An investigation of the drug discovery potential of phytochemicals from Monodora species through in silico ADMET profiling

Abstract

• Unlike many previous studies on African medicinal plants, this work presents the first comprehensive in silico DMPK profiling of phytochemicals from the genus Monodora . • More than 100 molecular descriptors were computed using three complementary computational tools, ensuring robustness and cross-validation of results. • The Monodora dataset was benchmarked against over 600 natural products with reported anti-SARS-CoV-2 activity, providing a unique comparative perspective. • This study not only identifies 132 structurally diverse compounds but also makes the curated dataset publicly available to support further molecular docking and target prediction studies. Phytochemicals are natural products of plant origin and hold a promise for drug discovery because of the traditional uses of medicinal plants. The genus Monodora is known for diverse traditional uses in ethnomedicine, and the present phytochemicals show diversity in compound classes. The compounds have been identified from the plants' different parts (e.g., roots, leaves, and stem bark), but the drug discovery potential of their constituent phytochemicals has not been investigated. The investigation of the DMPK profiles of small molecules early enough in drug discovery helps reduce the attrition rates. In this study, we have identified 132 phytochemicals with diverse biological properties from seven plant species from this genus, and these belong to several compound classes, including alkaloids, terpenoids, coumarins, etc. More than 100 molecular properties, often related to drug metabolism and pharmacokinetics (DMPK) were then computed for this dataset using three tools (SwissADME, QikProp and pkCSM), and compared with a dataset of more than 600 natural products reported with activities against the SARS-CoV-2 virus or its viral target proteins. It was demonstrated that the Monodora compounds outperformed the SARS-CoV-2 compounds for several key parameters, including those related to drug absorption, distribution, and toxicity. This small, diverse Monodora dataset holds promises for drug discovery and has been made available for working groups interested in molecular docking techniques and putative target determination. This study demonstrates that phytochemicals from Monodora not only show drug-likeness and favorable DMPK profiles but also outperform benchmark datasets of natural products already studied for antiviral activity.