# A Brief Review of Machine Learning-Based Bioactive Compound Research
## 2 Cheminformatics, Bioinformatics, and Databases for Machine Learning  
Cheminformatics is a research field that achieves desired goals by utilizing various information on chemicals, including 2D and 3D structures of chemical species. In general, the structural information of chemicals can be standardized and converted to machine-readable formats such as a simplified molecular-input line-entry system (SMILES) [29]. SMILES strings can be stored electronically into a database and then utilized through a variety of computational algorithms to identify, evaluate, and predict properties of chemicals. For example, the PubChem database [30] currently contains more than 100 million unique chemical structures extracted from contributed PubChem substance records ([[https://pubchemdocs.ncbi.nlm.nih.gov/statistics](https://pubchemdocs.ncbi.nlm.nih.gov/statistics)]([https://pubchemdocs.ncbi.nlm.nih.gov/statistics](https://pubchemdocs.ncbi.nlm.nih.gov/statistics)) (accessed on 10 December 2021)). There are other databases for chemical collections such as ChEMBL ([[https://www.ebiac.uk/chembl/](https://www.ebiac.uk/chembl/)]([https://www.ebiac.uk/chembl/](https://www.ebiac.uk/chembl/)) (accessed on 10 December 2021)) [31] and ZINC15 ([[https://zinc15.docking.org/](https://zinc15.docking.org/)]([https://zinc15.docking.org/](https://zinc15.docking.org/)) (accessed on 10 December 2021)) [32]. Therefore, integrating cheminformatics methods and databases into existing drug discovery procedures has become indispensable for successful drug development. Accordingly, it is not at all surprising that many initial hits (a hit is defined as a molecule that shows the desired type of activity in an experimental screening assay) and further optimization for lead compounds can be performed via computational methods, such as computer-aided drug design (CADD) approaches. There are many successful applications of CADD in the development of currently available commercial drugs. For example, zanamivir (Relenza), a neuraminidase inhibitor that is used as a prophylaxis to treat the symptoms of influenza, was approved by the United States Food and Drug Administration (FDA) in 1999 [33] and is one of the most successful outcomes of CADD. The identification of new bioactive compounds for a given target protein requires not only chemical information, but also detailed molecular information about the target protein, such as amino acid sequence, domain, and three-dimensional structural information. Fortunately, this kind of information has been accumulated experimentally and computationally over several decades [34]. Conceptually, genome (DNA sequences), RNA sequences, protein sequences, and chemical structure information that is fundamentally similar between individuals within the same species can be defined as static information. On the other hand, molecular information that can be dynamically changed between individuals and/or conditions, such as DNA sequence variants (DNA mutations), gene expressions, and protein amounts, can be defined as dynamic information (Figure 1). Rapid innovation in high-throughput screening methods and large-scale expansion into virtually all molecular and clinical domains has dramatically expanded the scale of this molecular data over the past decade [35]. Therefore, bioinformatics has emerged to analyze this vast amount of molecular data. The terms \"cheminformatics\" and \"bioinformatics\" are used interchangeably as the two fields deal primarily with computer-readable data from small chemical compounds, metabolites, RNAs, DNAs, and/or proteins. As mentioned above, cheminformatics mainly focuses on various chemical information, while bioinformatics primarily deals with DNA, RNA, and protein information.