Utilizing compound-protein machine learning representations to generate bioactivity predictions

A system comprising:

• at least one processor;
• and
• at least one non-transitory computer-readable storage medium storing instructions that, when executed by the at least one processor, cause the system to:
• receive, from a client device, a query compound and at least one of an ADMET target or a target biological program corresponding to a target biological activity;
• identify a plurality of compound-protein pairs comprising the query compound matched to a plurality of proteins;
• generate, utilizing a compound-protein interaction machine learning model from the plurality of compound-protein pairs, a plurality of binding scores between the query compound and the plurality of proteins, wherein the plurality of binding scores indicate likelihoods that the query compound will bind to the plurality of proteins;
• generating a compound-protein machine learning binding representation for the query compound comprising the plurality of binding scores for the plurality of compound-protein pairs, wherein the plurality of compound-protein pairs comprise the query compound matched to the plurality of proteins corresponding to the query compound;
• generate, from the compound-protein machine learning binding representation utilizing a trained target neural network that is different from the compound-protein interaction machine learning model, a predicted bioactivity result for the query compound, wherein the predicted bioactivity result comprises at least one of an ADMET prediction or a biological perturbation program prediction;
• and
• provide, to the client device, the predicted bioactivity result, comprising the at least one of the ADMET prediction or the biological perturbation program prediction, in response to receiving the at least one of the ADMET target or the target biological program.

A non-transitory computer-readable medium storing instructions that, when executed by at least one processor, cause a computing device to:

• receive, from a client device, a query compound and at least one of an ADMET target or a target biological program corresponding to a target biological activity;
• identify a plurality of compound-protein pairs comprising the query compound matched to a plurality of proteins;
• generate, utilizing a compound-protein interaction machine learning model from the plurality of compound-protein pairs, a plurality of binding scores between the query compound and the plurality of proteins, wherein the plurality of binding scores indicate likelihoods that the query compound will bind to the plurality of proteins;
• generating a compound-protein machine learning binding representation for the query compound comprising the plurality of binding scores for the plurality of compound-protein pairs, wherein the plurality of compound-protein pairs comprise the query compound matched to the plurality of proteins corresponding to the query compound;
• generate, from the compound-protein machine learning binding representation utilizing a trained target neural network that is different from the compound-protein interaction machine learning model, a predicted bioactivity result for the query compound, wherein the predicted bioactivity result comprises at least one of an ADMET prediction or a biological perturbation program prediction;
• and
• provide, to the client device, the predicted bioactivity result, comprising the at least one of the ADMET prediction or the biological perturbation program prediction, in response to receiving the at least one of the ADMET target or the target biological program.