Learning Drug Functions from Chemical Structures with Convolutional Neural Networks and Random Forests

Meyer, Jesse G.; Liu, Shengchao; Miller, Ian; Coon, Joshua J.; Gitter, Anthony

doi:10.1021/acs.jcim.9b00236

Cited by 73 publications

(48 citation statements)

References 72 publications

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“…CNN has been used to analyse chemical images to obtain insight into drug therapeutic functions. 21 For example, AtomNet predicts the binding affinity of small molecules to proteins on the basis of the structural information extracted by CNN. 22 Biological sequences are another widely explored type of data for drug repurposing.…”

Section: Ai Algorithms and Recent Advancements Deep Learning Architecmentioning

confidence: 99%

Artificial intelligence in COVID-19 drug repurposing

Zhou

Wang

Tang

et al. 2020

The Lancet Digital Health

431

296

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Drug repurposing or repositioning is a technique whereby existing drugs are used to treat emerging and challenging diseases, including COVID-19. Drug repurposing has become a promising approach because of the opportunity for reduced development timelines and overall costs. In the big data era, artificial intelligence (AI) and network medicine offer cutting-edge application of information science to defining disease, medicine, therapeutics, and identifying targets with the least error. In this Review, we introduce guidelines on how to use AI for accelerating drug repurposing or repositioning, for which AI approaches are not just formidable but are also necessary. We discuss how to use AI models in precision medicine, and as an example, how AI models can accelerate COVID-19 drug repurposing. Rapidly developing, powerful, and innovative AI and network medicine technologies can expedite therapeutic development. This Review provides a strong rationale for using AI-based assistive tools for drug repurposing medications for human disease, including during the COVID-19 pandemic.

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Section: Ai Algorithms and Recent Advancements Deep Learning Architecmentioning

confidence: 99%

Artificial intelligence in COVID-19 drug repurposing

Zhou

Wang

Tang

et al. 2020

The Lancet Digital Health

431

296

View full text Add to dashboard Cite

show abstract

“…Peng et al and Hu et al employed convolutional neural network (CNN) models to predict drug-target interactions [ 22 , 23 ]. Finally, Meyer et al applied CNN and RF models to learn drug functions from chemical structures [ 24 ]. Virtual screening has excellent applications for in-silico screening, can accelerate the drug discovery process, and can reduce the costs and time associated with experimental work.…”

Section: Deep Learning For Drug Discoverymentioning

confidence: 99%

Deep learning model for virtual screening of novel 3C-like protease enzyme inhibitors against SARS coronavirus diseases

Kumari

Subbarao

2021

Computers in Biology and Medicine

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In the context of the recently emerging COVID-19 pandemic, we developed a deep learning model that can be used to predict the inhibitory activity of 3CLpro in severe acute respiratory syndrome coronavirus (SARS-CoV) for unknown compounds during the virtual screening process. This paper proposes a novel deep learning-based method to implement virtual screening with convolutional neural network (CNN) architecture. The descriptors represent chemical molecules, and these descriptors are input into the CNN framework to train a model and predict active compounds. When compared to other machine learning methods, including random forest, naive Bayes, decision tree, and support vector machine, the proposed CNN model’s evaluation of the test set showed an accuracy of 0.86, a sensitivity of 0.45, a specificity of 0.96, a precision of 0.73, a recall of 0.45, an F-measure of 0.55, and a ROC of 0.71. The CNN model screened 17 out of 918 phytochemical compounds; 60 out of 423 from the natural product NCI divset IV; 17,831 out of 112,267 from the ZINC natural product database; and 315 out of 1556 FDA-approved drugs as anti-SARS-CoV agents. Further, to prioritize drug-like compounds, Lipinski's rule of five was applied to screen anti-SARS-CoV compounds (excluding FDA-approved drugs), resulting in 10, 59, and 14,025 hit molecules. Out of 10 phytochemical compounds, 9 anti-SARS-CoV agents belonged to the flavonoid group. In conclusion, the proposed CNN model can prove useful for developing novel target-specific anti-SARS-CoV compounds.

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“…The random forest is actually a special method of bagging that using the decision tree as a model in bagging (Breiman, 2001;Meyer et al, 2019). First, the bootstrap method is used to generate m training sets, which is a set of samples.…”

Section: Multi-classifier Random Forestmentioning

confidence: 99%

TOOme: A Novel Computational Framework to Infer Cancer Tissue-of-Origin by Integrating Both Gene Mutation and Expression

Lang

Wang

et al. 2020

Front. Bioeng. Biotechnol.

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Metastatic cancers require further diagnosis to determine their primary tumor sites. However, the tissue-of-origin for around 5% tumors could not be identified by routine medical diagnosis according to a statistics in the United States. With the development of machine learning techniques and the accumulation of big cancer data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO), it is now feasible to predict cancer tissue-of-origin by computational tools. Metastatic tumor inherits characteristics from its tissue-of-origin, and both gene expression profile and somatic mutation have tissue specificity. Thus, we developed a computational framework to infer tumor tissue-of-origin by integrating both gene mutation and expression (TOOme). Specifically, we first perform feature selection on both gene expressions and mutations by a random forest method. The selected features are then used to build up a multi-label classification model to infer cancer tissue-of-origin. We adopt a few popular multiplelabel classification methods, which are compared by the 10-fold cross validation process. We applied TOOme to the TCGA data containing 7,008 non-metastatic samples across 20 solid tumors. Seventy four genes by gene expression profile and six genes by gene mutation are selected by the random forest process, which can be divided into two categories: (1) cancer type specific genes and (2) those expressed or mutated in several cancers with different levels of expression or mutation rates. Function analysis indicates that the selected genes are significantly enriched in gland development, urogenital system development, hormone metabolic process, thyroid hormone generation prostate hormone generation and so on. According to the multiple-label classification method, random forest performs the best with a 10-fold cross-validation prediction accuracy of 96%. We also use the 19 metastatic samples from TCGA and 256 cancer samples downloaded from GEO as independent testing data, for which TOOme achieves a prediction accuracy of 89%. The cross-validation validation accuracy is better than those using gene expression (i.e., 95%) and gene

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Learning Drug Functions from Chemical Structures with Convolutional Neural Networks and Random Forests

Cited by 73 publications

References 72 publications

Artificial intelligence in COVID-19 drug repurposing

Artificial intelligence in COVID-19 drug repurposing

Deep learning model for virtual screening of novel 3C-like protease enzyme inhibitors against SARS coronavirus diseases

TOOme: A Novel Computational Framework to Infer Cancer Tissue-of-Origin by Integrating Both Gene Mutation and Expression

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