Typical scoring components in MPO include physical, chemical and predicted properties of a molecule, and desirability functions are used to define which values are preferred for each property. Once the scoring function is known, various machine learning methods can be used to explore the chemical space and generate novel molecular structures, including Monte Carlo tree search based on SMILES (simplified molecular-input line-entry system) strings ,