RETRACTED: The Use of DeepQSAR Models for the Discovery of Peptides with Enhanced Antimicrobial and Antibiofilm Potential
Post-publication record
OpenAlex flags this work as retracted, but it carries no matching Retraction Watch record in this frame.
Bibliographic record
Abstract
Increasing concerns regarding prolonged antibiotic usage have spurred the search for alternative treatments. Antimicrobial peptides (AMPs), first discovered in the 1980s, have exhibited significant potential against a broad range of bacteria. Short-sequenced AMPs are abundant in nature and present across various organisms. Recently, machine learning technologies, such as Quantitative Structure-Activity Relationships (QSAR), have enabled the expedited discovery of potential AMPs with broad-spectrum antibacterial activity as the amount of available AMP training data increases. Among these, Deep QSAR has recently emerged as a distinct type of application that utilizes conventional molecular descriptors in conjunction with more powerful deep learning (DL) models. Here, we demonstrate the power of Deep QSAR in predicting broad-spectrum AMP activity. Using a recurrent neural network-based QSAR model, we achieved nearly 90% 5-fold cross-validated accuracy in classifying AMP activity. Using the developed approach, we designed 100 novel peptides, of which 44 experimentally demonstrated more effective antibiofilm activity, and 31 peptides exhibited stronger antimicrobial activity compared to the well-characterized host defense peptide IDR-1018, which was demonstrated to possess broad-spectrum antibiofilm activity against a wide range of bacterial pathogens. . Additionally, a previous computer-aided peptide design study employing IDR-1018 derivatives successfully identified novel peptides with enhanced antibiofilm activity. Notably, 29 of these peptides demonstrated improvements of both antimicrobial and, particularly, antibiofilm properties, making them suitable prototypes for preclinical development and demonstrating the efficacy of DeepQSAR modeling in identifying novel and potent AMPs.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.033 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.020 | 0.012 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".