PubMed · 305320
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J A Dudgeon. 1977. Commentary: when to begin human trials.. https://pubmed.ncbi.nlm.nih.gov/305320/
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Plants are a vast reservoir of natural products with diverse structural scaffolds, making them an invaluable source for discovering novel enzymes that catalyze unique and evolutionarily specialized metabolic transformations in biosynthetic pathways. Rapid advances in genomics, metabolomics, protein structure prediction, and heterologous pathway reconstruction have enabled the identification of numerous cryptic biosynthetic enzymes responsible for key scaffold-forming and tailoring reactions in metabolism. Particularly notable are the discoveries of plant-derived enzymes that catalyze challenging chemical transformations, including oxidative carbon-carbon bond rearrangements, atypical cycloadditions, radical-mediated coupling reactions, and iterative scaffold remodeling. This review summarizes major advances in enzyme discovery in plant natural product biosynthesis in recent years, focusing on emerging catalytic mechanisms, strategies for elucidating pathways, and evolutionary relationships, and highlights their implications for synthetic biology, metabolic engineering, and the sustainable production of valuable natural products.
Marine natural products (MNPs) from macroalgae and marine sponges have inspired clinically important anticancer agents, including the cytarabine pharmacophore and the eribulin scaffold, while cyanobacterial dolastatin chemistry supplies the auristatin payloads of several marine-inspired antibody-drug conjugates (ADCs) such as brentuximab vedotin. Artificial intelligence (AI) methods, encompassing both classical machine learning (ML) with hand-engineered features and modern deep learning (DL) with many-layered neural networks, are increasingly supporting key decisions in natural-product anticancer drug discovery, including bioactivity prediction, target identification, absorption, distribution, metabolism, excretion and toxicity (ADMET) filtering, generative analogue design, and the selection of preclinical candidates. DL architectures relevant to this field include graph neural networks, transformer-based molecular generators, diffusion models for protein-ligand docking, and convolutional networks for mass spectrometry, while classical ML contributes interpretable fingerprint-based bioactivity models and molecular networking for dereplication. This review follows a systematic literature review methodology to organize the landscape of AI methods now applied to MNP anticancer discovery, distinguishing ML and DL approaches where relevant, situating them within the chemical context of macroalgal and sponge-derived oncology leads, and critically examining published case studies, including validation level (computational, in vitro, in vivo, clinical). The principal bottleneck for medical translation has shifted partly from algorithmic capability toward data infrastructure and experimental validation. Sparse, heterogeneous, and taxonomically biased bioactivity records limit what current models can learn and reduce the reliability of AI-prioritized candidates entering the preclinical pipeline. A roadmap is proposed that prioritizes open MNP-specific benchmarks, symbiont-aware modeling, and active learning loops with synthesizability and ADMET constraints. These AI workflows may accelerate the prioritization of marine-derived anticancer leads and support earlier, more evidence-based translational decisions in oncology drug development.
Cyanobacteria are prolific producers of biologically active compounds that are important in influencing ecology, behavior of interacting organisms, and as leads in drug discovery efforts. Here we discuss the challenges faced by all natural product researchers, especially those that focus on cyanobacteria, and then describe progress that has been made in these areas. We also propose some solutions, paths forward, and thoughts for consideration on these challenges.