Abstract: A machine learning system predicts a physicochemical property (e.g., lipophilicity) of candidate small molecules for pharmaceuticals. A machine learning model is constructed that is trained from a database of small molecule physicochemical properties including known lipophilicity and known retention time in a liquid chromatography column to create a learned association between lipophilicity and liquid chromatography retention time. A candidate small molecule having unknown lipophilicity and unknown retention time is applied to a liquid chromatography column. The retention time of the candidate small molecule in the liquid chromatography column is measured. The measured retention time in the liquid chromatography column is applied to the machine learning model to obtain lipophilicity for the candidate small molecule. One or more candidate small molecules having a lipophilicity value from approximately 1 to approximately 3 are selected from the machine learning model.