Image-based generation for molecule design with SketchMol

Zixu Wang, Yangyang Chen, Pengsen Ma, Zhou Yu, Jianmin Wang, Yuansheng Liu, Xiucai Ye, Tetsuya Sakurai, Xiangxiang Zeng

Abstract: Efficient molecular design methods are crucial for accelerating early stage drug discovery, potentially saving years of development time and billions of dollars in costs. Current molecular design methods rely on sequence-based or graph-based representations, emphasizing local features such as bonds and atoms but lacking a comprehensive depiction of the overall molecular topology. Here we introduce SketchMol, an image-based molecular generation framework that combines visual understanding with molecular design. SketchMol leverages diffusion models and applies a refinement technique called reinforcement learning from molecular experts to improve the generation of viable molecules. It creates molecules through a painting-like approach that simultaneously depicts local structures and global layout of the molecule. By visualizing molecular structures, various design tasks are unified within a single image-based framework. De novo design becomes sketching new molecular images, whereas editing tasks transform into filling partially drawn images. Through extensive experiments, we demonstrated that SketchMol effectively handles a variety of molecular design tasks. SketchMol is a model that explores the feasibility of incorporating image generation techniques into the field of small-molecule design.