Generating enhanced graphical user interfaces for presentation of anti-infective design spaces for selecting drug candidates

A tangible, non-transitory computer-readable medium storing instructions that, when executed, cause a processing device to:

• present, in a first screen of the GUI, a design space for a protein for an application, wherein the design space comprises a plurality of protein sequences, wherein:
• each protein sequence is associated with a respective plurality of activities pertaining to the application,
• the plurality of activities comprises one or more biomedical activities, biochemical activities, or some combination thereof,
• the plurality of protein sequences is generated by a machine learning model trained using a plurality of encodings, wherein the machine learning model uses causal inference to execute at least one of a plurality of alternative scenarios to filter the superset of protein sequences and to generate the plurality of protein sequences in the design space;
• present, in a second screen of the GUI, a solution space comprising a subset of the plurality of protein sequences, wherein each protein sequence contains the respective plurality of activities, wherein the subset of the plurality of protein sequences is selected based on one or more query parameters;
• receive, using a graphical element of the second screen, a selection of a sequence from the subset of the plurality of protein sequences;
• and
• present, in a third screen of the GUI, a candidate dashboard comprising information pertaining to the sequence, wherein the information pertains to a structure of the sequence, a correlation heatmap, experimental data, a list of probabilistic scores generated by inference models, external data related to the sequence, or some combination thereof.

A system comprising:

• a memory device storing instructions;
• and
• a processing device communicatively coupled to the memory device, the processing device executes the instructions:
• present, in a first screen of the GUI, a design space for a protein for an application, wherein the design space comprises a plurality of protein sequences, wherein:
• each protein sequence is associated with a respective plurality of activities pertaining to the application,
• the plurality of activities comprises one or more biomedical activities, biochemical activities, or some combination thereof,
• the plurality of protein sequences is generated by a machine learning model trained using a plurality of encodings, wherein the machine learning model uses causal inference to execute at least one of a plurality of alternative
• scenarios to filter the superset of protein sequences and to generate the plurality of protein sequences in the design space;
• present, in a second screen of the GUI, a solution space comprising a subset of the plurality of protein sequences, wherein each protein sequence contains the respective plurality of activities, wherein the subset of the plurality of protein sequences is selected based on one or more query parameters;
• receive, using a graphical element of the second screen, a selection of a sequence from the subset of the plurality of protein sequences;
• and
• present, in a third screen of the GUI, a candidate dashboard comprising information pertaining to the sequence, wherein the information pertains to a structure of the sequence, a correlation heatmap, experimental data, a list of probabilistic scores generated by inference models, external data related to the sequence, or some combination thereof.