Natural products, with remarkable chemical diversity, have been extensively investigated for their anticancer potential for more than a half-century. The collective efforts of the community have achieved the tremendous advancements, bringing natural products to clinical use and discovering new therapeutic opportunities, yet the challenges remain ahead. With remarkable changes in the landscape of cancer therapy and growing role of cutting-edge technologies, we may have come to a crossroads to revisit the strategies to understand nature products and to explore their therapeutic utility. This review summarizes the key advancements in nature product-centered cancer research and calls for the implementation of systematic approaches, new pharmacological models, and exploration of emerging directions to revitalize natural products search in cancer therapy.

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Natural Products in Cancer Therapy: Past, Present and Future.

Abnormal N6-methyladenosine (m6A) modification is closely associated with the occurrence, development, progression and prognosis of cancer, and aberrant m6A regulators have been identified as novel anticancer drug targets. Both traditional medicine-related approaches and modern drug discovery platforms have been used in an attempt to develop m6A-targeted drugs. Here, we provide an update of the latest findings on m6A modification and the critical roles of m6A modification in cancer progression, and we summarize rational sources for the discovery of m6A-targeted anticancer agents from traditional medicines and computer-based chemosynthetic compounds. This review highlights the potential agents targeting m6A modification for cancer treatment and proposes the advantage of artificial intelligence (AI) in the discovery of m6A-targeting anticancer drugs. Three stages of m6A-targeting anticancer drug discovery: traditional medicine-based natural products, modern chemical modification or synthesis, and artificial intelligence (AI)-assisted approaches for the future. 

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m6A modification: recent advances, anticancer targeted drug discovery and beyond

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Natural products (NPs) are primarily recognized as privileged structures to interact with protein drug targets. Their unique characteristics and structural diversity continue to marvel scientists for developing NP-inspired medicines, even though the pharmaceutical industry has largely given up. High-performance computer hardware, extensive storage, accessible software and affordable online education have democratized the use of artificial intelligence (AI) in many sectors and research areas. The last decades have introduced natural language processing and machine learning algorithms, two subfields of AI, to tackle NP drug discovery challenges and open up opportunities. In this article, we review and discuss the rational applications of AI approaches developed to assist in discovering bioactive NPs and capturing the molecular "patterns" of these privileged structures for combinatorial design or target selectivity. 

Natural product drug discovery in the artificial intelligence era

Natural products are continually explored in the development of new bioactive compounds with industrial applications, attracting the attention of scientific research efforts due to their pharmacophore-like structures, pharmacokinetic properties, and unique chemical space. The systematic search for natural sources to obtain valuable molecules to develop products with commercial value and industrial purposes remains the most challenging task in bioprospecting. Virtual screening strategies have innovated the discovery of novel bioactive molecules assessing in silico large compound libraries, favoring the analysis of their chemical space, pharmacodynamics, and their pharmacokinetic properties, thus leading to the reduction of financial efforts, infrastructure, and time involved in the process of discovering new chemical entities. Herein, we discuss the computational approaches and methods developed to explore the chemo-structural diversity of natural products, focusing on the main paradigms involved in the discovery and screening of bioactive compounds from natural sources, placing particular emphasis on artificial intelligence, cheminformatics methods, and big data analyses.

Applications of Virtual Screening in Bioprospecting: Facts, Shifts, and Perspectives to Explore the Chemo-Structural Diversity of Natural Products.

Machine learning approaches for elucidating the biological effects of natural products.

A phytochemical study on the aerial parts of Leonurus japonicus led to the isolation and identification of 38 labdane diterpenoids, including 18 new (1, 2, 11, 12, 16-21, 24, 30-34, 37, 38) and 20 known (3-10, 13-15, 22, 23, 25-29, 35, 36) analogues. Their structures were elucidated based on physical data analysis, including 1D and 2D NMR, HRMS, UV, IR, and X-ray diffraction. The structure of the known compound 4 was confirmed by single-crystal X-ray diffraction data. These compounds can be divided into furanolabdane (1-10), tetrahydrofuranolabdane (11-15), lactonelabdane (16-23), labdane (24-29), and seco-labdane (30-38) type diterpenoids. All compounds were screened by lipopolysaccharide (LPS)-induced nitric acid (NO) production in RAW264.7 cells to evaluate anti-inflammatory effects. Compounds 1, 5, 10-13, 16-19, 31-33, and 38 inhibited NO production with IC50 values lower than 50 μM, with compound 30 being the most active, with an IC50 value of 3.9 ± 1.7 μM. Further studies show that compound 30 inhibits pro-inflammatory cytokine production and IKK α/β phosphorylation and restores the IκB expression levels in the NF-κB signaling pathway.

Structurally Diverse Labdane Diterpenoids from Leonurus japonicus and Their Anti-inflammatory Properties in LPS-Induced RAW264.7 Cells.

The invention discloses a 6-(1,3-benzodioxo-5-(methyl))-substituted uracil derivative, a synthesis method and application thereof, and a drug, wherein the structural general formula is represented bya formula I, n is equal to 0 or 1, R1 is selected from C1-C3 alkyl, and R2 is any one selected from allyl, a group with a structural formula represented by a formula (1) and a group with a structuralformula represented by a formula (2). According to the invention, by using an S-DABO compound and a diphenylamine ester compound SPJ-L-5 as lead materials, based on a pharmacophore splicing and molecular heterozygosis drug molecule design strategy, a core pharmacophore 1,3-benzodioxy of SPJ-L-5 is introduced into a C-6 site of a pyrimidine ring of an S-DABO compound while a preferred group at a C-2 site is reserved, so that excellent anti-HIV drug candidates with broad spectrum, high efficiency and moderate price are developed.

Qin Huayan

Papers

Machine learning approaches for elucidating the biological effects of natural products.

Structurally Diverse Labdane Diterpenoids from Leonurus japonicus and Their Anti-inflammatory Properties in LPS-Induced RAW264.7 Cells.

6-(1,3-benzodioxy-5-(methyl)-substituted uracil derivative, synthesis method and application thereof, and medicine