To group similar assays, we performed neural topic modeling based on the assay descriptions available in ChEMBL. To create a generalizable description grouping, clustering with BERTopic was applied to all unique assay descriptions in ChEMBL (with < 500 characters). First, assay embeddings were created using BioBERT as described previously. These embeddings were then reduced to 5 dimensions using Uniform Manifold Approximation and Projection (UMAP) as is the default setting in BERTopic. Apart from unsupervised dimensionality reduction, we also tested semi-supervised UMAP. To this end, the categorical labels, assay type, BAO format and standard type from ChEMBL were used. The number of neighbors used for manifold approximation was set to 15 and the minimum distance between embedded points to 0.1. These embeddings were then clustered using Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN). In order to vary the level of detail captured by the topics, HDBSCAN was fitted with 16, 32, 68 and 126 as minimum cluster size. Using BERTopic, the word importance within a cluster was assigned with class-based term frequency-inverse document frequency (c-TF-IDF). Descriptions that were not assigned to a cluster after training, hereafter referred to as outliers, were assigned to the most similar cluster based on c-TF-IDF. Because this is a stochastic clustering approach results are given as the mean ± standard deviation of three repeats with different seeds. We have made Jupyter Notebook-based tutorials to show how to obtain these clusters for any ChEMBL dataset of interest and how to get information on the underlying pharmacological experiments using a pretrained large-language model ().