QSAR without borders.
QSAR without borders.
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DOI：
10.1039/d0cs00098a
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发表时间：
2020-06-07
影响因子：
46.2
通讯作者：
Tropsha A
中科院分区：
文献类型：
--
作者：
Muratov EN;Bajorath J;Sheridan RP;Tetko IV;Filimonov D;Poroikov V;Oprea TI;Baskin II;Varnek A;Roitberg A;Isayev O;Curtarolo S;Fourches D;Cohen Y;Aspuru-Guzik A;Winkler DA;Agrafiotis D;Cherkasov A;Tropsha A
化学生物活性和物理性质的预测一直是统计方法以及最近的机器学习和人工智能方法在化学科学中最重要的应用之一。这一研究领域被广泛称为定量构效关系（QSAR）建模，在过去的55年中开发了许多重要的算法，并在物理有机化学和药物化学中得到了广泛的应用。这一观点总结了QSAR建模的最新技术进展。重要的是，它还强调了算法的适用性，建模方法，并在QSAR开发的验证实践，以广泛的研究领域超越传统的QSAR领域。这些领域包括纳米技术、材料科学、生物材料、临床信息学等。随着现代研究方法产生的数据量迅速增加，强大的数据驱动建模方法的知识对于化学研究内外许多学科的科学家来说变得至关重要。我们希望，这一贡献将有助于应对这一挑战。
Prediction of chemical bioactivity and physical properties has been one of the most important applications of statistical and more recently, machine learning and artificial intelligence methods in chemical sciences. This field of research, broadly known as Quantitative Structure-Activity Relationships (QSAR) modeling, has developed many important algorithms and has found a broad range of applications in physical organic and medicinal chemistry in the past 55+ years. This Perspective summarizes recent technological advances in QSAR modeling. Importantly, it also highlights the applicability of algorithms, modeling methods, and validation practices developed in QSAR to a wide range of research areas beyond traditional QSAR fields. These fields include nanotechnology, materials science, biomaterials, clinical informatics, and others. As modern research methods generate rapidly increasing amounts of data, knowledge of robust data-driven modelling methods is becoming essential for scientists in many disciplines both within and outside of chemical research. We hope that this contribution will serve to address this challenge.
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影响因子：
13.6
作者：
Bartók AP;De S;Poelking C;Bernstein N;Kermode JR;Csányi G;Ceriotti M
通讯作者：
Ceriotti M
影响因子：
14.8
作者：
Arrowsmith CH;Audia JE;Austin C;Baell J;Bennett J;Blagg J;Bountra C;Brennan PE;Brown PJ;Bunnage ME;Buser-Doepner C;Campbell RM;Carter AJ;Cohen P;Copeland RA;Cravatt B;Dahlin JL;Dhanak D;Edwards AM;Frederiksen M;Frye SV;Gray N;Grimshaw CE;Hepworth D;Howe T;Huber KV;Jin J;Knapp S;Kotz JD;Kruger RG;Lowe D;Mader MM;Marsden B;Mueller-Fahrnow A;Müller S;O'Hagan RC;Overington JP;Owen DR;Rosenberg SH;Roth B;Ross R;Schapira M;Schreiber SL;Shoichet B;Sundström M;Superti-Furga G;Taunton J;Toledo-Sherman L;Walpole C;Walters MA;Willson TM;Workman P;Young RN;Zuercher WJ
通讯作者：
Zuercher WJ
影响因子：
8.6
作者：
Behler, Joerg;Parrinello, Michele
通讯作者：
Parrinello, Michele
影响因子：
6.4
作者：
Atieh, Maya;Taylor, Graham;Gharabaghi, Bahram
通讯作者：
Gharabaghi, Bahram
DOI：
10.1039/c6gc01492e
发表时间：
2016-08-21
期刊：
Green chemistry : an international journal and green chemistry resource : GC
影响因子：
--
作者：
Alves V;Muratov E;Capuzzi S;Politi R;Low Y;Braga R;Zakharov AV;Sedykh A;Mokshyna E;Farag S;Andrade C;Kuz'min V;Fourches D;Tropsha A
通讯作者：
Tropsha A