PepComLibGen: A Web Server for Generating Peptide Libraries for Computer-Aided De Novo Peptide Design and Combinatorial Lead Optimization
Bibliographic record
Abstract
Small peptides offer unique advantages for drug discovery due to their high specificity, low cost, and ability to disrupt protein-protein interactions, making them a promising chemical space for discovering new drugs and biotechnological tools. However, there is currently no available resource to generate a machine-readable library of small peptides from all possible sequences of canonical amino acids that can be used in a computer-aided drug discovery (CADD) campaign. To address this gap, we present a novel web server (www.peptidelibgen.com) that generates comprehensive SMILES libraries of small peptides from user-defined canonical amino acids and their cyclic analogues (including N-C terminal, side chain-N terminal, side chain-C terminal, side chain-side chain, and disulfide bridge). The server incorporates enantiomers of canonical amino acids, a user-specific noncanonical amino acid, end-capping, and library filters based on ease of synthesis, stability, and aqueous solubility. The generated peptides are output as smiles strings with structured FASTA identifiers, facilitating easy interpretation of top hits in a CADD workflow. To supplement this tool, we have also included an additional widget that allows one to rapidly obtain the SMILES codes for large libraries of combinatorial structures, especially useful for incorporation as unnatural residues when one seeks to screen a larger structural space. Overall, our web server provides a valuable resource for the discovery of new drug candidates using small peptides.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.031 | 0.029 |
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".