A Comparative Evaluation of Machine Learning and Deep Graph Learning for Chemical Ecotoxicological Prediction
Bibliographic record
Abstract
Regulating chemicals to protect the environment based on ecotoxicological assessments is a major challenge. However, experimental ecotoxicity tests are time-consuming and expensive, which underscores the need for accurate prediction methods. In this study, we conducted a comprehensive analysis on the application of machine learning and graph-based learning techniques for the ecotoxicological prediction of chemicals. A total of 161 models were constructed using a combination of three molecular representations (Morgan, MACCS, and Mol2vec), six machine learning algorithms (KNN, NB, RF, SVM, XGB, and DNN), and five graph neural networks (GAT, GCN, MPNN, Attentive FP, and FPGNN). In predicting the ecotoxicity of three aquatic taxonomic groups - fish, crustaceans, and algae - GCN achieved the best performance overall. In the same-species predictions, GCN models achieved the highest values of area under the ROC curve (AUC), ranging between 0.982 and 0.992. In cross-species predictions, GAT and GCN achieved the best and second-best performance, respectively. However, both models exhibited a reduction of approximately 17% in AUC values when predicting the fish group while being trained on the same chemical data for the crustaceans and algae groups. Interestingly, cross-species predictions for unseen chemicals are only better off by DNN with the MACCS fingerprint, yielding an AUC of 0.821. Our findings underscore the critical need to further advance computational prediction methods in order to accurately predict the ecotoxicity of chemicals across species. The ecotoxicology prediction web server for fish, algae, and crustaceans is accessible at https://app.cbbio.online/ecotoxicology/home.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".