Year: 2025
Type: article
Abstract: Understanding molecular interactions beyond single-molecule properties is critical for studying real-world chemical systems. Quantum chemical calculations of molecule–molecule interactions are computationally demanding, making large, publicly available datasets scarce. Here, we present an efficient framework for generating initial configurations of molecular interaction systems and construct a molecular interaction dataset, containing 49,620 individual molecules and 247,741 molecular pairs spanning chromophore–solvent, solute–solvent, and drug–drug interactions, each associated with experimentally characterized equilibrium structures. Our dataset can be used for theoretical studies and machine learning applications in chemical sciences, particularly for modeling intermolecular interactions and structure-based prediction of experimental properties. In future work, we plan to expand the dataset to include non-equilibrium structures and atomic forces, thereby broadening its applicability to reaction modeling and force field development. We present a scalable framework for systematically generating initial configurations of interacting molecular systems, resulting in the first large-scale dataset to provide broad and systematically curated coverage of 247,741 interaction systems across 49,620 unique molecules. Unlike prior datasets that focus on individual molecules or narrow interaction types, our dataset covers chromophore–solvent, solute–solvent, and drug–drug interactions. This resource contributes to the data-driven modeling of intermolecular phenomena in conjunction with experimental values, supporting the development of machine learning models based on pairwise geometries within interacting molecular systems.
Source: Journal of Cheminformatics
Authors: Seunghun Jang, Gyoung S. Na
Institution: Korea Research Institute of Chemical Technology
Language: English
Subfield: Atomic and Molecular Physics, and Optics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,390
Awards: G1049915054, G116065841
OpenAlex ID: W4417534696