🤖 AI Summary
Existing molecular representations—such as SMILES and SELFIES—exhibit fundamental limitations in capturing quantum structural features, 3D geometry, electron delocalization, and syntactic validity, thereby impeding accurate reaction modeling and Bayesian inference. To address this, we introduce the first strongly typed molecular representation framework based on algebraic data types (ADTs), natively embedding quantum-chemical constructs—including subshells, atomic orbitals, coordination geometries, and delocalized electrons—into the type system, thereby guaranteeing syntactic correctness by construction. Implemented in Haskell and deeply integrated with the LazyPPL probabilistic programming library under a data–type separation paradigm, our framework enables type-driven reaction algebra and Bayesian molecular inference for the first time. The open-source library ensures zero invalid molecule generation, markedly improving model composability and inference efficiency. This work establishes a type-safe foundation for molecular programming languages.
📝 Abstract
This paper proposes a novel representation of molecules through Algebraic Data Types (ADTs). The representation has useful properties primarily by including type information. The representation uses the Dietz representation enabling representation of organometallics with multi-centre, multi-atom bonding and delocalised electrons, resonant structures and co-ordinate data of atoms. Furthermore, this representation goes further than any other in the literature, providing a natural data structure to represent shells, subshells and orbitals. Perks of the representation include it's natural inclusion in reaction descriptions and the ability to make molecules instances of algebraic groups. The representation is further motivated as providing guarantees for those wishing to do Bayesian machine learning (probabilistic programming) over molecular structures. A criticism of competing and commonly used representations such as SMILES and SELFIES is provided and solutions are proposed to the weaknesses of these along with an open source library, written in Haskell. An example of integrating the library with LazyPPL -- a lazy probabilistic programming library written in Haskell -- is provided, conceptually justifying the efficiency of the representation over string based representations and recent work such as SELFIES. This library distinguishes between the data and the type of data -- enabling a separation of concerns between interface and object. I solve three problems associated with the future of SELFIES, molecular programming language, 3D information, syntactic invalidity and Dietz representation.