MOTIVATION: Graphs have been demonstrated to have an impressive ability to keep the structural and semantic properties of chemical compounds and are therefore widely used in molecular modeling. Moreover, Graph Neural Networks (GNNs) and Large Language Models (LLMs) have achieved outstanding results in predicting molecular properties and generating textual descriptions given graphs or texts, respectively. Recently, some mathematical models have been presented for molecular science applications, which combine the GNNs ability to capture the structural and semantic information and the generative ability of LLMs. However, these models are dispersed across the literature and vary in architecture, input-output design, and application scope, making it difficult to systematically compare them or to identify suitable approaches for specific research objectives. RESULTS: This paper classifies recent GNN-LLM models and summarizes them with the aim of providing guidance for research involving their use or the development of new models. We present tables that depict the properties and parameters of the models, as well as their associated chemical computational applications. In addition, a new model classification is presented to help researchers define the models they use or future ones. Finally, we quantitatively compare these models based on their reported experimental results.

Agglomerative hierarchical clustering has become a common tool for the analysis and visualization of data, thus being present in a large amount of scientific research and predating all areas of bioinformatics and computational biology. In this work, we focus on a critical problem, the nonuniqueness of the clustering when there are tied distances, for which several solutions exist but are not implemented in most hierarchical clustering packages. We analyze the magnitude of this problem in one particular setting: the clustering of microsatellite markers using the Unweighted Pair-Group Method with Arithmetic Mean. To do so, we have calculated the fraction of publications at the Scopus database in which more than one hierarchical clustering is possible, showing that about 46% of the articles are affected. Additionally, to show the problem from a practical point of view, we selected two opposite examples of articles that have multiple solutions: one with two possible dendrograms, and the other with more than 2.5 million different possible hierarchical clusterings.