DOGMH: A Diffusion and Optimization Approach for Generative Molecular Health Informatics in Consumer Electronics

This is a task of computational chemistry and pharmaceutical development, with rapidly growing relevance for consumer healthcare electronics: deducing three-dimensional molecular structures from two-dimensional diagrams. In this paper, we introduce a new generative model, DOGMH, specifically tailored for applications on consumer electronic devices, which considerably increases the accuracy in this process. DOGMH borrows from the diffusion mechanisms of classical nonequilibrium thermodynamics and treats atoms as individual particles, excelling in the inversion of the diffusion process-turning noise into organized molecular structures. Our approach leverages a specially tailored bilevel optimization technique that ensures roto-translational invariance and allows for efficient processing on resource-constrained consumer devices. We show that DOGMH outperforms existing methods in terms of both conformational synthesis and distance distribution accuracy. DOGMH also provides the ability to create AI-enabled interactive 3D visualizations of molecular structures for consumer health education. Our work paves the way for advanced molecular informatics capabilities in consumer-grade devices and will potentially revolutionize personal healthcare management through AI-enhanced insights. By bridging the gap between complex molecular modeling and consumer electronics, DOGMH will open up a new frontier in intelligent, molecularly aware health devices accessible to the general public.