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 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".