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.

高效的分子设计方法对于加速早期药物发现至关重要，可节省数年的开发时间和数十亿美元的成本。当前的分子设计方法依赖于基于序列或基于图形的表示，强调键和原子等局部特征，但缺乏对整体分子拓扑结构的全面描述。在这里，我们介绍了 SketchMol，这是一种基于图像的分子生成框架，它将视觉理解与分子设计相结合。SketchMol 利用扩散模型并应用一种来自分子专家的称为强化学习的改进技术来改进活分子的生成。它通过一种类似绘画的方法创建分子，同时描绘分子的局部结构和全局布局。通过可视化分子结构，各种设计任务被统一在一个基于图像的框架中。从头设计变成了草绘新的分子图像，而编辑任务则转变为填充部分绘制的图像。通过广泛的实验，我们证明了 SketchMol 可以有效地处理各种分子设计任务。