Image-based profiling for drug discovery: due for a machine-learning upgrade?
Image-based profiling for drug discovery: due for a machine-learning upgrade?
复制标题
基于图像的药物发现分析：机器学习升级？
DOI：
10.1038/s41573-020-00117-w
复制
发表时间：
2021-03
期刊：
影响因子：
--
通讯作者：
Carpenter AE
中科院分区：
文献类型：
--
作者：
Chandrasekaran SN;Ceulemans H;Boyd JD;Carpenter AE
基于图像的轮廓分析是一种成熟的策略，通过该策略，生物图像中存在的丰富信息被减少到多维轮廓，即提取的基于图像的特征的集合。这些图谱可以被挖掘出相关的模式，揭示出意想不到的生物活性，这对药物发现过程中的许多步骤都很有用。这些应用包括鉴定与疾病相关的可筛选表型，了解疾病机制和预测药物的活性，毒性或作用机制。其中一些应用最近已经得到验证，并已进入学术界和制药行业的生产模式。其中一些在实践中产生了令人失望的结果，但现在由于改进了机器学习策略，更好地利用基于图像的信息，这些方法重新引起了人们的兴趣。尽管挑战依然存在，但新的计算技术，如深度学习和单细胞方法，可以更好地捕捉图像中的生物信息，有望加速药物发现。基于图像的轮廓分析是一种挖掘生物图像中丰富信息的策略。Carpenter及其同事讨论了机器学习的应用如何重新引起人们对药物发现过程各个方面的基于图像的分析的兴趣，从了解疾病机制到预测药物的活性或作用机制。
Image-based profiling is a maturing strategy by which the rich information present in biological images is reduced to a multidimensional profile, a collection of extracted image-based features. These profiles can be mined for relevant patterns, revealing unexpected biological activity that is useful for many steps in the drug discovery process. Such applications include identifying disease-associated screenable phenotypes, understanding disease mechanisms and predicting a drug’s activity, toxicity or mechanism of action. Several of these applications have been recently validated and have moved into production mode within academia and the pharmaceutical industry. Some of these have yielded disappointing results in practice but are now of renewed interest due to improved machine-learning strategies that better leverage image-based information. Although challenges remain, novel computational technologies such as deep learning and single-cell methods that better capture the biological information in images hold promise for accelerating drug discovery. Image-based profiling is a strategy to mine the rich information in biological images. Carpenter and colleagues discuss how the application of machine learning is renewing interest in image-based profiling for all aspects of the drug discovery process, from understanding disease mechanisms to predicting a drug’s activity or mechanism of action.
登录
查看更多内容
影响因子：
9.9
作者：
Breinig M;Klein FA;Huber W;Boutros M
通讯作者：
Boutros M
影响因子：
14.8
作者：
Bruni G;Rennekamp AJ;Velenich A;McCarroll M;Gendelev L;Fertsch E;Taylor J;Lakhani P;Lensen D;Evron T;Lorello PJ;Huang XP;Kolczewski S;Carey G;Caldarone BJ;Prinssen E;Roth BL;Keiser MJ;Peterson RT;Kokel D
通讯作者：
Kokel D
影响因子：
3.2
作者：
Bolognesi, Maddalena Maria;Manzoni, Marco;Cattoretti, Giorgio
通讯作者：
Cattoretti, Giorgio
影响因子：
48
作者：
Caicedo JC;Cooper S;Heigwer F;Warchal S;Qiu P;Molnar C;Vasilevich AS;Barry JD;Bansal HS;Kraus O;Wawer M;Paavolainen L;Herrmann MD;Rohban M;Hung J;Hennig H;Concannon J;Smith I;Clemons PA;Singh S;Rees P;Horvath P;Linington RG;Carpenter AE
通讯作者：
Carpenter AE
影响因子：
5.8
作者：
Boyd, Joseph C.;Pinheiro, Alice;Walter, Thomas
通讯作者：
Walter, Thomas