Speaker
Description
Computational databases are pivotal in modern chemistry, enabling the advanced data-driven exploration of chemical space. Transition metal complexes are a particularly versatile class of molecules due to their tunability of metal center and coordinating ligands, offering broad applications in therapeutics, catalysis and supramolecular chemistry. However, exploring the vast chemical space of possible molecular complexes remains challenging due to the need for efficient algorithms to generate realistic molecular complexes tailored to specific applications.
In this contribution, we address these challenges by introducing a new Python library named DART (“Directed Assembly of Random Transition metal complexes”)[1]. DART contains a dataset of 41,018 ligands extracted from 107,185 complexes recorded in the Cambridge Structural Database. Using these ligands, the algorithm assembles 3D structures of novel molecular complexes in a high-throughput fashion by combining multiple ligands with a specified metal center. In order to target specific chemical spaces, users can refine the input ligands by applying a selection of powerful ligand filters. All options in DART are set using straightforward yaml input files - making DART accessible to everyone independent of Python expertise and democratizing chemical modelling. As a minimal example, after downloading DART it is a matter of minutes to generate 1000 structures of neutral square-planar Pd(II) complexes with two randomly selected N-O donor ligands with charge of –1, which do not contain any methyl groups.
Overall, we expect our workflow to contribute to a rational approach to high-throughput screening and the generation of new databases of transition metal complexes shared FAIRly to support the accelerated discovery of new molecular complexes for targeted applications.
References:
[1] Sommer, T.; Clarke, C.; Kleuker, F.; García-Melchor, M. Manuscript in preparation.