Background/Objectives: Methicillin-resistant Staphylococcus aureus (MRSA) is one of the resistant pathogenic microorganisms that poses a global health threat due to its resistance to β-lactam antibiotics where the protein penicillin-binding protein 2a (PBP2a) plays a crucial role in its resistance. This study explores the potential of phytochemicals from Uvaria chamae, a plant with known medicinal properties, to serve as dual-site inhibitors of PBP2a, targeting both the active and allosteric sites. Methods: Phytochemicals previously identified in U. chamae were subjected to molecular docking and molecular dynamics simulations to evaluate their binding affinities and stability at PBP2a’s active and allosteric sites. The compounds’ pharmacokinetic profiles were predicted in silico using SwissADME tools. Root-mean-square deviation (RMSD), radius of gyration, and binding free energy were analyzed for dynamic stability. Results: Among the evaluated compounds, Uvarinol and Dichamanetin demonstrated high binding affinities compared to the co-crystallized ligand and standard antibiotics like ceftaroline. Uvarinol exhibited the highest binding affinity at both sites, with a docking score of −14.94 kcal/mol and a predicted inhibition constant (Ki) of 0.01 nM. Molecular dynamics simulations further confirmed the robust stability of Uvarinol and Dichamanetin, as indicated by consistently lower RMSD values relative to the co-crystallized ligand. Pharmacokinetic predictions revealed favorable drug-likeness and low toxicity, although Uvarinol showed limited gastrointestinal absorption. Conclusions: Uvarinol and Dichamanetin show promise as dual-site PBP2a inhibitors, offering a novel strategy to combat MRSA resistance. Their structural and pharmacokinetic properties make them viable candidates for further development, though experimental validation and formulation optimization are necessary to overcome bioavailability challenges.

Molecular docking (MD) have improved our understanding of how bioactive natural products (NPs) interact with Plasmodium Falciparum targets, to elicit their pharmacological actions. MD is done by selecting a few molecular targets, and placing them in the active site, to discover the most affinity-favoured compounds. However, the number of proteins selected for such MD influence ones assertion about the possible binding targets of the NPs under study. Using few protein targets limits docking score comparisons, which is the basis for the identification of potential multi-target NPs for antimalaria therapy. In addition, docking without molecular dynamics simulations (MDS), limits insights about the flexibility of target-ligand interactions in real biological systems. We had proposed multi-target virtual screening (with number of proteins => 10) as a prerequisite in studies evaluating potential binding targets of NPs. This, followed by distribution plots and or heatmap plots, can help prioritize targets and ligands for further MDS and then experimental validation. Using this strategy, we docked 1000+ NPs against 18 high-priority Plasmodium falciparum targets to select high-affinity binders, including compound C761 with multi-target potential against PfPKG and PfMAP. Though binding free energy from MDS did not correlate with docking scores, other results like RMSD, ROG and PCA suggest favourable conformational changes that can be explored for possible dual inhibition of PfPKG and PfAAP by C761. Our study provides a baseline framework to help researchers design better virtual screening workflows in elucidating molecular targets of natural products against Plasmodium falciparum.

This chapter looks at the growing importance of the environment in the process of making bioactive compounds useful in medicine. In the past, drug discovery often ignored the environment or saw it as unimportant. However, here, we suggest a new approach that considers both nonliving (abiotic) and living (biotic) environmental factors, such as climate, soil type, microbes, and ecosystems, when planning how to create these compounds. We introduce the idea of environmental fingerprinting, which involves identifying the unique environmental features that influence how and where certain bioactive compounds are produced. These features help scientists decide which compounds to focus on. By using advanced tools such as artificial intelligence (AI), machine learning, and metabolomics, we show how environmental data can improve the search for new medicines by helping predict how well a compound will work and how active it will be in the body. The chapter shares real-life examples, such as desert plants that produce helpful chemicals during droughts and marine bacteria that produce antibiotics in saltwater. These examples show how environmental conditions affect both the variety and power of natural compounds. This new way of thinking could greatly improve how we find new drugs, especially by helping us discover compounds suited to specific environments. The chapter also discusses the challenges of combining environmental data with computer models, such as missing data, ethical concerns, and the need for scientists from different fields to work together. By shifting the paradigm of retrosynthesis toward an environment-driven approach, the chapter paves the way for more efficient and ecologically informed therapeutic innovation.

Neglected tropical diseases (NTDs) continue to place a heavy burden on low- and middle-income countries. In these regions, drug development is often slow and expensive, and NTDs are rarely a priority due to limited financial incentives and complex science. This chapter looks at how combining drug repurposing and polypharmacology can offer faster and more affordable ways to discover treatments for NTDs. Using ideas from computational systems biology, we outline both a theoretical and practical approach to exploring lesser-known drug targets through multi-target strategies. We highlight modern computer-based tools, like network pharmacology, omics data analysis, and machine learning, that help identify new links between drugs and diseases in both humans and pathogens. We focus especially on in silico (computer-based) drug repurposing pipelines and how databases like DrugBank, ChEMBL, and SchistoDB can help match existing drugs to neglected diseases. Real-life examples, such as Miltefosine for leishmaniasis, the broad potential of Ivermectin, and using statins to treat schistosomiasis, show how targeting multiple disease pathways can be effective. The chapter also addresses challenges, such as gaps in our understanding of pathogens, drug delivery issues, and regulatory barriers, especially in settings with fewer resources. Looking ahead, we support moving toward more precise tropical medicine by combining genetic and biological data with AI tools, while keeping research ethically and inclusive of local communities. In summary, the chapter shows how drug repurposing can be a powerful and fair way to improve treatment options for neglected diseases.

Glucagon-like peptide-1 receptor (GLP-1R) is a focal target in antidiabetic drug discovery. Synthetic non-peptide compounds are routinely employed to investigate GLP-1R, however, scaffolds from natural products will offer us unique structural diversity to design more potent compounds. We investigated the potential of some natural compounds as agonists of GLP-1R. First, we carried out a virtual screening of 244 compounds, from 19 medicinal plants used in antidiabetic traditional medicine, against GLP-1R. We used molecular dynamics simulations, for 200ns, to further investigate the lead five compounds. Binding Free Energies (BFE), Root Mean Square Deviation (RMSD) and other parameters were employed to understand these interactions. Our results showed that lupeol (-57.23) had good BFE (kcal/mol) compared to danuglipron (-56.47), thus demonstrating more robust interactions with GLP-1R. The pocket-buried residues, CYS191 and GLN186, showed strong hydrogen bonds while the critical residue, TRP4, showed strong van der Waals interactions to stabilize the ligands. Moreover, the average RMSD (Å) of lupeol-bound GLP-1R (3.45) are comparable to danuglipron-bound GLP-1R (3.33), indicating good stability in the pocket. This potentially situates lupeol as GLP-1R agonist similar to danuglipron, and thus opens up the opportunity for more further experimental work, to validate its agonist properties on GLP1R.