In ChEMBL broad assay categorizations are available but it does not contain groupings of the sameor closely related-assays. Instead, each publication/record is assigned a unique assay ID. Similarly, for our NLP approach using BioBERT (Figure ), we created a novel, detailed assay categorization using BERTopic to cluster biological assays based on assay descriptions. Briefly, we employed UMAP and semi-supervised UMAP representations as input for HDBSCAN clustering with a varied minimum cluster size, and c-TF-IDF was used to assign outliers to their closest related cluster. The clustering performance on the whole dataset was evaluated based on the 'purity' of the preannotated labels within different categories. Here normalized purity is defined as the sum of the frequencies of the dominant class labels for all clusters divided by the total number of records, and min-max scaled by the prevalence of the most frequently occurring label per property (meaning that no clustering/random clustering leads to a normalized purity of 0). For assay type, BAO format and standard type, the most frequently occurring labels have a prevalence of 0.63, 0.35, and 0.30, respectively. Figure shows that clustering increases normalized purity. Records with the same assay type and BAO format are more successfully grouped together than ones with the same the standard type. This analysis also shows that outlier reduction has a beneficial effect on cluster normalized purity and that increasing the minimum cluster size reduces normalized purity.