# A Brief Review of Machine Learning-Based Bioactive Compound Research
## 4 How a Machine Learns from Data and Creates a Model for a Task Using Machine Learning Algorithms  
Machine learning (ML) is a research area encompassing computational methods of learning relationships from data without specifying them. Deep learning (DL) is a subset of ML [54] and is used in almost all areas because of the recent rapid development of hardware. In general, these approaches utilize a large number of quality-assured samples with many features. There are two types of ML methods, depending on dependent variables (or labels). Supervised learning methods learn the relationships between input features and the given labels of samples from a training dataset, whereas unsupervised learning algorithms infer patterns of input features without labels. We focus only on the supervised learning method in this review. To build a model that can accurately classify samples into classes (classification) or predict values for samples (regression), a dataset, which contains a large number of correctly labelled-samples with many features, should be divided into three sub-datasets, called training, validation, and test datasets. The training dataset is used for training to create an optimal model among the various models to be tested. Therefore, only the training dataset is used to train the models. In contrast, the validation set is a dataset used for testing multiple models with different hyper-parameters to find the optimal values for the best model. Through extensive training steps using training and validation datasets, the best model, which has been trained and validated, is selected. Then, the selected model's classification (or regression) performance is evaluated using the test dataset. Therefore, the proper way to introduce a ML-based model is to explicitly describe all processes and parameters as much as possible [55]. Because the performance of a ML-based model is solely based on training, validation, and test datasets as well as ML algorithms with parameters (e.g., random initializations, data shuffling, dropout, etc. ), it should be mandated to report all the information for the sake of transparency and reproducibility [56]. Details of machine learning have been well covered in recent reviews [57; 58; 59]. Numerous ML-based applications have been developed for a variety of purposes, including drug discovery. For example, [PERSON] et al. introduced a transformer-based deep learning model named MT-DTI that can accurately predict binding affinities between drugs and a target protein [60] (Figure 2). Briefly, this model utilized PubChem's approximately 97 million compounds to train key knowledge such as the syntax and semantics of small molecules using their SMILES information. The trained molecular transformer (MT) information was transferred into a drug target interaction (DTI) model (so-called transfer learning), and the final DTI model, called MT-DTI, was constructed using two DTI datasets, KIBA [61] and Davis [62]. These datasets contain experimentally validated interactions between kinases and kinase inhibitors. The MT-DTI model takes two input sequences, the SMILES of chemicals and the amino acid sequences of a target protein, to predict their binding affinities, and it can therefore be used to identify potential target proteins of known bioactive compounds or vice versa. Nuclear magnetic resonance (NMR) spectroscopic data can also be used to build ML-based classification models. [PERSON] et al. [63] built a model that can predict NP classes of chemicals using an ML-based approach with one-dimensional \({}^{13}\)C NMR spectroscopic data (Figure 2). In this study, the proposed approach does not rely on a typical peak matching task that requires available well-annotated NMR spectroscopic databases of chemicals, but they constructed a ML model that can predict the presence or absence of a particular NP family from the \({}^{13}\)C NMR spectra.