Machine learning systems for automated pharmaceutical molecule identification

A system for pharmaceutical molecule identification using machine learning, the system comprising:

• a memory;
• and
• one or more processors communicatively coupled to the memory, the one or more processors configured to:
• obtain pharmaceutical data indicating properties of previously-discovered pharmaceutical molecules from one or more databases,
• wherein the pharmaceutical data includes molecular physiochemical data and one or more of:
• drug impact data,
• side effect data,
• toxicity data,
• solubility data, or
• a combination thereof;
• perform natural language processing (NLP) on at least a portion of the pharmaceutical data to convert the at least a portion of the pharmaceutical data to training data,
• wherein the training data comprises vectorized representations of the properties of the previously-discovered pharmaceutical molecules;
• train one or more machine learning (ML) models based on the training data to configure the one or more ML models to identify additional pharmaceutical molecules,
• wherein the additional pharmaceutical molecules are distinct from the previously-discovered pharmaceutical molecules;
• and
• generate an output that indicates one or more molecules identified by the one or more ML models,
• wherein at least a portion of the output comprises simplified molecular-input line-entry system (SMILES) representations of the one or more molecules identified by the one or more ML models.

A non-transitory computer-readable storage medium storing instructions that, when executed by one or more processors, cause the one or more processors to perform operations for pharmaceutical molecule identification using machine learning, the operations comprising:

• obtaining pharmaceutical data indicating properties of previously-discovered pharmaceutical molecules from one or more databases,
• wherein the pharmaceutical data includes molecular physiochemical data and one or more of:
• drug impact data,
• side effect data,
• toxicity data,
• solubility data, or
• a combination thereof;
• performing natural language processing (NLP) on at least a portion of the pharmaceutical data to convert the at least a portion of the pharmaceutical data to training data,
• wherein the training data comprises vectorized representations of the properties of the previously-discovered pharmaceutical molecules;
• training one or more machine learning (ML) models based on the training data to configure the one or more ML models to identify additional pharmaceutical molecules,
• wherein the additional pharmaceutical molecules are distinct from the previously-discovered pharmaceutical molecules;
• and
• generating an output that indicates one or more molecules identified by the one or more ML models,
• wherein at least a portion of the output comprises simplified molecular-input line-entry system (SMILES) representations of the one or more molecules identified by the one or more ML models.