Hyperparameter optimization and neural architecture search algorithms for graph Neural Networks in cheminformatics
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
• Comprehensive review of cheminformatics datasets for molecular property prediction. • Survey of optimization techniques for Graph Neural Networks in cheminformatics. • Comparison of optimization methods, highlighting strengths and limitations. • Identify gaps and future directions in Graph Neural Networks for cheminformatics. Cheminformatics, an interdisciplinary field bridging chemistry and information science, leverages computational tools to analyze and interpret chemical data, playing a critical role in drug discovery, material science, and environmental chemistry. Traditional methods, reliant on rule-based algorithms and expert-curated datasets, face challenges in scalability and adaptability. Recently, machine learning and deep learning have revolutionized cheminformatics by offering data-driven approaches that uncover complex patterns in vast chemical datasets, advancing molecular property prediction, chemical reaction modeling, and de novo molecular design. Among the most promising techniques are Graph Neural Networks (GNNs), which have emerged as a powerful tool for modeling molecules in a manner that mirrors their underlying chemical structures. Despite their success, the performance of GNNs is highly sensitive to architectural choices and hyperparameters, making optimal configuration selection a non-trivial task. Neural Architecture Search (NAS) and Hyperparameter Optimization (HPO) are crucial for improving GNN performance, but the complexity and computational cost of these processes have traditionally hindered progress. This review examines various strategies for automating NAS and HPO in GNNs, highlighting their potential to enhance model performance, scalability, and efficiency in key cheminformatics applications. As the field evolves, automated optimization techniques are expected to play a pivotal role in advancing GNN-based solutions in cheminformatics.
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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 | 0.002 | 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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 it