3D Molecule Generation via Diffusion Model with a Self-Attention-Based EGNN
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide The discovery of new drugs is of great significance to human health. The diffusion model has emerged as a powerful tool for generating 3D molecular structures, achieving significant success in various applications. However, existing models lack direct interatomic confinement features and are difficult to capture long-range dependencies in macromolecules. These issues lead to the generation of molecular structures that are either inaccurate or lack rationality. To address the above issues, this paper proposes a novel 3D Molecule Generation via Diffusion Model with a self-attention-based E( n ) equivariant graph neural network (EGNN) named MGDM-Sa, which incorporates a dual equivariant score neural network DualESNet, to capture the global graph features and the local atomic environment. Especially, in DualESNet, we design a novel equivariant encoder eEncoder, which integrates EGNN and self-attention. During message propagation, EGNN ensures the equivariation of geometric features, and self-attention layer captures the long-range dependencies in the molecular graph. Thus, this design enhances the representation capability of MGDM-Sa. The experimental results demonstrate that MGDM-Sa is capable of generating valid, unique, novel, stable, and diverse drug-like molecules, thereby underscoring its potential to accelerate the drug discovery process.
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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 | 0.000 | 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 | 0.000 |
| 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".