From machine learning to deep learning: progress in machine intelligence for rational drug discovery.

By Lu Zhang, J. Tan, Dan Han, Hao Zhu. Drug Discovery Today, 2017

587 citations recorded by the source corpus.

Subfields: Biophysics, Computational Theory and Mathematics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/3ad59b205830046a154351412b2016793921f2d2

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Citations in the corpus

The rise of artificial intelligence in healthcare applications (2020, background): “This includes drug compound property and activity prediction, de novo design of drug compounds, drug (cid:1) receptor interactions, and drug reaction prediction [16].”
Next-Generation Machine Learning for Biological Networks. (2018, unknown): “For additional insight into machine learning in the context of biological research, we refer the reader to several excellent review articles (Califano et al., 2012; Pe’er and Hacohen, 2011; Zhang et al., 2017).”
Machine learning applications in drug development (2019, methodology): “…discovery: for example, drug candidate identiﬁcation via molecule docking, in order to predict and preselect interesting drug-target interactions for further research [43]; and protein engineering, that is, de novo molecular design of proteins with speciﬁc expected binding or motif functions [44].”
Artificial Intelligence (AI) and Internet of Medical Things (IoMT) Assisted Biomedical Systems for Intelligent Healthcare (2022, unknown): “regression and has medium accuracy.”
Potential of quantum computing for drug discovery (2018, methodology): “…with the increasing amount of activity data available nowadays, has motivated the use of nonlinear statistical models for QSAR, most of them employing artiﬁcial neural networks and other machine learning techniques, such as support vector machines (SVM), random forest, and decision trees [42].”
Artificial intelligence to deep learning: machine intelligence approach for drug discovery (2021, background): “Thus, the biggest question that arises in front of pharmaceutical companies is managing the cost and speed of the process [5].”
Tackling Climate Change with Machine Learning (2019, background): “For instance, it is important to ensure that low-carbon variable generators produce energy as efficiently and profitably as For discussions and examples of different types of advanced electricity markets, see [74–77].”
Artificial intelligence in drug discovery and development (2020, unknown): “…tools have been utilized for the identiﬁcation of potential drug candidates and have evolved into AI-based QSAR approaches, such as linear discriminant analysis (LDA), support vector machines (SVMs), random forest (RF) and decision trees, which can be applied to speed up QSAR analysis [27–29].”