Abstract: Methods and systems are disclosed in which an automated or semi-automated laboratory may be combined with a machine learning methodology to enable predicting outcomes of chemical reactions or to predict reaction conditions. The model may be trained on reactions including data from the laboratory, purposefully selected to satisfy a desired goal by a user. The user can interact with the process and the model via dedicated user interfaces designed to enable efficient user-machine interaction. The method can be used in the context of multiple challenging problems in chemistry such as steering an automated chemistry laboratory, synthesizing a large collection of compounds such as DNA encoded library, or recommending high yielding reaction conditions for reactions involving drug-like compounds.