# 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
To this end, they trained the model with continuous variables of NP spectra in the NAPROC-13 database [64], which contains (13)C spectral information of more than 18,000 unique NP structures and found that a XGBoost-based classifier accurately predicted a given NP into eight NP families (sesquiterpenoid, diterpenoid, triterpenoid, lignan, steroid, chroman, flavonoid, and alkaloid). String information, such as DNA nucleotides and/or protein amino acid sequences, can be used to build a ML model for a particular purpose. [PERSON] et al. [65] developed a machine learning bioinformatics method for predicting a natural product's antibiotic activity directly from the sequence of its biosynthetic gene cluster. In this study, they assembled a training dataset from the MiBIG (Minimum Information about Biosynthetic  
Figure 2: Machine learning-based models have been developed for different purposes. Various input data were used as independent variables (X, **left** panel) to predict dependent variables (Y, **right** panel). Five different examples are shown. The first example shows a model (called MT-DTI) that can predict the binding affinity (Y, the equilibrium dissociation constant known as \(K_{\text{D}}\)) between a given protein and ligand (small chemical compound). Strings of protein (amino acids) and ligand (SMILES) information (X) are used as input. The second model takes NMR data (X) as input and classifies them into known NP classes (Y). The third model analyzes genomic sequences called biosynthetic gene clusters (X) and predicts the likelihood that a natural product will have activity (Y) from those gene clusters. The fourth model takes a SMILE of a chemical of interest (X, e.g., natural product) and identifies similar small chemicals (Y) with desired multi-target profiles on disease-related targets. The last model detects biosynthetic gene clusters (Y) using a deep learning model called DeepBGC. Bacterial genome sequences are used as input (X). Gene Custer) database, comprising sequences of biosynthetic gene clusters (BGCs). They assembled a dataset of known BGCs paired with the activity of their products by representing the BGCs as vectors based on the number of times various gene annotations appeared in the cluster. Then, binary classification models for antibacterial, anti-Gram-positive, anti-Gram-negative, antifungal/antitumor/cytotoxic, antitumor/cytotoxic, and antifungal activities were constructed using this information as the training data set. The proposed binary classifiers can achieve up to 80% accuracy. Another study also built a ML model with the BGC information. [PERSON] et al. [66] present a deep learning strategy (DeepBGC) that offers reduced false positive rates in BGC identification and an improved ability to extrapolate and identify novel BGC classes, compared to existing machine-learning tools. DeepBGC is a deep learning approach that uses a Bidirectional Long Short-Term Memory (BiLSTM) RNN [67] and a word-embedding skip-gram neural network (pfam2 vec) such as word2 vec [68]. The input consists of amino acid sequences of protein family (Pfam) domains [69] represented by vectors, and the output is a sequence of values between 0 and 1, representing the prediction score of whether a given domain is part of a BGC. This implementation improved the accuracy of BGC detection in genome sequences and the ability to identify BGCs capable of encoding natural products with novel biological activities. The identification of novel NP-like chemicals with desired properties is of great interest in drug discovery. [PERSON] et al. [70] present a machine-learning workflow for the identification of NP mimetics with the desired polypharmacology. More than 3.3 million commercially available compounds were computationally screened to identify chemicals similar to (\(-\))-galantamine, a natural product-based drug approved by the US Food and Drug Administration (FDA) for the treatment of cognitive decline in mild to moderate Alzheimer's disease (AD) [71].