BiFDR: Brain-Inspired Federated Diffusion Transformer with Reinforcement for privacy-preserving molecular generation

Objective:

Generative drug discovery is hampered by challenges in data privacy and the immense computational cost of SOTA models. To surmount these barriers, we developed Brain-Inspired Federated Diffusion with Reinforcement (BiFDR), a privacy-preserving and resource-efficient framework.

Methods:

BiFDR integrates three synergistic modules. A Neuro-inspired Federated Coordinator (NeuroFed) orchestrates secure collaboration via synaptic plasticity-inspired principles, combining server-side pruning with client-side Low-Rank Adaptation (LoRA) and sparse asynchronous updates. A Transformer-based diffusion generator (TransFuse) efficiently creates chemically valid molecules in a compressed latent space using attention mechanisms. Finally, a reinforcement learning agent (T-JORM) steers the generative process towards novel 2D and 3D molecular structures, guided by a multi-faceted, Tanimoto-based reward function.

Results:

Benchmarked against baseline models, BiFDR improving the Quantitative Estimate of Drug-likeness by 13.7%, the Molecular-level Structural Information Score by 5.7%, and the Molecular Interaction Analysis Index by 52.3%. The framework also enhanced synthetic feasibility, reflected by a 9.5% reduction in the Synthetic Accessibility Score. Critically, BiFDR substantially strengthened data privacy, achieving a 43.6% reduction in the mutual information metric.

Conclusion:

BiFDR establishes an effective and efficient paradigm for generative drug discovery. It consistently produces molecules with superior drug-likeness, structural novelty, and interaction potential. By ensuring synthetic accessibility while rigorously preserving privacy and minimizing computational overhead, BiFDR presents a viable and scalable solution for modern, collaborative drug development pipelines.