QSAR modeling and computer‐aided design of antimicrobial peptides
Bibliographic record
Abstract
The drastic increase in multi-drug-resistant bacteria has created an urgent need for new therapeutic interventions, including antimicrobial peptides, an interesting template for novel drug development. However, the process of optimizing peptide antimicrobial activity and specificity using large peptide libraries is both tedious and expensive. Here we confirm the use of a mathematical model for prediction, prior to synthesis, of peptide antibacterial activity toward the antibiotic resistant pathogen Pseudomonas aeruginosa. By the use of novel descriptors quantifying the contact energy between neighboring amino acids, as well as a set of inductive and conventional QSAR descriptors, we were able to model the antibacterial activity of peptides. Cross-correlation and optimization of the implemented descriptor values enabled us to build two models, using very limited sets of peptides, which were able to correctly predict the activity of 85 or 71% of the tested peptides, within a twofold deviation window of the corresponding previously assessed IC(50) values, measured earlier. Though these two models were significantly different in size, they demonstrated no significant difference in their predictive power, implying that it is possible to build powerful predictive models using even small sets of structurally different peptides, when using contact-energy descriptors and inductive and conventional QSAR descriptors in the model design.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".