Year: 2026
Type: preprint
Abstract: Computational chemists frequently build data processing workflows as ad-hoc scripts that are difficult to reuse, share, or deploy. Yet, many of the alternative workflow platforms demand significant complexity overhead or create manual breaks in workflows involving different pieces of software. pdChemChain is an open-source Python framework that bridges interactive use in python with reuse and deployment, by treating chemical data processing chains as composable, self-serializing pandas DataFrame operations. The framework is built around three design principles: (1) every operation takes a DataFrame and returns a DataFrame, (2) operations compose via the + operator into chains that are themselves operations, and (3) all chains are self-configuring, i.e. serializable to YAML for command-line deployment without code changes. Chains are built interactively in notebooks with immediate visual feedback, then deployed as-is via CLI or as scoring components in generative design frameworks. Built on widely used RDKit and pandas, pdChemChain provides links for molecular processing, property calculation, score transformation, and multi-parameter aggregation, while remaining extensible through a minimal dataclass-based API. pdChemChain is available at https://github.com/EBjerrum/pdchemchain under the LGPL-3.0 license.
Source: ChemRxiv
Author: Esben Jannik Bjerrum
Language: English
Subfield: Information Systems and Management
Field: Decision Sciences
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W7212008100