OmniInvent: Generalist Generative Chemical Language Models through Blank Infilling

Drug design requires navigating a vast and sparsely populated chemical space while balancing structural novelty with multiple property constraints. Most generative approaches to the problem remain specialized to individual tasks, however. Here, we introduce OmniInvent, a chemical language model that operates on unmodified SMILES and supports both de novo and substructure-constrained molecular generation within a single framework. OmniInvent formulates molecular design as an autoregressive blank infilling problem over partially masked SMILES and is pretrained using an on-the-fly graph-based masking strategy that randomly selects connected molecular substructures and masks the corresponding SMILES segments without the need for task-specific fragmentation strategies. In direct sampling, OmniInvent generates highly valid, unique, and diverse molecules in de novo design, fragment linking, scaffold decoration, and motif extension. In goal-directed design, it matches or exceeds state-of-the-art performance on the Practical Molecular Optimization benchmark for de novo design and achieves competitive performance in substructure-constrained design despite being trained as a general-purpose model. These results show that blank infilling on SMILES provides an effective foundation for building generalist molecular generative models across diverse drug design tasks.