VNFlow: integration of variational autoencoders and normalizing flows for novel molecular design
Bibliographic record
Abstract
Generative Artificial Intelligence is transforming the molecular discovery by enabling exploration of the vast, largely unexplored chemical space. However, current methods, including normalizing flows, struggle to balance the optimization of complex objectives and sampling speed, particularly when generating specific compound classes and more intricate scaffolds, such as aromatic rings. This work developed a generative model that efficiently samples novel molecules while optimizing their drug-likeness, ease of synthesis or chemical reactivity. To achieve this, we employed normalizing flows combined with variational autoencoders to generate samples which were evaluated for the Quantitative Estimate of Drug-likeness, the Synthetic Accessibility scores and, in case of organofluorine-phosphates, electronic density on the central phosphorus atom, approximated by Hirschfeld charges calculated with density functional theory. Our framework efficiently generated a diverse range of organofluorine-phosphates, demonstrating that combining normalizing flows directly with SELFIES or group-SELFIES can address key limitations in inverse molecular design, particularly when variational autoencoders cannot be applied due to a lack of available training data. Normalizing flows capture the chemical structures in a holistic way which paves the way towards targeted therapies that enable the optimization of complex molecular objectives.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".