Transformer models for protein-guided drug compound generation: A comparison of amino acid sequences, pre-trained protein embeddings, SMILES, and SELFIES
Bibliographic record
Abstract
Drug discovery is a time-consuming and costly process that notoriously suffers from low success rates. Increased availability of chemically relevant data and advances in machine learning techniques offer potential solutions for aiding in the drug development pipeline. This thesis explores the use of Transformers in the conditional generation of potential drug compounds from protein context. Building on previous research, this work implements four transformer models to take in protein information as input and generate potential binding compounds. Each model uses either SMILES or SELFIES string representations of compounds and amino acid sequences or pretrained ESM-2 protein embeddings as contextual input. These models are trained and compared in their ability to generate chemically feasible compounds that approximate the physiochemical properties of the training set and show binding potential specific to the contextual proteins. The utilization of SEFLIES increased compound validity and diversity but overall had a negative performance impact compared to their SMILES counterparts. Pretrained protein embeddings were shown to decrease validity but improved model performance despite no change to model structure or size. These results highlight the potential of transformer models paired with pretrained protein embeddings to enhance the drug discovery process with the generation of lead compounds from novel proteins without any fine-tuning or retraining.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".