Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods for utilizing language machine learning model (LLM) as autonomous reasoners to navigate and execute multiple layers of a computerized bio-activity discovery pipeline of a tech-bio exploration system. In particular, the disclosed systems can utilize an LLM that learns to access one or more tech-bio exploration tools to execute one or more processes and/or tasks in a bio-activity discovery pipeline. For instance, the disclosed systems can provide an interactive query prompt interface to enable users to provide tech-bio queries (as prompts) and utilize the LLM with the prompts to execute one or more tasks in the bio-activity discovery pipeline to generate and/or retrieve bio-activity data for the query. Moreover, the disclosed systems can utilize one or more LLMs to autonomously utilize and/or interact with one or more tech-bio tools in the bio-activity discovery pipeline to generate and/or obtain bio-activity data.

Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods for generating a sequential attachment-based fragment embedding (SAFE) molecular string representation that represents a molecular representation as an order agnostic sequence of interconnected fragment blocks. Indeed, the disclosed systems can generate the SAFE representation for processing via large language models for downstream molecular design tasks. For instance, the disclosed systems can extract fragments (and attachment points) from a molecular string representation, concatenate the extracted fragments using separation character connections between the fragments to generate a set of linked fragments, and can iterate over attachment points for the fragments to generate ring link characters in the set of linked fragments to simulate fragment links. In addition, the disclosed systems can utilize the SAFE representation to enable various downstream fragment-based molecular design tasks via large language models.

Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods for utilizing language machine learning model (LLM) as autonomous reasoners to navigate and execute multiple layers of a computerized bio-activity discovery pipeline of a tech-bio exploration system. In particular, the disclosed systems can utilize an LLM that learns to access one or more tech-bio exploration tools to execute one or more processes and/or tasks in a bio-activity discovery pipeline. For instance, the disclosed systems can provide an interactive query prompt interface to enable users to provide tech-bio queries (as prompts) and utilize the LLM with the prompts to execute one or more tasks in the bio-activity discovery pipeline to generate and/or retrieve bio-activity data for the query. Moreover, the disclosed systems can utilize one or more LLMs to autonomously utilize and/or interact with one or more tech-bio tools in the bio-activity discovery pipeline to generate and/or obtain bio-activity data.

Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods for generating a sequential attachment-based fragment embedding (SAFE) molecular string representation that represents a molecular representation as an order agnostic sequence of interconnected fragment blocks. Indeed, the disclosed systems can generate the SAFE representation for processing via large language models for downstream molecular design tasks. For instance, the disclosed systems can extract fragments (and attachment points) from a molecular string representation, concatenate the extracted fragments using separation character connections between the fragments to generate a set of linked fragments, and can iterate over attachment points for the fragments to generate ring link characters in the set of linked fragments to simulate fragment links. In addition, the disclosed systems can utilize the SAFE representation to enable various downstream fragment-based molecular design tasks via large language models.

Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods for utilizing language machine learning model (LLM) as autonomous reasoners to navigate and execute multiple layers of a computerized bio-activity discovery pipeline of a tech-bio exploration system. In particular, the disclosed systems can utilize an LLM that learns to access one or more tech-bio exploration tools to execute one or more processes and/or tasks in a bio-activity discovery pipeline. For instance, the disclosed systems can provide an interactive query prompt interface to enable users to provide tech-bio queries (as prompts) and utilize the LLM with the prompts to execute one or more tasks in the bio-activity discovery pipeline to generate and/or retrieve bio-activity data for the query. Moreover, the disclosed systems can utilize one or more LLMs to autonomously utilize and/or interact with one or more tech-bio tools in the bio-activity discovery pipeline to generate and/or obtain bio-activity data.