Shahrekord University of Medical Sciences Future Natural Products 2783-4662 8 2 2022 12 31 Phytochemistry-five decades of research in Africa: A bibliometric analysis 42 58 10.34172/fnp.2022.09 EN Adeiza Shuaibu Suleiman https://orcid.org/0000-0002-9293-2600 Mansur Lawal https://orcid.org/0000-0003-3502-6510 Shaibu Adona Sadiku https://orcid.org/0000-0002-0355-8777 Journal Article 10.34172/fnp.2022.09 2022 09 20 2022 11 07 Background and aims: This study used bibliometric tools to quantitatively achieve a structural overview of research characteristics and potentials of phytochemistry in Africa within the last five decades (1970–2022). Methods: A total of 2662 phytochemistry related publications from 822 sources published between 1970 and 2022 were identified from Dimensions database and subjected to bibliometrics analysis using Bibliometrix package and VOSviewer software. Results: The publications span 9775 authors, 30 African countries, and 1142 organizations. In terms of research themes, text mining for high- relevance/frequency keywords revealed that "Phenol" (7.3182) and "Flavonoid" (5.3637) were the most cited plant metabolites among all publications. The key nounphrases for solvents were "Aqueous", "methanol" and "ethanol". The most cited terms in plant family were "Tamaricaceae" (4.9575) and "Lamiaceae" (4.9273); plant species, "Acacia nilotica" (4.4909) and "Aloe barbadensis" (4.3946); bacteria strains, "Klebsiella pneumoniae" (4.6932) and "Staphylococcus aureus" (3.3538); fungal species, "Aspergillus" (2.7228) and "Penicillium notatum" (2.4054); Viral strains, "Human immune deficiency virus" (3.3482) and "Hepatitis C virus" (3.2796); parasites, "Plasmodium" (12.0576) and "Leishmania sp" (8.3602). The most cited methods of detection and analysis of phytochemicals were "Gas chromatography mass spectrometry" (1.7256) and "High performance liquid chromatography" (1.6889). Interactive Site Suitability Models, orthogonal partial least squares discriminant analysis (OPLS-DA), plant organically bound tritium (OBT), and quantitative structure–activity relationship models (QSAR models) were the most cited models. Inequality in the geographical distribution of publication output was the source of concern. Conclusion: A drive towards computational phytochemistry could be detected as an important change in research focus.