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本文引用的文献

1
Target 2035 - update on the quest for a probe for every protein.2035年目标——关于为每种蛋白质寻找一种探针的探索进展
RSC Med Chem. 2021 Dec 3;13(1):13-21. doi: 10.1039/d1md00228g. eCollection 2022 Jan 27.
2
Computed structures of core eukaryotic protein complexes.核心真核蛋白复合物的计算结构。
Science. 2021 Dec 10;374(6573):eabm4805. doi: 10.1126/science.abm4805.
3
Crowdsourced identification of multi-target kinase inhibitors for RET- and TAU- based disease: The Multi-Targeting Drug DREAM Challenge.基于 RET 和 TAU 的疾病的多靶点激酶抑制剂的众包鉴定:多靶向药物 DREAM 挑战赛。
PLoS Comput Biol. 2021 Sep 14;17(9):e1009302. doi: 10.1371/journal.pcbi.1009302. eCollection 2021 Sep.
4
Accurate prediction of protein structures and interactions using a three-track neural network.使用三轨神经网络准确预测蛋白质结构和相互作用。
Science. 2021 Aug 20;373(6557):871-876. doi: 10.1126/science.abj8754. Epub 2021 Jul 15.
5
Highly accurate protein structure prediction with AlphaFold.利用 AlphaFold 进行高精度蛋白质结构预测。
Nature. 2021 Aug;596(7873):583-589. doi: 10.1038/s41586-021-03819-2. Epub 2021 Jul 15.
6
A white-knuckle ride of open COVID drug discovery.新冠开放药物研发的惊险历程。
Nature. 2021 Jun;594(7863):330-332. doi: 10.1038/d41586-021-01571-1.
7
ZINC20-A Free Ultralarge-Scale Chemical Database for Ligand Discovery.ZINC20-A 免费超大尺度化学数据库,用于配体发现。
J Chem Inf Model. 2020 Dec 28;60(12):6065-6073. doi: 10.1021/acs.jcim.0c00675. Epub 2020 Oct 29.
8
New Trends in Virtual Screening.虚拟筛选的新趋势
J Chem Inf Model. 2020 Sep 28;60(9):4109-4111. doi: 10.1021/acs.jcim.0c01009.
9
An open-source drug discovery platform enables ultra-large virtual screens.一个开源药物发现平台可实现超大规模虚拟筛选。
Nature. 2020 Apr;580(7805):663-668. doi: 10.1038/s41586-020-2117-z. Epub 2020 Mar 9.
10
D3R grand challenge 4: blind prediction of protein-ligand poses, affinity rankings, and relative binding free energies.D3R 大分子对接挑战赛 4:蛋白质-配体构象、亲和力排序和相对结合自由能的盲态预测。
J Comput Aided Mol Des. 2020 Feb;34(2):99-119. doi: 10.1007/s10822-020-00289-y. Epub 2020 Jan 23.

CACHE(计算命中发现实验的批判性评估):一项公私合作的基准测试计划,旨在推动用于命中发现的计算方法的开发。

CACHE (Critical Assessment of Computational Hit-finding Experiments): A public-private partnership benchmarking initiative to enable the development of computational methods for hit-finding.

作者信息

Ackloo Suzanne, Al-Awar Rima, Amaro Rommie E, Arrowsmith Cheryl H, Azevedo Hatylas, Batey Robert A, Bengio Yoshua, Betz Ulrich A K, Bologa Cristian G, Chodera John D, Cornell Wendy D, Dunham Ian, Ecker Gerhard F, Edfeldt Kristina, Edwards Aled M, Gilson Michael K, Gordijo Claudia R, Hessler Gerhard, Hillisch Alexander, Hogner Anders, Irwin John J, Jansen Johanna M, Kuhn Daniel, Leach Andrew R, Lee Alpha A, Lessel Uta, Morgan Maxwell R, Moult John, Muegge Ingo, Oprea Tudor I, Perry Benjamin G, Riley Patrick, Rousseaux Sophie A L, Saikatendu Kumar Singh, Santhakumar Vijayaratnam, Schapira Matthieu, Scholten Cora, Todd Matthew H, Vedadi Masoud, Volkamer Andrea, Willson Timothy M

机构信息

Structural Genomics Consortium, University of Toronto, Toronto, Ontario, Canada.

Ontario Institute for Cancer Research, Toronto, Ontario, Canada.

出版信息

Nat Rev Chem. 2022 Apr;6(4):287-295. doi: 10.1038/s41570-022-00363-z. Epub 2022 Feb 15.

DOI:10.1038/s41570-022-00363-z
PMID:35783295
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9246350/
Abstract

One aspirational goal of computational chemistry is to predict potent and drug-like binders for any protein, such that only those that bind are synthesized. In this Roadmap, we describe the launch of Critical Assessment of Computational Hit-finding Experiments (CACHE), a public benchmarking project to compare and improve small molecule hit-finding algorithms through cycles of prediction and experimental testing. Participants will predict small molecule binders for new and biologically relevant protein targets representing different prediction scenarios. Predicted compounds will be tested rigorously in an experimental hub, and all predicted binders as well as all experimental screening data, including the chemical structures of experimentally tested compounds, will be made publicly available, and not subject to any intellectual property restrictions. The ability of a range of computational approaches to find novel binders will be evaluated, compared, and openly published. CACHE will launch 3 new benchmarking exercises every year. The outcomes will be better prediction methods, new small molecule binders for target proteins of importance for fundamental biology or drug discovery, and a major technological step towards achieving the goal of Target 2035, a global initiative to identify pharmacological probes for all human proteins.

摘要

计算化学的一个理想目标是为任何蛋白质预测出强效且类似药物的结合物,从而只合成那些具有结合能力的物质。在本路线图中,我们描述了计算命中发现实验关键评估(CACHE)项目的启动,这是一个公共基准测试项目,旨在通过预测和实验测试的循环来比较和改进小分子命中发现算法。参与者将针对代表不同预测场景的新型且具有生物学相关性的蛋白质靶点预测小分子结合物。预测出的化合物将在一个实验中心进行严格测试,所有预测出的结合物以及所有实验筛选数据,包括经实验测试化合物的化学结构,都将公开提供,且不受任何知识产权限制。一系列计算方法寻找新型结合物的能力将得到评估、比较并公开发表。CACHE每年将开展3项新的基准测试活动。其成果将是更好的预测方法、针对对基础生物学或药物发现具有重要意义的靶点蛋白质的新型小分子结合物,以及朝着实现“2035目标”迈出的重大技术一步,“2035目标”是一项全球倡议,旨在为所有人类蛋白质鉴定出药理学探针。