Computational Methods for Generating and Evaluating Three-Dimensional Molecular Structures

Computational simulations have become indispensable in modern chemical research, providing detailed insights into molecular structures and dynamics. Their reliability depends strongly on accurate three-dimensional structures and, consequently, on the identification of relevant conformers. However, for large systems composed of multiple interacting molecules, generating and evaluating these conformers computationally remains challenging. Addressing this challenge, this work presents computational tools for the efficient exploration of intermolecular interactions and the reliable evaluation of conformational energies. The automated interaction site screening (aISS) algorithm efficiently docks multiple molecules, thereby identifying energetically favorable intermolecular orientations of systems up to thousands of atoms. Applicable to chemically diverse molecules, it significantly expands the capabilities of current docking tools that mostly focus on bioorganic molecules and additionally offers features like site-specific docking for mechanistic studies. The quantum cluster growth (QCG) algorithm further extends molecular docking to the explicit modeling of solvation. It builds physically meaningful solute–solvent clusters including the generation of low-energy conformer ensembles, which are well-suited for addressing the shortcomings of commonly applied implicit solvent models in different applications such as structural studies and spectral analysis. The solvMPCONF196 benchmark set, which comprises biologically relevant systems solvated by explicit water molecules, provides valuable insights into the performance of methods for evaluating conformational energies. It is found that the most accurate methods tested on the solvMPCONF196 were also the computationally most expensive ones, making them impractical for large systems. A potential solution to this is the multi-layer ONIOM scheme implemented in the xtb program suite that allows the combination of efficient force field (FF) and semiempirical quantum mechanical (SQM) approaches with highly accurate methods. It accelerates energy evaluations and geometry optimizations without a significant loss of accuracy, demonstrated for solute–solvent clusters and complex materials such as metal–organic frameworks. Finally, the DipCONFS benchmark and DipCONFL dataset provide insights into large-scale dataset generation and offer nearly 30,000 DFT data points to support the development of approximate methods like machine-learned interatomic potentials capable of treating large systems. Together, the tools and datasets introduced in this work enable more routine and reliable structural exploration and support accurate simulations of complex molecular systems.