A collaborative constrained graph diffusion model for the generation of realistic synthetic molecules
This addresses the challenge of exploring the vast molecular space for drug discovery and sustainability, offering an incremental improvement in efficiency and realism for computational chemistry.
The paper tackles the problem of generating chemically valid synthetic molecules by introducing CoCoGraph, a collaborative constrained graph diffusion model, which outperforms state-of-the-art methods on benchmarks with up to an order of magnitude fewer parameters and generates molecules with distributions more closely matching real ones across 36 chemical properties.
Developing new molecular compounds is crucial to address pressing challenges, from health to environmental sustainability. However, exploring the molecular space to discover new molecules is difficult due to the vastness of the space. Here we introduce CoCoGraph, a collaborative and constrained graph diffusion model capable of generating molecules that are guaranteed to be chemically valid. Thanks to the constraints built into the model and to the collaborative mechanism, CoCoGraph outperforms state-of-the-art approaches on standard benchmarks while requiring up to an order of magnitude fewer parameters. Analysis of 36 chemical properties also demonstrates that CoCoGraph generates molecules with distributions more closely matching real molecules than current models. Leveraging the model's efficiency, we created a database of 8.2M million synthetically generated molecules and conducted a Turing-like test with organic chemistry experts to further assess the plausibility of the generated molecules, and potential biases and limitations of CoCoGraph.