arXiv:2605.15354v2 Announce Type: replace Abstract: Despite the success of foundation models in language and vision, molecular graph generation still lacks a unified framework for heterogeneous design tasks with reliable controllability. While reinforcement learning (RL) offers a natural post-training mechanism for task-specific optimization, applying it to graph generative models is hindered by the vast atom-wise action spaces and chemically invalid intermediate states. We propose \textbf{Co}ntrollable \textbf{Mole}cular Generative Foundation Models (CoMole), built with a unified motif-aware
The proliferation of foundation models in other domains (language, vision) naturally drives researchers to apply similar paradigms to complex scientific generation tasks like molecular design.
This development could significantly accelerate drug discovery, materials science innovation, and chemical engineering by providing more efficient and controllable molecular design tools.
The proposed CoMole framework introduces a unified, motif-aware approach to molecular generation, potentially overcoming prior limitations in controlling chemical validity and specific design tasks.
- · Pharmaceutical companies
- · Biotechnology and materials science companies
- · AI-driven drug discovery platforms
- · Chemical engineering researchers
- · Traditional, less automated drug discovery methods
- · Companies reliant on brute-force molecular screening
Molecular generative foundation models become a standard tool in scientific research and industrial design.
Reduced R&D timelines and costs lead to faster development of new drugs and advanced materials.
The ability to rapidly design and optimize novel molecular structures creates entirely new industries and therapeutic approaches, altering economic landscapes.
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Read at arXiv cs.LG