Natural Products in Cancer Therapy: Past, Present and Future

Natural Products and Bioprospecting, Год журнала: 2021, Номер 11(1), С. 5 - 13

Опубликована: Янв. 3, 2021

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

Язык: Английский

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

Molecular Cancer, Год журнала: 2022, Номер 21(1)

Опубликована: Фев. 14, 2022

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

Язык: Английский

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Natural product drug discovery in the artificial intelligence era

Chemical Science, Год журнала: 2021, Номер 13(6), С. 1526 - 1546

Опубликована: Дек. 13, 2021

Natural products (NPs) are primarily recognized as privileged structures to interact with protein drug targets.

Язык: Английский

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

Frontiers in Chemistry, Год журнала: 2021, Номер 9

Опубликована: Апрель 29, 2021

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

Язык: Английский

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Toward autonomous laboratories: Convergence of artificial intelligence and experimental automation

Progress in Materials Science, Год журнала: 2022, Номер 132, С. 101043 - 101043

Опубликована: Ноя. 11, 2022

Язык: Английский

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Machine Learning in Antibacterial Drug Design

Frontiers in Pharmacology, Год журнала: 2022, Номер 13

Опубликована: Май 3, 2022

Advances in computer hardware and the availability of high-performance supercomputing platforms parallel computing, along with artificial intelligence methods are successfully complementing traditional approaches medicinal chemistry. In particular, machine learning is gaining importance growth available data collections. One critical areas where this methodology can be applied development new antibacterial agents. The latter essential because high attrition rates drug discovery, both industry academic research programs. Scientific involvement area even more urgent as resistance becomes a public health concern worldwide pushes us increasingly into post-antibiotic era. review, we focus on latest used discovery agents targets, covering small molecules peptides. For benefit reader, summarize all databases useful for design address current shortcomings.

Язык: Английский

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Current Landscape of Methods to Evaluate Antimicrobial Activity of Natural Extracts

Molecules, Год журнала: 2023, Номер 28(3), С. 1068 - 1068

Опубликована: Янв. 20, 2023

Natural extracts have been and continue to be used treat a wide range of medical conditions, from infectious diseases cancer, based on their convenience therapeutic potential. products derived microbes, plants, animals offer broad variety molecules chemical compounds. are not only one the most important sources for innovative drug development animal human health, but they also an inspiration synthetic biology chemistry scientists towards discovery new bioactive compounds pharmaceuticals. This is particularly relevant in current context, where antimicrobial resistance has risen as global health problem. Thus, efforts being directed toward studying natural compounds’ composition potential generate drugs with better efficacy lower toxicity than existing molecules. Currently, methodologies analyze vitro activity determine suitability agents. Despite traditional technologies employed, technological advances contributed implementation methods able circumvent issues related analysis capacity, time, sensitivity, reproducibility. review produces updated conventional evaluate

Язык: Английский

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Frontier studies on natural products: moving toward paradigm shifts

Science China Chemistry, Год журнала: 2023, Номер 66(4), С. 928 - 942

Опубликована: Янв. 31, 2023

Язык: Английский

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Advances in machine learning screening of food bioactive compounds

Trends in Food Science & Technology, Год журнала: 2024, Номер 150, С. 104578 - 104578

Опубликована: Июнь 6, 2024

Язык: Английский

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Predicting fungal secondary metabolite activity from biosynthetic gene cluster data using machine learning

Microbiology Spectrum, Год журнала: 2024, Номер 12(2)

Опубликована: Янв. 9, 2024

Fungal secondary metabolites (SMs) contribute to the diversity of fungal ecological communities, niches, and lifestyles. Many SMs have one or more medically industrially important activities (e.g., antifungal, antibacterial, antitumor). The genes necessary for SM biosynthesis are typically located right next each other in genome known as biosynthetic gene clusters (BGCs). However, whether bioactivity can be predicted from specific attributes BGCs remains an open question. We adapted machine learning models that bacterial BGC data with accuracies high 80% data. trained our predict cytotoxic/antitumor on two sets: (i) (data set comprised 314 BGCs) (ii) (314 (1,003 BGCs). found had balanced between 51% 68%, whereas training 56% 68%. low prediction accuracy bioactivities likely stems small size set; this lack data, coupled finding including did not substantially change currently limits application approaches studies. With >15,000 characterized SMs, millions putative genomes, increased demand novel drugs, efforts systematically link urgently needed.IMPORTANCEFungi key sources natural products iconic penicillin statins. DNA sequencing has revealed there pathways but chemical structures >99% produced by these remain unknown. used artificial intelligence diverse pathways. predictions were generally low, because only very few known. products, present study suggests is urgent need identify pathways, their bioactivities.

Язык: Английский

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