2022
DOI: 10.1101/2022.11.16.516845
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Predicting Antimicrobial Activity for Untested Peptide-Based Drugs Using Collaborative Filtering and Link Prediction

Abstract: The increase of bacterial resistance to currently available antibiotics has underlined the urgent need to develop new antibiotic drugs. Antimicrobial peptides (AMPs), alone or in combination with other peptides and/or existing antibiotics, have emerged as promising candidates for this task. However, given that there are thousands of known AMPs and an even larger number can be synthesized, it is inefficient to comprehensively test all of them using standard wet lab experimental methods. These observations stimu… Show more

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“…[21][22] ML-guided approach based on descriptor space search and selection has already been used to predict antimicrobial activity. [23][24][25][26] The way of representing molecules is a crucial step. Numeric vectors consisting of molecular descriptor values (features) have already been utilized before the widespread applicability of ML in QSAR (quantitative structure−activity relationship) modeling.…”
Section: Introductionmentioning
confidence: 99%
“…[21][22] ML-guided approach based on descriptor space search and selection has already been used to predict antimicrobial activity. [23][24][25][26] The way of representing molecules is a crucial step. Numeric vectors consisting of molecular descriptor values (features) have already been utilized before the widespread applicability of ML in QSAR (quantitative structure−activity relationship) modeling.…”
Section: Introductionmentioning
confidence: 99%