Scalable Inference-time Steering in Molecular Design with Multimodal Meta Flow Maps

Biological design problems naturally have continuous (e.g. molecular structures), discrete (e.g. DNA residue types) and often both modalities (e.g. protein structure-sequence co-design). Diffusion and CTMC-based methods have been developed for multimodal generation, and, for cross-modal tasks, the two are typically combined. However, it remains challenging to adapt these methods at inference-time to fulfill design constraints. Recent developments of one-shot samplers such as Meta Flow Maps (MFM) have demonstrated high controllability for image generation, showcasing the power of inference-time steering with accurately estimated reward gradients. Here, we adapt Meta Flow Maps to biological domains, and develop MFM framework both in discrete (dMFM) and multimodal settings (multiMFM). We demonstrate dMFM by designing DNA sequences with a target mechanical property. MultiMFM trained on molecular the datasets QM9 and GEOM-Drugs are applied to design molecules of a given physical property. In both cases, our steering algorithms achieve high accuracy with a single trained backbone, outperforming fine-tuning methods adapted for task-specific ends.