On the other hand, because of a large number of parameters in the virtual ligand, it is not a simple task to transfer the optimized parameters to a real molecule. Therefore, the discussions about ligand engineering in this paper were only qualitative. In addition, as showcased by the example of PCp3 (Figure ), because a single set of VL parameters cannot describe conformational flexibility of a ligand, the prediction must be made carefully (e.g., use the Boltzmann weighted average). We believe that these drawbacks can be overcome once a machine learning model that maps VL parameters to the chemical space of ligands while considering conformational flexibility is established. The optimized parameters can be then utilized as quantitative guides to pick up promising candidates from databases 25 such as the ligand knowledge base (LKB) and the kraken. Execution of such informatic processing is one of the tasks in the near future.