虚拟高通量硅筛选

Markus H.J. Seifert, Kristina Wolf, Daniel Vitt
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引用次数: 63

摘要

计算机方法可以通过平均节省1.3亿美元和0.8年的时间,大大有利于药物的发现和开发。虚拟高通量筛选(vHTS)应用于大型虚拟分子数据库的对接和定位等硅方法,以丰富生物活性化合物,从而产生先导结构。在工业环境中,这里概述的常用的基于配体和基于受体的方法需要更快的计算速度才能获得最大的收益。使用新的快速反馈驱动筛选方法的智能数据库搜索似乎在成本和时间效益方面都特别有益。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Virtual high-throughput in silico screening

In silico methods may benefit drug discovery and development significantly by saving an average of $130 million and 0.8 years per drug. Virtual high-throughput screening (vHTS) applies in silico approaches, such as docking and alignment, to large virtual molecular databases to enrich biologically active compounds in order to yield lead structures. In an industrial environment, the commonly used ligand-based and receptor-based methods outlined here need to be computationally faster to return the utmost benefit. Intelligent database searching using new fast feedback-driven screening methods appears to be particularly rewarding in terms of both cost and time benefits.

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