Ligand GA is a genetic algorithm (machine learning) tool recently developed that produces small molecule ligands to proteins in a computational context by random changes of chemical structure (mutation) and recombination of existing structural fragments (crossover). The software, written in Matlab [2], creates optimistically high binding molecules based on the structure and chemical composition of the protein binding site. Notably, this is done without the use of external libraries of known small molecules or fragments, but rather with basic molecular manipulations of mutation and crossing over, guided iteratively by docking and scoring to a protein binding site and subject to ADME constraints. Ligand GA thus explores entirely a new and larger molecule search space. The fitness function in this machine learning approach is 0 th order based on molecule structure and CCDC GOLD docking score but is changeable to include a variety of inputs. In this work, only ADME restrictions are used beyond the modeled binding interaction.