Dftdescriptorpipeline Achieves Automated Molecular Descriptor Extraction and Four Case Studies

Scientists increasingly rely on computational methods to predict and understand molecular behaviour, yet extracting meaningful insights from density functional theory (DFT) calculations remains a laborious and time-consuming task. Yu-Chien Huang (National Taiwan Normal University), Dennis Chung-Yang Huang (Oklahoma State University and Hokkaido University), and Yun-Cheng Tsai (National Taiwan Normal University), along with their collaborators, address this challenge by introducing DFTDescriptorPipeline, a novel automated workflow designed to efficiently analyse DFT output files, generate molecular descriptors, and model structure–property relationships.

This work is significant because it streamlines the connection between molecular structure and chemical reactivity, enabling faster and more effective rational molecular design. The approach is validated through case studies involving photoswitchable molecules and catalytic reactions, demonstrating its practical utility across different chemical systems. By producing interpretable models and providing a generalisable framework that integrates computational chemistry with data-driven methodologies, DFTDescriptorPipeline has the potential to accelerate discovery and insight generation across a wide range of chemical applications.

DFTDescriptorPipeline for Chemical Descriptor Extraction is a powerful

This innovative platform addresses a long-standing need for scalable and reproducible analysis in physical organic chemistry, bridging the gap between quantum chemical calculations and data-driven molecular design. In each instance, the resulting MLR models yielded interpretable results, confirming the power of this approach for understanding complex chemical phenomena. Traditionally, understanding the connection between molecular structure and chemical behaviour has relied heavily on linear free energy relationships (LFERs), with Hammett analysis being a cornerstone technique in organic chemistry. However, conventional methods often depend on experimentally derived parameters, limiting their scope to simpler molecules and failing to account for crucial steric effects.
To overcome these limitations, researchers have begun incorporating computationally derived parameters into LFERs, expanding their applicability to a wider range of chemical systems. This automation allows for the efficient analysis of larger datasets and facilitates the rapid exploration of molecular design spaces. Furthermore, the pipeline quantifies steric effects using Sterimol parameters, providing a comprehensive set of descriptors that capture both electronic and spatial characteristics of molecules. These descriptors are then aggregated into a unified table, ready for use in constructing MLR models.

The entire process, from input preparation to model construction and visualization, is streamlined and automated, requiring minimal coding expertise from the user. This accessibility is expected to lower the barrier to entry for scientists seeking to integrate computational chemistry into their data-driven molecular design workflows. Specifically, the pipeline extracts frontier orbital energies, computes Sterimol parameters along a defined axis, and aggregates these features into a unified table with substituent-aware prefixes. Detailed procedures for descriptor extraction, anchor-based NBO descriptors, Sterimol geometry processing, and the final feature aggregation framework are meticulously described, ensuring transparency and reproducibility. The researchers anticipate that this platform will serve as a generalizable framework, adding to the toolbox for connecting data science and physical organic chemistry, and ultimately accelerating the discovery of new molecules with tailored properties.

DFT Descriptor Pipeline for Steric Effects

This innovative platform addresses limitations in traditional Hammett analysis, which struggles to account for steric effects crucial in catalytic reactivity and selectivity. The study pioneered a three-step process: parameter extraction from DFT output, descriptor tabulation, and MLR model construction, previously performed manually, hindering scalability. The pipeline systematically parses log files using regular-expression parsing and numerical computation, integrating error handling for robustness across varied formats. Experiments employ a detailed procedure for descriptor extraction, beginning with identifying anchor atoms, specifically, the O, H bonds of acids, carbonyl carbons, and adjacent aryl carbons, using the NBO summary.

Steric descriptors, L, B1, and B5, are then computed after removing specific atoms from the geometry, defining the Sterimol axis using Bondi radii with a hydrogen adjustment of 1.09Å. The resulting values are stored with substituent-aware prefixes, facilitating downstream modeling and ensuring clarity in data interpretation. This end-to-end process, visually outlined in Figure 2 of the work, enables users to upload DFT outputs and experimental data, automatically extracting descriptors and identifying optimal MLR models with minimal coding intervention. The system delivers a unified pandas table containing aggregated features, streamlining data analysis and facilitating the connection between data science and physical organic chemistry. Researchers anticipate this platform will serve as a generalizable framework, lowering the barrier to adoption for scientists seeking to integrate chemical calculations into data-driven molecular design.

Automated Descriptor Extraction via DFT and MLR enables

Experiments revealed that the models consistently provide interpretable results, confirming the versatility of this approach across a wide range of chemical contexts. Measurements confirm that steric quantification is achieved via Sterimol parameters, L, B1, and B5, computed along the C1, C2 axis, providing a comprehensive steric profile. Researchers implemented the workflow via a publicly available platform, https://github. com/p eculab/DFTDescriptorPipeline, which automatically aligns extracted descriptors with experimental identifiers. The process comprises three key stages: descriptor extraction, steric quantification, and feature aggregation.

Tests prove that anchor atoms are inferred from the NBO summary by locating the O, H bond(s) of the acid, the carbonyl carbon (C1), and the adjacent aryl carbon (C2), enabling precise descriptor assignment. Furthermore, the team computed Sterimol descriptors after removing specific atoms from the geometry, defining the C1, C2 axis as the Sterimol axis, and utilising Bondi radii with a hydrogen adjustment of 1.09Å. Measurements confirm that the resulting L, B1, and B5 values are stored as Ar Ster L, Ar Ster B1, and Ar Ster B5, respectively. The breakthrough delivers a complete end-to-end pipeline, encompassing input preparation, automated descriptor extraction, substituent matching.