# A Brief Review of Machine Learning-Based Bioactive Compound Research
## 7 Future Perspective  
As discussed in previous sections, ML technology can establish strategically faster studies and new opportunities to develop bioactive NPs. Despite the usefulness of ML methods, there are a few more topics that need to be considered for practical application.  
NPs are mostly generally recognized as safe (GRAS, [[https://www.fda.gov/food/food-ingredients-packaging/generally-recognized-safe-gras](https://www.fda.gov/food/food-ingredients-packaging/generally-recognized-safe-gras)]([https://www.fda.gov/food/food-ingredients-packaging/generally-recognized-safe-gras](https://www.fda.gov/food/food-ingredients-packaging/generally-recognized-safe-gras)) (accessed on 10 December 2021)) and food-grade substances and used as ingredients for a variety of health supple ments, functional food products, and cosmetic products. GRAS-listed NPs (i.e., berberine, canthaxanthin, eugenol, L-Glutamine, or limonene) can be directly applied to such products without any complex legal regulation, and this accessibility is a strength of NPs. In contrast, a downside of NPs is that their therapeutic efficacy is often low and limited compared to synthetic compounds. For example, phytoplankton coumarin is a plant-derived NP to defend plants from microbial infection and is studied as a bioactive NP for numerous disease indications [88]. However, coumarin itself needs to be better improved, or so-called optimized, as a derivative to achieve better effectiveness against bacterial infection, hypertension, or inflammation [88,89]. Therefore, the weight between using NPs of nature by themselves or further developing NP derivatives must be considered prior to using ML models.  
To process the lead optimization task more efficiently, many machine learning approaches have been studied recently: (i) atom modification reinforcement learning models that add or delete atoms or bonds [90,91], (ii) generative reinforcement learning which generates similar but modified structure [92], (iii) generative machine learning with controlled chemical properties that also generates similar modified structure with preserved predictive properties [93], and (iv) a 3D structure-based ligand design model that uses a 3D crystal structure of protein and ligand to generate novel molecules [94]. These applications demonstrated the possibility of lead optimization or de novo small molecule-focused machine learning methods. Their goals are to apply listed models to drug discovery, but the basis of these models is not limited to de novo synthesis or the lead optimization of new chemical entities. By adopting such methods, NPs can serve as \"seed\" or \"starting\" molecules and NP derivatives as resulting modified products with preserved chemical structure similarity.  
Conceptualization, K.K. and S.L.; investigation, J.P., B.R.B. and H.H.K.; writing--original draft preparation, K.K., J.P., B.R.B. and S.L.; writing--review and editing, K.K. and S.L.; visualization, J.P. and H.H.K.; supervision, K.K.; funding acquisition, K.K. All authors have read and agreed to the published version of the manuscript.  
This study was funded by a research grant from the Korea Food Research Institute (Project Number: E0210601-01). This work was supported by the National Research Foundation of Korea (NRF) grants funded by the Korean government (MSIT) (NRF-2019R1C1C1010385) and by a grant from the National R&D Program for Cancer Control, Ministry of Health and Welfare, Republic of Korea (1720100). This research was also supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2020R1A6A1A03043283). The Department of Microbiology was supported through the Research-Focused Department Promotion Project as a part of the University Innovation Support Program for Dankook University in 2021.  
Not applicable.