Results for “Seaweed”

PubMed2026

Phylogenomics and female reproductive morphology reframe the classification of the Halymeniales (Rhodophyta).

The red algal order Halymeniales (Rhodophyta) exhibits remarkable morphological and taxonomic diversity but its higher-level relationships remain poorly resolved. Here, we present a comprehensive phylogenomic analysis based on newly generated plastid (170 protein-coding genes), mitochondrial (23 genes), and complete nuclear ribosomal cistron sequences from 56 taxa, complemented with an expanded rbcL dataset encompassing 334 sequences. Our results provide a robust phylogenomic framework for the Halymeniales, offering a taxonomic backbone for future systematic studies. The analyses consistently recover six early-diverging lineages (Acrodiscus, Isabbottia, Norrissia, Pachymenia, Zymurgia, and Tsengia) and two strongly supported larger clades (Halymenia s.l. and Grateloupia s.l.). While most small and recently described genera are monophyletic, several traditional genera (e.g., Halymenia, Cryptonemia, Grateloupia) are poly- or paraphyletic, requiring considerable taxonomic revision. At the family level, the data indicate that reinstatement of the Grateloupiaceae sensu Kim et al. (2021) would entail a revised circumscription of the Halymeniaceae and the recognition of at least five small families to accommodate the early-diverging lineages. Although such a revised classification would result in monophyletic families, it is not supported by morpho-anatomical characters. Instead, we propose a more stable two-family system, recognizing a broadly circumscribed Halymeniaceae that is sister to the Tsengiaceae. Female reproductive characters, particularly the structure of carpogonial and auxiliary cell ampullae, support this two-family system and further characterize many genus-level clades, although substantial convergence across lineages exists.

Phylogeny↗

PubMed2026

Artificial intelligence for anticancer drug discovery from natural products of macroalgae and sponges: A systematic review.

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.

Biological Products↗