arXiv Machine Learning
Elite-Weighted Supervised Fine-tuning for Goal-Directed Molecular Optimization
Read the original on arXiv Machine Learning →The Flow has not summarised this story yet — read it at arXiv Machine Learning.
Related stories
arXiv Machine Learning
Jul 22
Adopting Reinforcement Learning with Verifiable Rewards for Molecular Generation
arXiv:2607. 19044v1 Announce Type: new Abstract: Leveraging large language models (LLMs) for molecular generation has shown remarkable potential in chemical and drug design.
arXiv Machine Learning
Jul 7
On the Design Space of Discrete Diffusion Online Adaptation for Molecular Optimization
arXiv:2607. 02834v1 Announce Type: new Abstract: Molecular optimization often starts from a pretrained generative model that captures a broad prior over valid molecular structures.
arXiv AI
4d ago
MFG: Multi-turn Feedback Guided Molecular Optimization
arXiv:2610.18851v1 Announce Type: new Abstract: Multi-objective molecular optimization is a critical process in drug discovery that aims to modify a given molecule to simultaneously improve multiple...
arXiv AI
Oct 7
Navigating Route Latent Space for Synthesizable Molecular Design
arXiv:2610.07560v1 Announce Type: new Abstract: Goal-directed molecular design has advanced rapidly, yet a substantial proportion of designed molecules remain difficult to synthesize in practice, lim...
arXiv AI
Aug 20
PGFS++: Molecular Property Improvement under Synthesis and Diversity Constraints
PGFS++ is a synthesis‑aware reinforcement learning framework that improves molecular properties while ensuring the resulting molecules can be synthesized and remain structurally similar to the input. It builds on PGFS+ by using trainable embedding lookup tables for reaction templates and second reactants, a more effective scoring function, and a refined RL algorithm. Experiments demonstrate that PGFS++ enhances target properties and preserves high output diversity, overcoming the reward‑hacking failure mode seen in earlier versions.
arXiv Machine Learning
Sep 30
MARCO: Multi-Round Agentic Reinforcement for Conditional Molecular Optimization
arXiv:2609.36683v1 Announce Type: new Abstract: Molecular optimization is inherently iterative: a candidate is proposed, evaluated against several objectives, and revised while preserving a relations...