Kinetic model derived from machine learning for accurate prediction of microalgal hydrogen production via conversion from low thermally pre-treated palm kernel expeller waste

Sobri, Mohamad Zulfadhli Ahmad; Khoo, Kuan Shiong; Sahrin, Nurul Tasnim; Ardo, Fatima Musa; Ansar, Sabah; Hossain, Md. Sohrab; Kiatkittipong, Worapon; Lin, Cheng‐Fang; Ng, Hui-Suan; Zaini, Juliana; Bilad, Muhammad Roil; Lam, Man Kee; Lim, Jun Wei

doi:10.1016/j.chemosphere.2023.139526

Cited by 2 publications

(1 citation statement)

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“…ML greatly improves work efficiency and accuracy. Driven by increasing computer power and algorithmic advances, ML has become a powerful tool for finding data patterns 24 , and it is widely applied in various fields, such as biology 25 , 26 , medicine 27 , 28 , healthcare 29 , environment 30 , fuel cells 31 and energy 32 . ML methods can also be used to develop new AMPs.…”

Section: Introductionmentioning

confidence: 99%

Machine learning assisted rational design of antimicrobial peptides based on human endogenous proteins and their applications for cosmetic preservative system optimization

Yue,

Song,

Zhu

et al. 2024

Sci Rep

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Preservatives are essential components in cosmetic products, but their safety issues have attracted widespread attention. There is an urgent need for safe and effective alternatives. Antimicrobial peptides (AMPs) are part of the innate immune system and have potent antimicrobial properties. Using machine learning-assisted rational design, we obtained a novel antibacterial peptide, IK-16-1, with significant antibacterial activity and maintaining safety based on β-defensins. IK-16-1 has broad-spectrum antimicrobial properties against Escherichia coli, Staphylococcus aureus, Pseudomonas aeruginosa, and Candida albicans, and has no haemolytic activity. The use of IK-16-1 holds promise in the cosmetics industry, since it can serve as a preservative synergist to reduce the amount of other preservatives in cosmetics. This study verified the feasibility of combining computational design with artificial intelligence prediction to design AMPs, achieving rapid screening and reducing development costs.

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