Machine Learning Tools to Assist the Synthesis of Antibacterial Carbon Dots

Abstract

Zirui Bian,1,* Tianzhe Bao,2,* Xuequan Sun,3,4 Ning Wang,1 Qian Mu,5 Ting Jiang,6 Zhongxiang Yu,6 Junhang Ding,2 Ting Wang,7 Qihui Zhou2 1Department of Bone, Huangdao District Central Hospital, Qingdao, People’s Republic of China; 2Qingdao Key Laboratory of Materials for Tissue Repair and Rehabilitation, School of Rehabilitation Sciences and Engineering, University of Health and Rehabilitation Sciences, Qingdao, People’s Republic of China; 3Weifang Eye Institute, Weifang Eye Hospital, Zhengda Guangming Eye Group, Weifang, People’s Republic of China; 4Zhengda Guangming International Eye Research Center, Qingdao Zhengda Guangming Eye Hospital, Qingdao University, Qingdao, People’s Republic of China; 5Department of Biomaterials, LongScience Biological (Qingdao) Co, LTD, Qingdao, People’s Republic of China; 6Heart Center, Qingdao Hiser Hospital Affiliated of Qingdao University (Qingdao Traditional Chinese Medicine Hospital), Qingdao University, Qingdao, People’s Republic of China; 7Department of Orthopaedic Surgery, The Affiliated Hospital of Qingdao University, Qingdao, People’s Republic of China*These authors contributed equally to this workCorrespondence: Qihui Zhou; Junhang Ding, Tel [phone], Email [email protected]; [email protected]: The emergence and rapid spread of multidrug-resistant bacteria (MRB) caused by the excessive use of antibiotics and the development of biofilms have been a growing threat to global public health. Nanoparticles as substitutes for antibiotics were proven to possess substantial abilities for tackling MRB infections via new antimicrobial mechanisms. Particularly, carbon dots (CDs) with unique (bio)physicochemical characteristics have been receiving considerable attention in combating MRB by damaging the bacterial wall, binding to DNA or enzymes, inducing hyperthermia locally, or forming reactive oxygen species.Methods: Herein, how the physicochemical features of various CDs affect their antimicrobial capacity is investigated with the assistance of machine learning (ML) tools.Results: The synthetic conditions and intrinsic properties of CDs from 121 samples are initially gathered to form the raw dataset, with Minimum inhibitory concentration (MIC) being the output. Four classification algorithms (KNN, SVM, RF, and XGBoost) are trained and validated with the input data. It is found that the ensemble learning methods turn out to be the best on our data. Also, ϵ-poly(L-lysine) CDs (PL-CDs) were developed to validate the practical application ability of the well-trained ML models in a laboratory with two ensemble models managing the prediction.Discussion: Thus, our results demonstrate that ML-based high-throughput theoretical calculation could be used to predict and decode the relationship between CD properties and the anti-bacterial effect, accelerating the development of high-performance nanoparticles and potential clinical translation. Keywords: carbon dots, machine learning, antibacterial, minimum inhibitory concentration, classification algorithms

Keywords

• carbon dots
• machine learning
• antibacterial
• minimum inhibitory concentration
• classification algorithms