# A Brief Review of Machine Learning-Based Bioactive Compound Research
###### Abstract  
Bioactive compounds are often used as initial substances for many therapeutic agents. In recent years, both theoretical and practical innovations in hardware-assisted and fast-evolving machine learning (ML) have made it possible to identify desired bioactive compounds in chemical spaces, such as those in natural products (NPs). This review introduces how machine learning approaches can be used for the identification and evaluation of bioactive compounds. It also provides an overview of recent research trends in machine learning-based prediction and the evaluation of bioactive compounds by listing real-world examples along with various input data. In addition, several ML-based approaches to identify specific bioactive compounds for cardiovascular and metabolic diseases are described. Overall, these approaches are important for the discovery of novel bioactive compounds and provide new insights into the machine learning basis for various traditional applications of bioactive compound-related research.  
bioactive compound; natural product; machine learning; bioinformatics; cheminformatics; chemical space; cardiovascular disease; metabolic disease +
Footnote †: journal: Medical  
processing units (GPUs) and tensor processing units (TPUs) in recent years, it has become possible to navigate a chemical subspace with desired properties. There are many successful case studies of the application of ML for various purposes. For example, recent studies identified many NP-like small molecules through the exploration of the chemical space of NPs [15; 16; 17]. In addition, ML models can accurately discriminate wild from farmed salmon based on gas chromatography with flame ionization detector (GC-FID) fatty acid profiles [18]. Therefore, with the help of state-of-the-art machine learning techniques, a variety of tasks such as discovering novel bioactive compounds with desired properties by training data measured through various large-scale experiments have become possible.  
Recently, many ML algorithms are being applied to drug discovery, including hit identification, lead optimization, and clinical development stages [19; 20; 21]. Their goal is to accelerate the discovery of drugs more quickly and efficiently. For example, the traditional hit identification process requires labor-intensive, massive high throughput screening (HTS) that consumes many resources. To overcome this process, ML- or DL-based drug-target interaction (DTI) algorithms are used to identify small molecules that can bind to the desired target proteins using either sequence-based or 3D structure-based data [22; 23; 24; 25]. To evaluate the properties of a given compound, cheminformatics approaches, such as the quantitative structure-activity relationship (QSAR), a computational modeling method for predicting the relationship between structural properties and biological activities of chemical compounds, are used widely, along with the prediction of absorption, distribution, metabolism, excretion, and toxicity (ADMET) [26; 27; 28]. Collectively, these computational predictions are effective at reducing the time and cost required for drug development, and many appropriate applications can also be applied to NP-based drug or active molecule discovery, which is expected to accelerate such processes.  
In this review, we avoid discussing in-depth characteristics of machine learning techniques and the discovery and/or development of novel bioactive compounds, but rather offer an overview of recent research trends toward the machine learning-driven prediction and evaluation of bioactive compounds based on actual cases.