Bibliographic record
Abstract
Current antibacterial treatments cannot overcome the rapidly growing resistance of bacteria to antibiotic drugs, and novel treatment methods are required. One option is the development of new antimicrobial peptides (AMPs), to which bacterial resistance build-up is comparatively slow. Deep generative models have recently emerged as a powerful method for generating novel therapeutic candidates from existing datasets; however, there has been less research focused on evaluating the search spaces associated with these generators from which they sample their new data-points. In this research we employ five deep learning model architectures for de novo generation of antimicrobial peptide sequences and assess the properties of their associated latent spaces. We train a RNN, RNN with attention, WAE, AAE and Transformer model and compare their abilities to construct desirable latent spaces in 32, 64, and 128 dimensions. We assess reconstruction accuracy, generative capability, and model interpretability and demonstrate that while most models are able to create a partitioning in their latent spaces into regions of low and high AMP sampling probability, they do so in different manners and by appealing to different underlying physicochemical properties. In this way we demonstrate several benchmarks that must be considered for such models and suggest that for optimization of search space properties, an ensemble methodology is most appropriate for design of new AMPs. We design an AMP discovery pipeline and present candidate sequences and properties from three models that achieved high benchmark scores. Overall, by tuning models and their accompanying latent spaces properly, targeted sampling of new anti-microbial peptides with ideal characteristics is achievable.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".