The complexity of TMCs poses both challenges and opportunities to the discovery of new complexes. For example, the complicated ligand-ligand interactions makes it difficult to design reasonable ligands that can spatially match the coordination site well in terms of steric hinderance. Steric strain affects the connectivity, ligand arrangement, and the magnetic and electrochemical properties of complexes. On the other hand, each ligand in a complex is structurally independent, which brings much flexibility to the ligand design. Ligands can be partially modified by inserting or removing a functional group or the entire ligand can be replaced by an entirely different ligand, or even more than one ligand can be substituted. The flexible manipulation on ligands enriches the diversity of TMCs. To facilitate the investigation of the binding interactions between metal ion guests and host structures, HostDesigner 70 introduces scoring algorithms to identify promising host component from predefined fragments and the proposed host-guest systems are built via the LINKER and OVERLAY modules. LINKER connects fragments together from the library while OVERLAY superimposes linking fragments onto a predefined complex structure. COSMOS 71 uses a datadriven method to predict 3D structures of small molecules including both organic and organometallic systems. A fragment library which consists of rigid fragments and cyclic fragments was built by decomposing molecules in the Cambridge Structural Database (CSD). For a given query molecule, COSMOS first deconstructs it into fragments, and the query fragments are then matched against the built-in library. Finally, the matched fragments are connected to construct 3D structures, along with adjusting the torsion angles to minimize steric interactions. COSMOS is a biased method because the distribution of fragments in the built-in library is highly imbalanced, indicating that fragments with high frequency are more likely to be matched than fragments with lower occurrence. Chu et al. proposed a fragment-based evolutionary algorithm (EA) to de novo design functional TMCs via a search space of ligand scaffolds. As a generic and population-based optimization strategy, EA provides approximate solutions to problems that are difficult to exhaustively sample. The quality of candidate solutions is evaluated by a fitness function and the evolutionary process is iterated until the predefined property criteria is satisfied. One prerequisite of EA is the presence of fitness functions to guide the optimization process. In the case study, the authors used a QSAR model as the only fitness function to generate ruthenium olefin metathesis catalysts. Although this evolutionary method successfully optimizes multiple highly active catalysts, these promising structures are estimated to be synthetically inaccessible, which limits the practical applications of this method. To overcome this drawback, DENOPTIM 79 introduces a set of predefined connection rules to control the synthetic accessibility. Significant effort to leverage EA to design organometallics has been reported recently, with such approaches as stk, PoreMatMod.ji and NaviCatGA. The Kulik lab has developed molSimplify for the screening of TMCs. This toolkit predefines some common coordination geometries as templates to construct the geometry of a 3D-complex.