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.

Abstract: Methods and systems provide proposed pathways for synthesizing chemical reactions given a user-proposed target molecule, user-provided reaction constraints, or a combination of both. Embodiments may leverage training the model using both known successful reactions and infeasible reactions, either known or created by a prior use of the model. Chemical reactions for producing the target molecule and substrates are proposed using the model. From the proposed reactions, synthesis pathways are extracted and ranked according to a cost estimation. The ranked synthesis pathways are then provided to the user.