Global interpretability and geometry of graph convolu- tional neural networks for chemistry in terms of chemical moieties
Bibliographic record
Abstract
Graph convolutional neural nets, such as SchNet, [Schütt et al, Journal of Chemical Physics, 2018, 148, 241722], provide accurate predictions of chemical quantities without invoking any direct physical or chemical principles. These methods learn a hidden statistical representation of molecular systems in an end-to-end fashion; from xyz coordinates to molecular properties with many hidden layers in between. This naturally leads to the interpretability question: what underlying chemical model determines the algorithm’s accurate decision-making? To answer this question, we analyze the hidden layer activations of QM9-trained SchNet, also known as “embedding vectors” with dimension- reduction, linear discriminant analysis and Euclidean-distance measures. The result is a quantifiable geometry of the model’s decision making that identifies chemical moieties and has a low parametric space of ∼ 5 important parameters from the fully-trained 128-parameter embedding. The geometry of the embedding space organizes these moieties with sharp linear boundaries that can classify each chemical environment within <5 × 10−4 error. Euclidean distance between embedding vectors can be used to demonstrate a versatile molecular similarity measure, outperforming other popular hand- crafted representations such as Smooth Overlap of Atomic Positions (SOAP). We also reveal that the embedding vectors can be used to extract observables that are related to chemical environments such as pKa and NMR. The work is in line with the recent push for explainable AI and gives insights into the depth of modern statistical representations of chemistry, such as graph convolutional neural nets, in this rapidly evolving technology.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".