An End-to-End Framework for Molecular Conformation Generation via\n Bilevel Programming
Bibliographic record
Abstract
Predicting molecular conformations (or 3D structures) from molecular graphs\nis a fundamental problem in many applications. Most existing approaches are\nusually divided into two steps by first predicting the distances between atoms\nand then generating a 3D structure through optimizing a distance geometry\nproblem. However, the distances predicted with such two-stage approaches may\nnot be able to consistently preserve the geometry of local atomic\nneighborhoods, making the generated structures unsatisfying. In this paper, we\npropose an end-to-end solution for molecular conformation prediction called\nConfVAE based on the conditional variational autoencoder framework.\nSpecifically, the molecular graph is first encoded in a latent space, and then\nthe 3D structures are generated by solving a principled bilevel optimization\nprogram. Extensive experiments on several benchmark data sets prove the\neffectiveness of our proposed approach over existing state-of-the-art\napproaches. Code is available at https://github.com/MinkaiXu/ConfVAE-ICML21\n
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".