MAPCliff-WMGR: Exploring Activity Cliffs in Molecular Activity Prediction Enhanced by Weighted Molecular Graph Representations
Bibliographic record
Abstract
In drug discovery, accurately predicting molecular activity is crucial for identifying and optimizing molecules with desirable biological properties. A significant challenge in this field is the phenomenon of activity cliffs, where molecules with similar structures exhibit significantly divergent biological activities. This study introduces MAPCliff-WMGR, a computational framework designed to predict molecular activity under the activity cliff scenario using weighted molecular graphs. MAPCliff-WMGR consists of a core mGraphSNN GAT module that integrates model-specific adjustments to better handle molecular data, enabling the model to effectively predict molecular activity under the activity cliff scenario. Due to activity cliff data exhibiting characteristics of spectral bias, MAPCliff-WMGR addresses this by employing an Independent Feature Mapping (IFM) module that uses sinusoidal transformations to map features into a frequency-rich domain. Experimental results demonstrate that MAPCliff-WMGR achieves an average RMSE of 0.677 for cliff molecules, which is 7.2% better than the best-performing baseline. Furthermore, we build the MACE-R7 platform, a richer benchmark with various response types and targets, on which our method achieves an average improvement of 3.2% in overall prediction and 8.7% for cliff molecules. Moreover, the model’s interpretability is further demonstrated to uncover critical atoms responsible for activity cliffs through attention-based analysis and dimensionality reduction visualizations. Finally, a case study on small-molecule drugs targeting estrogen receptor alpha (ERα) for breast cancer treatment underscores the model’s ability to accurately predict activity for cliff molecules, validating its potential for virtual drug screening.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.004 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".