Every molecule ever synthesised can be drawn as a 2D skeletal diagram, yet in modern property prediction this universally available representation has received less focus in favour of molecular graphs, 3D conformers, or billion-parameter language models, each imposing its own computational and data-engineering overhead. We present $\textbf{MolSight}$, the first systematic large-scale study of vision-based Molecular Property Prediction (MPP). Using 10 vision architectures, 7 pre-training strategies, and $2\,M$ molecule images, we evaluate performance across 10 downstream tasks spanning physical-property regression, drug-discovery classification, and quantum-chemistry prediction. To account for the wide variation in structural complexity across pre-training molecules, we further propose a $\textbf{chemistry-informed curriculum}$: five structural complexity descriptors partition the corpus into five tiers of increasing chemical difficulty, consistently outperforming non-curriculum baselines. We show that a single rendered bond-line image, processed by a vision encoder, is sufficient for competitive molecular property prediction, i.e. $\textit{chemical insight from sight alone}$. The best curriculum-trained configuration achieves the top result on $\textbf{5 of 10}$ benchmarks and top two on $\textbf{all 10}$, at $\textbf{$\textit{80$\times$ lower}$}$ FLOPs than the nearest multi-modal competitor.

翻译：所有被合成的分子都可以绘制成二维骨架图，然而在现代性质预测中，这种普遍可用的表示形式却较少受到关注，取而代之的是分子图、三维构象或拥有数十亿参数的语言模型——每一种方法都带来了额外的计算与数据工程负担。我们提出\textbf{MolSight}，这是首个基于视觉的分子性质预测（MPP）系统化大规模研究。通过使用10种视觉架构、7种预训练策略以及2M张分子图像，我们在涵盖物理性质回归、药物发现分类和量子化学预测的10个下游任务中评估了性能。考虑到预训练分子在结构复杂度上的广泛差异，我们进一步提出了一种\textbf{基于化学知识的课程学习策略}：通过五个结构复杂度描述符将语料库划分为五个难度递增的化学层级，该方法始终优于非课程基准方法。研究表明，单个经图像编码器处理的渲染键线图足以实现具有竞争力的分子性质预测，即仅凭视觉即可获得化学洞察。最佳课程训练配置在10个基准测试中的5个上取得了最优结果，并在全部10个测试中位列前两名，其FLOPs仅为最接近的多模态竞争者的80倍。