Increasing the greediness of generative flow networks through action-values
Bibliographic record
Abstract
In recent years, deep learning has emerged as a highly promising tool in drug discovery, offering significant reductions in both time and cost associated with the process.Specifically, a novel generative method known as Generative Flow Networks (GFlowNets) has demonstrated promising results by showcasing its ability to generate a diverse pool of candidates for small molecule generation tasks.Recognizing the potential of GFlowNets, it is essential to investigate and improve their ability to generate high reward and diverse samples, which are crucial for drug development process.This thesis proposes an approach to address this challenge in complex and multi-dimensional scenarios, namely Q-learning GFlowNets (QGFN) -an approach which allows to control the greediness of a GFlowNet by leveraging the well-known reinforcement learning (RL) technique of action values.QGFN increases exploration diversity while preserving the ability to generate high-reward candidates.Empirical results show that QGFN effectively generates high-reward samples in a variety of tasks without sacrificing diversity.This work demonstrates significant improvements that can translate into practical applications in the field of drug discovery, contributing to its advancement.i List of Tables5.1 Fragment-based molecule task: Reward and Diversity at inference after training. . . . . . .
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".