Assay2Mol: large language model-based drug design using BioAssay context
This addresses the challenge of utilizing untapped unstructured assay data for early-stage drug design, offering a domain-specific incremental improvement.
The authors tackled the problem of leveraging unstructured text from biochemical screening assays for drug discovery by developing Assay2Mol, a large language model-based workflow that retrieves similar assay records and generates candidate molecules, outperforming recent machine learning approaches and improving synthesizability.
Scientific databases aggregate vast amounts of quantitative data alongside descriptive text. In biochemistry, molecule screening assays evaluate candidate molecules' functional responses against disease targets. Unstructured text that describes the biological mechanisms through which these targets operate, experimental screening protocols, and other attributes of assays offer rich information for drug discovery campaigns but has been untapped because of that unstructured format. We present Assay2Mol, a large language model-based workflow that can capitalize on the vast existing biochemical screening assays for early-stage drug discovery. Assay2Mol retrieves existing assay records involving targets similar to the new target and generates candidate molecules using in-context learning with the retrieved assay screening data. Assay2Mol outperforms recent machine learning approaches that generate candidate ligand molecules for target protein structures, while also promoting more synthesizable molecule generation.