# A Brief Review of Machine Learning-Based Bioactive Compound Research
## 2 Cheminformatics, Bioinformatics, and Databases for Machine Learning
Many DNA and RNA nucleotides and/or protein databases are publicly available, including the nucleotide database maintained by the National Center for Biotechnology Information (NCBI) ([[https://www.ncbi.nlm.nih.gov/nucleotide/](https://www.ncbi.nlm.nih.gov/nucleotide/)]([https://www.ncbi.nlm.nih.gov/nucleotide/](https://www.ncbi.nlm.nih.gov/nucleotide/)) (accessed on 10 December 2021)) [36], the UniProt database ([[https://www.uniprot.org/](https://www.uniprot.org/)]([https://www.uniprot.org/](https://www.uniprot.org/))  
Figure 1: Various molecular and phenotypic data can be integrated to build a machine learning model. DNA, RNA, and protein, as well as chemical information, can be represented as strings (**top left**). This kind of fundamental molecular information can be defined as static information (deterministic), which shows little or no difference between individuals. On the other hand, phenotypic information (e.g., clinical data; **bottom left**) and high-throughput screening data (e.g., RNA-seq) produce dynamic information of molecules that can be integrated and represented as matrices (e.g., rows represent features and columns indicate samples, or vice versa) (**middle**). A collection of properly labelled matrices (**top right**) is used as input data to build a machine learning model for a particular purpose (**bottom right**). (accessed on 10 December 2021)) [37], and the Protein Data Bank (PDB) database ([[https://www.rcsb.org/](https://www.rcsb.org/)]([https://www.rcsb.org/](https://www.rcsb.org/)) (accessed on 10 December 2021)) [38]. In addition, there are several web-based ready-to-use databases related to bioactive compounds, which have been well described in a review [39]. These databases mostly contain sequence (string) information of given molecules, obtained from various molecular experiments. For example, DNA and RNA can be represented as sequences of A, G, C, and T (or U for RNA), while protein can be denoted as strings of twenty letters where a single letter indicates an amino acid. In contrast to this static molecular information, there are big databases that contain massive amounts of dynamic molecular information generated by high-throughput screening approaches, such as next-generation sequencing (NGS). Compared to the static molecular information mentioned above, NGS-based high-throughput screening systems have been generating molecular dynamic data, such as RNA (transcription or gene expression), DNA (single nucleotide variant, SNV; insertion and deletion, INDEL; copy number variation, CNV; structural variation, SV), and DNA or RNA binding proteins in various cells, tissues, and even at the single cell level. Currently, more than 10 petabytes of raw sequencing data ([[https://trace.ncbi.nlm.nih.gov/Traces/sra](https://trace.ncbi.nlm.nih.gov/Traces/sra)]([https://trace.ncbi.nlm.nih.gov/Traces/sra](https://trace.ncbi.nlm.nih.gov/Traces/sra)) (accessed on 10 December 2021)) can be downloaded from the Sequence Read Archive (SRA) website. The majority of these data is from gene expression profiling experiments, such as RNA-seq. For example, changes in the expression level of approximately 60,000 genes (or 230,000 transcripts) in human cells, with drug treatment, can be measured with an RNA-seq approach. With 100 samples, the total number of values would be 6,000,000 for genes (or 23,000,000 for transcripts), and this information can be represented as a matrix (Figure 1). However, since most genes are not normally expressed, the number of values will typically be reduced to less than one-third of that number. Then, the mode of action (MOA) prediction of the given drug can be achieved by analyzing this dynamic information using various bioinformatic algorithms and/or databases. In general, a typical MOA prediction can be conducted using gene ontology (GO) analysis and/or gene set enrichment analysis (GSEA).