arXiv:2605.30610v1 Announce Type: new Abstract: Adapting generative foundation models, in particular diffusion and flow models, to optimize given reward functions (e.g., binding affinity) while satisfying constraints (e.g., molecular synthesizability) is fundamental for their adoption in real-world scientific discovery applications such as molecular design or protein engineering. While recent works have introduced scalable methods for reward-guided fine-tuning of such models via reinforcement learning and control schemes, it remains an open problem how to algorithmically trade-off reward maxim
The increasing sophistication of generative AI models like diffusion and flow models has made their application to complex scientific challenges, such as molecular design, a critical area of focus.
This development indicates progress in leveraging AI for accelerated discovery in fields like medicine and materials science, potentially shortening development cycles for new drugs and chemicals.
The ability to fine-tune generative models with specific constraints and reward functions moves AI closer to being a practical tool for designing novel molecules with predefined properties.
- · Pharmaceuticals
- · Biotechnology
- · Chemicals industry
- · AI software developers
- · Traditional drug discovery methods
- · Companies without AI integration
- · Inefficient R&D processes
Accelerated discovery and design of novel molecules for various applications.
Reduced costs and timelines for developing new drugs, materials, and industrial chemicals.
Potential for entirely new classes of therapeutics and materials based on AI-driven design, impacting global health and industrial production.
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Read at arXiv cs.LG