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. 2021 Nov 10;10(11):1376. doi: 10.3390/antibiotics10111376

Table 1.

Main Artificial Intelligence technologies used in the included studies.

Reference Characteristics Outcomes Technology
da Cunha et al., 2021 Detecting certain metabolic fingerprints through spectroscopy; using ML technology to analyze and further predict the mechanism of action and potency of different antibiotics Successfully predicted the mechanism of action; accurate estimation of the antibiotic potency ML + high-throughput Fourier-transform infrared spectroscopy
Zoffman et al., 2019 Searching, identifying, and predicting potency of compounds with a random forest model Assess phenotypic changes and antibacterial potency; predicted the phenotypic changes in compounds with identical and different mechanism of action ML-random forest model
Stokes et al., 2020 Using DL and NN to search databases and predict potential antimicrobial compounds, further empirically testing them Successfully combined AI technologies and clinical investigation; halicin displayed strong antibacterial properties DL + NN
Parvaiz et al., 2021 Using ML to search for and identify potential candidates possessing beta-lactamase inhibition quality Identified 74 compounds, out of which one showed great promise and further used ML in order to search for compounds structurally similar, concluding that all of the 28 additionally returned results had antibacterial activity ML-random forest model
Hamid et al., 2019 Used neural networks in order to distinguish between bacteriocin and con-bacteriocin sequences The algorithm can successfully predict and classify bacteriocins based on their sequence RNN
Fields et al., 2020 Used ML to design and test bacteriocin-derived compounds and further assess their antimicrobial activity The study designed and empirically tested compounds returned by the ML algorithm, with significant results ML
Badura et al., 2021 Used ANN to generate computational chemistry models and identify and classify compounds Transformed chemical information into computational models used to further search and identify antimicrobial compounds ANN
Feng et al., 2019 Used IDQD in order to analyze and search for patterns in certain sequences and predict further patterns in antibacterial peptides The study used this type of ML to successfully identify antimicrobial agents based on certain features of the antibacterial peptides ML-IDQD
Bhadra et al., 2018 Used ML to analyze the distribution pattern of amino acids in antibacterial peptides The model grouped amino acids based on certain properties in different groups and further predicted and identified antimicrobial peptides ML-random forest model
Napgal et al., 2018 Used ML to search, analyze and predict peptides based on certain features Analyzed peptides capable of inducting response of the APCs and further used ML to predict such peptides based on their structure ML
Su et al., 2019 Used NN trained on various datasets to achieve performance in feature selection and structure analysis Analyzed the features and structure of amino acids and peptides in order to identify novel antimicrobial peptides NN
Fjell et al., 2007 Used Hidden Markov models to construct an algorithm that enables recognition of individual classes of antimicrobial peptides Constructed a database that functions as a discovery tool for antimicrobial peptides NN-Hidden Markov models
Cherkasov et al., 2009 Used NN to search various databases and identify and further design antimicrobial peptides Screened a large number of peptides and selected the most potent ones for in vitro testing, further concluding that two compounds exhibited strong antimicrobial effects NN
Cruz-Monteagudo et al., 2011 Used ML to create and define classification rules for antimicrobial peptides The study aimed to assess both the toxicity and potency of antimicrobial peptides ML
Grafskaia et al., 2018 Used ML to create an algorithm for the identification of toxin-like that also acted as antimicrobial agents The combined ML and proteomic technologies showed the potential of such research, even though the study returned a small number of candidate peptides ML
Macesic et al., 2020 ML was used to assess and quantify both bacterial resistance and susceptibility to certain antibiotics Used ML to predict phenotypic polymyxin resistance in Klebsiella pneumoniae and to assess antimicrobial susceptibility ML
Mansbach et al., 2020 The study used ML to construct and design molecules capable of penetrating the membrane of Pseudomonas aeruginosa The algorithm constructed and considered every possible fragment-based design, obtaining five compounds that were experimentally validated and showed good membrane penetration ML-Hunting Fox Algorithm
Smith et al., 2020 The study used ML in combination with genetic algorithms to assess intrinsic activity and efficacy of compounds The study aimed to optimize dosing regimens when using antibiotic combinations, particularly against A. baumannii, the algorithm returning six regimens capable of eradicating the bacteria; even though these were not empirically tested ML
Hu et al., 2007 The study used ML and conventional methods to find new antimicrobial agents to counter the antimicrobial resistance of Yersinia spp. The study combined ML and multiple conformational high-throughput docking in order to find YpkA inhibitors; the algorithm returned 7 compounds that were empirically tested and showed antimicrobial activity ML

ML: Machine Learning; DL: Deep Learning; NN: Neural Networks; RNN: Recurrent Neural Networks; ANN: Artificial Neural Networks; IDQD: Increment of Diversity with Quadratic Discriminant.