Exploring Multi-Objective Deep Reinforcement Learning Methods for Drug Design
Bibliographic record
Abstract
Drug design and optimization are complex tasks that require strategically efficient exploration of the extremely vast search space. Various fragmentation strategies have been presented in the literature to reduce the complexity of the molecular search space. From the optimization perspective, drug design can be viewed as a multi-objective optimization process. Deep reinforcement learning (DRL) frameworks have displayed promising performances in this field. However, lengthy training periods and inefficient use of sample data limit the scalability of the current frameworks. In this paper, we (1) review the fundamental concepts of deep or multi-objective RL methods and their applications in molecular design, (2) investigate the performance of a recent multi-objective DRL-based and fragment-based drug design framework, named DeepFMPO, in a real application by integrating protein-ligand docking affinity score, and (3) compare this method with a single-objective variant. Through experiments, we find that the DeepFMPO framework (with docking score) can achieve limited success, however, it is incredibly unstable. Our findings encourage further exploration and improvement. Possible sources of the framework's instability and suggestions of further modifications to stabilize the framework are examined.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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".