# 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, weighted holistic atom localization and entity shape (WHALES) descriptors [72], which represent the partial charge distribution and three-dimensional shape of compounds, were used to identify initial compounds similar to (\(-\))-galantamine. Then, the compounds most similar to (\(-\))-galantamine were further evaluated using the SOM-based prediction of drug equivalence relationships (SPiDER) [73] and target inference generator (TIGER) [74] applications, which predict target proteins of a given compound. Because (\(-\))-galantamine reversibly inhibits acetylcholinesteraseerase (AChE), the purpose of this process was to identify AChE inhibitors. Finally, this computational pipeline identified eight compounds with bioactivity on at least one of the macromolecular targets of (\(-\))-galantamine, showing different polypharmacological properties. Inference about the origin of NPs provide insight into our understanding of the physicochemical properties of NPs. [PERSON] [75] developed a machine learning-based classification model to classify NPs into three classes: marine natural products (MNPs), terrestrial natural products (TNPs), and NPs that appear in both the terrestrial and marine environments. Briefly, molecular structures were retrieved from the Reaxys(r) database (Elsevier Information Systems GmbH; [[https://www.reaxys.com](https://www.reaxys.com)]([https://www.reaxys.com](https://www.reaxys.com)) (accessed on 10 December 2021)) and were filtered appropriately. A total of 22,398 NPs were defined, which comprised 10,790 MNPs, 10,857 TNPs, and 761 as both. The data set was randomly divided into a training set of 15,676 NPs and a test set of 6722 NPs. Then, classification models were constructed using random forest, support vector machine, and multilayer perceptron networks with an overall predictive accuracy of 81% for the test set. Although all these examples only represent limited use cases of machine learning algorithms, these examples show how machine learning techniques can be utilized for NP-related research. Machine Learning Application of NP or NP-Like Chemical Compounds Discovery for Cardiovascular and Metabolic Diseases  
Machine learning approaches may identify active molecules derived from NPs to improve human health in numerous areas of interest through methodologies described in previous sections. From a metabolic disease perspective, there are many attempts to discover NP mimetics that target specific proteins. For example, PPARy is a master regulator protein of metabolism which is identified to be involved in metabolic diseases such as chronic inflammation, obesity, and diabetes when its function is lost or dysregulated [76; 77]. [PERSON] et al. identified several active compound structures toward PPARy that are related to cinnamic acid and trvacillin acid through non-linear Bayesian regression with Guassian processes and virtual screening [78]. Another study suggests caffeine as an anti-diabetic NP through an iterative stochastic elimination (ISE) model built on in-house data and confirmation with wet experiments [79]. Recently, a deep learning based model using latent knowledge, molecule to target interactions, and compound property as heterogeneous input features predicted several NPs or NP-derivatives for various cardiovascular diseases (CVD): ergosterol and arginine for heart failure; reserpine, norepinephrine, octopamine, and digitoxin for hypertension; resveratrol for myocardial infarction; and aspirin and agmatine for stroke [80]. In an extensive perspective of metabolic syndrome and CVD, inflammation is a biological process that is critical in the first line of defense of the host immune system, and dysregulated inflammatory responses can lead to cardiovascular and metabolic diseases [81; 82]. A recent study [83] identified capsaicin, hypaphorine, and moupinamide among NPs as anti-inflammatory drug candidates using the ISE model, and the results were further supported by the literature.