.

摘要

计算方法是跨学科药物发现研究不可或缺的一部分。了解计算工具背后的科学原理、它们的机遇和局限性,对于在不同层面真正影响药物发现至关重要。如果以科学合理的方式应用,计算方法能够提高识别和评估潜在药物分子的能力,但这些方法仍存在一些弱点,妨碍了简单的应用。本文综述了计算机辅助药物发现的当前趋势,并讨论了选定的计算领域。重点介绍了有助于识别和优化新药候选物的方法。重点在于计算概念和方法的介绍与讨论,而非案例研究或应用示例。因此,本文旨在为广大读者提供计算药物发现当前方法学范围的概述。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/04b3/4756805/c9112f6096c0/f1000research-4-7147-g0000.jpg

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