Acceleration of Inverse Molecular Design by Using Predictive Techniques

This study addresses one of the most important drawbacks inherently related to molecular searches in chemical compound space by greedy algorithms such as Best First Search and Genetic Algorithm, i.e., the large computational cost required to optimize one or more quantum-chemical properties. Significant speed-ups are obtained by initial property screening via predictive techniques starting already from very small databases. It is shown that the attainable acceleration depends heavily on the molecular properties, the predictive model, the molecular descriptor, and the current size of the database. We discuss the implementation and performance of predictive techniques in molecular searches based on a fixed molecular framework with a selection of sites to be filled with groups from a chemical fragment library. It is shown that for some properties speed-ups of a factor of 5 to even 20 can be obtained, while inverse design procedures on more complex properties still reach speed-ups of a factor of 2 without losing performance.