Molecular representations have essential roles in bio-cheminformatics as they facilitate the growth of machine learning applications in numerous sub-domains of biology and chemistry, especially drug discovery. These representations transform the structural and chemical information of molecules into machine-readable formats that can be efficiently processed by computer programs. In this paper, we present a comprehensive review, providing readers with diverse perspectives on the strengths and weaknesses of well-known molecular representations, along with their respective categories and implementation sources. Moreover, we provide a summary of the applicability of these representations in de novo molecular design, molecular property prediction, and chemical reactions. Besides, representations for macromolecules are discussed with highlighted pros and cons. By addressing these aspects, we aim to offer a valuable resource on the significant role of molecular representations in advancing bio-cheminformatics and its related domains.

Glioblastoma is a highly malignant cancer with no effective treatment. It is vital to elucidate the mechanisms which drive glioblastoma in order to identify therapeutic targets. The differences in protein expression between glioblastoma, grade I–III glioma, and normal brain tissue reflect the functional alterations driving malignancy. However, proteomic analysis of glioblastoma has been hampered by the heterogeneity of glioblastoma and the variety of methodology used in its study. To reduce these inconsistencies, we performed a meta-analysis of the literature published since 2015, including 14 datasets from eight papers comparing the whole proteome of glioblastoma to normal brain or grade I–III glioma. We found that 154 proteins were commonly upregulated and 116 proteins were commonly downregulated in glioblastoma compared to normal brain. Meanwhile, 240 proteins were commonly upregulated and 125 proteins were commonly downregulated in glioblastoma compared to grade I–III glioma. Functional enrichment analysis revealed upregulation of proteins involved in mRNA splicing and the immune system and downregulation of proteins involved in synaptic signaling and glucose and glutamine metabolism. The identification of these altered biological pathways provides a basis for deeper investigation in the pursuit of an effective treatment for glioblastoma.

Pateamine A is a naturally occurring metabolite extracted from the marine sponge Mycale hentscheli. It exhibits potent cytotoxicity towards cancer cell lines and has been shown to target protein translation initiation via inhibition of the function of eukaryotic initiation factor 4A proteins. We have synthesised a simplified analogue of pateamine A, consisting of the skeletal core of the natural product but with the thiazole heterocycle replaced by a triazole. The convergent design of the synthesis features a base-induced opening of a δ-valerolactone to access the Z,E-dienoate moiety, Julia–Kocienski olefination and copper-catalysed azide–alkyne cycloaddition. Bioactivity testing of the simplified pateamine A analogue (3) indicated a significant reduction in cytotoxicity, compared to natural pateamine A. We propose that this reduced activity is due mainly to the substitution of the thiazole for the triazole heterocycle. This supports the hypothesis that the thiazole of pateamine A is important for binding to its biological target.