Benchmarks of molecular machine learning models often treat the molecular representation as a neutral input format, yet the representation defines the syntax of validity, edit operations, and invariances that models implicitly learn. We propose MolADT, a typed intermediate representation (IR) for molecules expressed as a family of algebraic data types that separates (i) constitution via Dietz-style bonding systems, (ii) 3D geometry and stereochemistry, and (iii) optional electronic annotations. By shifting from string edits to operations over structured values, MolADT makes representational assumptions explicit, supports deterministic validation and localized transformations, and provides hooks for symmetry-aware and Bayesian workflows. We provide a reference implementation in Haskell (open-source, archived with DOI) and worked examples demonstrating delocalised/multicentre bonding, validation invariants, reaction extensions, and group actions relevant to geometric learning.

翻译：分子机器学习模型的基准测试通常将分子表示视为一种中性的输入格式，然而该表示定义了模型隐式学习的有效性语法、编辑操作与不变性。我们提出MolADT，一种以代数数据类型族表达的分子类型化中间表示，它将（i）通过Dietz式键合系统描述的结构组成，（ii）三维几何与立体化学，以及（iii）可选的电子注释分离开来。通过从字符串编辑转向对结构化值的操作，MolADT使表示假设显式化，支持确定性验证与局部化变换，并为对称感知及贝叶斯工作流提供接口。我们提供了Haskell语言的参考实现（开源，附DOI存档）及工作示例，展示了离域/多中心键合、验证不变量、反应扩展以及与几何学习相关的群作用。