Recent applications of artificial intelligence in RNA-targeted small molecule drug discovery

Figures & data

Figure 1. Artificial intelligence (AI) applications and tools in RNA-targeted small molecule drug discovery. Successful applications of AI tools in the various stages of drug discovery have been reported. CNN, convolutional neural network; DNN, deep neural network; GNN, graph neural network; Lasso, least absolute shrinkage and selection operator; LDA, linear discriminant analysis; LSTM, long short-term memory; MFFNN, multilayer feed-forward neural network; MLP, multilayer perceptron; MRF, Markov random fields; QSAR/QSPR, quantitative structure – activity/property relationship; SVM, support vector machine.

Table 1. Advantages and disadvantages of the ML and DL algorithms used in RNA-targeted small molecule drug discovery.

Algorithm	Type	Use	Advantages	Disadvantages
Decision trees	Supervised	Regression	Easy to understand and interpret output Most suitable for visual representation Requires little data pre-processing Non-parametric	Susceptible to overfitting Very sensitive to small changes in data
Extremely randomized trees	Ensemble	Classification, regression	Can handle datasets with noisy data Useful for high-dimensional data Low bias Faster than random forest	Can be difficult to implement Need to remove irrelevant features via a pre-processing step
Gradient boosting	Supervised	Classification, regression	Relatively faster to train particularly on large datasets Can handle various feature types	Susceptible to overfitting Computationally expensive Difficult to interpret output
Hierarchical clustering	Unsupervised	Clustering	Robust, flexible, and scalable Easy to apply and to interpret output Produces superior results compared to other clustering algorithms	Sensitive to outliers Computationally expensive Relies on the user’s judgment to evaluate the quality of the output
k-nearest neighbors	Supervised	Classification, regression	Simple to implement Multiple applications User defined distance metric	Susceptible to overfitting Slow predictions Model size increases as more new data is incorporated
Least absolute shrinkage and selection operator (Lasso)	Supervised	Regression	Computationally efficient Simple to implement Automated selection of features	Estimates produced may be unstable Difficult to run tests to check for errors
Linear regression	Supervised	Regression	Simple to implement and easy to interpret output Useful for quantifying relationships and making predictions	Susceptible to overfitting Linear assumption may not accurately capture the true relationship
Linear discriminant analysis	Supervised	Dimensionality reduction, classification	Simple and fast to implement	Requires normal distribution assumption on features May not be appropriate to use when there are few variables
Logistic regression	Supervised	Classification	Simple to implement and very efficient to train Can give predictions about the importance of each feature Less susceptible to overfitting	Linear assumption between dependent and independent variables Difficult to capture complex relationships
Multiple instance learning	Supervised	Classification	Can handle weakly labeled data Can learn from the relationship between a group of instances and their label	Performance dependent on quality of labeling Difficult to interpret output
Naïve Bayes	Supervised	Classification	Simple to implement Fast, efficient, and has low storage requirements Can handle high-dimensional data	Zero frequency can occur when a variable does not exist in the training dataset Core assumptions can be unrealistic, resulting in poor classification
Neural networks	Supervised	Classification, regression	Any data that can be converted to numeric data can be used Suitable for non-linear data with large number of data points Fast predictions	Black boxes Computationally expensive Dependent on large training data
Random forest	Ensemble	Classification, regression	Robust Works well with large datasets and on different data types	Complex and computationally expensive Sampling of subsets may introduce bias
Support vector machine (SVM)	Supervised	Classification, regression	Can handle high-dimensional data Performs well with small datasets Able to model non-linear decision boundaries	Computationally expensive when the dataset is large Sensitive to the choice of parameters

Table 2. Data repositories containing ncRNA – disease associations*.

Repository	No. of ncRNAs*	No. of diseases	Description	URL	References
CLC	122	29	Manually curated catalog of lncRNAs that have been implicated in cancer; Includes GENCODE identifiers	https://www.gold-lab.org/clc	[15]
dbDEMC v3.0	3,268	40	Database of differentially expressed miRNAs identified in human cancers; Constructed by mining literature with high-throughput experimentation	https://www.biosino.org/dbDEMC	[16]
EVLncRNAs v2.0	4,010 (124 species)	1082	Resource containing curated information on sequence, functional, and phenotypic information of experimentally validated lncRNAs from various species	https://www.sdklab-biophysics-dzu.net/EVLncRNAs2/	[17]
HMDD v4.0	1,817	2,360	Database containing information on human miRNAs and their disease associations supported by experimentally derived genetic and epigenetic data	http://www.cuilab.cn/hmdd	[18]
Lnc2Cancer v3.0	2,659	216	Manually curated database providing associations between lncRNAs or circRNAs with human cancers; Includes experimentally supported information regarding the regulatory mechanisms, biological functions, and clinical applications of lncRNAs and circRNAs	http://bio-bigdata.hrbmu.edu.cn/lnc2cancer	[19]
LncRNADisease v2.0	16,798	566	Database with experimentally supported and predicted ncRNA – disease associations obtained by manual curation of literature	http://www.rnanut.net/lncrnadisease/	[20]
lncRNASNP v3.0	438,104	129,513	Resource providing information on SNPs in lncRNAs from eight eukaryotic species	http://gong_lab.hzau.edu.cn/lncRNASNP3/	[21]
miR2Disease	349	163	Manually curated database containing information on miRNA – disease associations, including miRNA expression patterns in the disease state and detection method for miRNA expression	http://www.mir2disease.org	[22]
miRCancer	57,984	196	Database containing miRNA – cancer associations obtained by text mining of	http://mircancer.ecu.edu	[23]
RNADisease v4.0 (formerly MNDRv3.0)	3,400,000	4,090	Formerly known as MNDR v3.0; Repository for RNA – disease association and includes a comprehensive analysis of RNAs from HTS data of cancer and normal samples	http://www.rnadisease.org/	[24]
TANRIC	>12,000	20	Resource providing clinical and other molecular data supporting the role of lncRNAs in human cancers	https://ibl.mdanderson.org/tanric/_design/basic/main.html	[25]

*Abbreviations used: CLC, cancer lncRNA census; circRNA, circular RNA; dbDEMC, database of differentially expressed microRNAs in human cancers; EVLncRNA, experimentally validated functional lncRNAs; HMDD, the human microRNA disease database; HTS: high-throughput screening; lncRNA, long non-coding RNA; miRNA, microRNA; MNDR, mammalian ncRNA-disease repository; ncRNA, non-coding RNA; TANRIC, the atlas of noncoding RNAs in cancer.

Table 3. RNA secondary and tertiary structure prediction tools that employs AI methods*.

Prediction tool	Method	URL	References
Secondary structure prediction
AdaptiveLSTMRNA	DL	https://eie.usts.edu.cn/prj/AdaptiveLSTMRNA/index.html	[38]
CONTRAfold	CLLM	http://contra.stanford.edu/contrafold/	[39]
ContextFold	SP and PA	https://www.cs.bgu.ac.il/~negevcb/contextfold/	[40]
EternaFold	Multitask	https://github.com/eternagame/EternaFold	[41]
KNetFold	KNN	http://knetfold.abcc.ncifcrf.gov/	[42]
MXfold	SSVM	https://github.com/mxfold/mxfold	[43]
MXfold2	SSVM, DL	https://github.com/mxfold/mxfold2	[44]
SPOT-RNA	DL	https://github.com/jaswindersingh2/SPOT-RNA	[45]
Tertiary structure prediction
ARES	DL	http://drorlab.stanford.edu/ares.html	[46]
BARNACLE	Bayesian network	http://sourceforge.net/projects/barnacle-rna/	[47]
DeepFoldRNA	DL	https://zhanggroup.org/DeepFoldRNA/	[48]
E2Efold-3D	DL	https://github.com/ml4bio/E2Efold-3D	[49]
JAR3D	SCFG/MRF	http://rna.bgsu.edu/data/jar3d/models https://github.com/BGSU-RNA/JAR3D	[50]
metaRNAmodules	Bayesian network	https://rth.dk/resources/mrm/index.php	[51]
RMDetect	Bayesian network	https://github.com/chichaumiau/RMDetect	[52]
RNA3DCNN	CNN	https://github.com/lijunRNA/RNA3DCNN	[53]
TreeFolder	CRF	Not available	[54]

*Abbreviations used: AI, artificial intelligence; CRF, conditional random fields; CLLM, conditional log-linear model; CNN, convolutional neural network; DL, deep learning; KNN, k-nearest neighbor; PA, online passive-aggressive learning; SCFG/MRF, stochastic context-free grammars and Markov random fields; SP, discriminative structured-prediction learning; SSVM, structured support vector machine.

Figure 2. (a) Workflow for the DeepFoldRNA method [48]. (b) Model of Saccharomyces cerevisiae P-site tRNA generated by DeepFoldRNA (green) superimposed with the native structure (blue, PDB ID: 6Q8Y). L-BFGS, limited-memory Broyden-Fletcher-Goldfarb-Shanno; RMSD, root mean square deviation.

Figure 3. Chemical substructures enriched in selective binders in RNA-focused libraries. The Merck group adapted the (a) Automated ligand identification system as a method to screen small molecule libraries against RNAs [87]. Using an ML approach and the results of their screening, they built an RNA-focused library and found (b) 12 substructures enriched in RNA binders. Likewise, the Schneekloth group built an RNA-focused library, ROBIN, from their previous (c) Small molecule microarray screening projects and identified (d) Ten substructures enriched in selective RNA binders [88]. RP-HPLC, reversed-phase high-performance liquid chromatography.

Figure 4. Application of AI in the optimization of small molecules that target (a) RNA hairpin 91 (red) in the ribosomal peptidyl transfer center of mycobacterium tuberculosis (PDB ID: 5O61). (b) Secondary structure of hairpin 91. (c) Chemical structures, ribosome activity, and inhibitory concentration (IC50) of the small molecules that were designed based on the insights obtained from the machine and deep learning approaches [100].

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