SMORE-DRL: Scalable Multi-Objective Robust and Efficient Deep Reinforcement Learning for Molecular Optimization
Bibliographic record
Abstract
The adoption of AI techniques within the domain of drug design provides an opportunity for systematic and efficient exploration of the vast chemical search space. In recent years, advancements in this domain have been driven by AI frameworks, including deep reinforcement learning (DRL). However, the scalability and performance of existing DRL methodologies are constrained by prolonged training periods and inefficient sample data utilization. Furthermore, generalization capabilities of these models have not been fully investigated. To overcome these limitations, we take a multi-objective optimization perspective and introduce SMORE-DRL, a fragment and transformerbased multi-objective DRL architecture for the optimization of molecules across multiple pharmacological properties, including binding affinity to both single and dual cancer protein targets. Our approach involves pretraining a transformer-encoder model on molecules encoded by a novel hybrid fragment-SMILES representation method. Fine-tuning is performed through a novel gradient-alignment-based DRL, where lead molecules are optimized by selecting and replacing their fragments with alternatives from a fragment dictionary, ultimately resulting in more desirable drug candidates. Our findings indicate that SMOREDRL is superior to current models for lead optimization in terms of quality, efficiency, scalability, and robustness. Furthermore, SMORE-DRL demonstrates the capability of generalizing its optimization process to lead molecules that are not present during the pretraining or fine-tuning phases.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".