Computational structure‐based drug design: Predicting target flexibility,Wiley Interdisciplinary Reviews: Computational Molecular Science

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Computational structure‐based drug design: Predicting target flexibility
Wiley Interdisciplinary Reviews: Computational Molecular Science ( IF 16.8 ) Pub Date : 2018-04-29 , DOI: 10.1002/wcms.1367
Jelisa Iglesias ₁ , Suwipa Saen-oon ₂ , Robert Soliva ₂ , Victor Guallar _{1,

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Affiliation

The role of molecular modeling in drug design has experienced a significant revamp in the last decade. The increase in computational resources and molecular models, along with software developments, is finally introducing a competitive advantage in early phases of drug discovery. Medium and small companies with strong focus on computational chemistry are being created, some of them having introduced important leads in drug design pipelines. An important source for this success is the extraordinary development of faster and more efficient techniques for describing flexibility in three‐dimensional structural molecular modeling. At different levels, from docking techniques to atomistic molecular dynamics, conformational sampling between receptor and drug results in improved predictions, such as screening enrichment, discovery of transient cavities, etc. In this review article we perform an extensive analysis of these modeling techniques, dividing them into high and low throughput, and emphasizing in their application to drug design studies. We finalize the review with a section describing our Monte Carlo method, PELE, recently highlighted as an outstanding advance in an international blind competition and industrial benchmarks.

中文翻译：

基于计算结构的药物设计：预测目标灵活性

在过去的十年中，分子建模在药物设计中的作用经历了重大改进。计算资源和分子模型的增加以及软件的开发，终于在药物发现的早期阶段引入了竞争优势。正在创建专注于计算化学的中小型公司，其中一些公司在药物设计流程中引入了重要的线索。成功的重要来源是在三维结构分子建模中描述灵活性的更快，更高效的技术的非凡发展。从对接技术到原子分子动力学，受体和药物之间的构象采样在不同层面上都可以改善预测结果，例如筛选富集，发现瞬时空洞，在这篇评论文章中，我们对这些建模技术进行了广泛的分析，将其分为高通量和低通量，并着重介绍了它们在药物设计研究中的应用。最后，我们将以描述我们的蒙特卡洛方法PELE的部分作为总结来完成审查，该方法最近被强调为国际盲人竞争和行业基准方面的杰出进步。

更新日期：2018-04-29

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