Special Issue "Virtual Screening of Natural Product Databases for Drug Discovery"
Deadline for manuscript submissions: 29 December 2023 | Viewed by 5761
Interests: virtual screening; molecular modeling; natural products
Interests: drug design; molecular modeling; bioactive compounds
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For centuries, natural products have been the only source of bioactive compounds for therapeutic purposes. In the mid-1970s, they dominated the sources of novel human therapeutics in the pharmaceutical drug pipeline, particularly for cancer and infectious diseases. Indeed, two-thirds of the currently available drugs originated from unaltered natural products and their analogues, or contain natural product pharmacophores. Currently, approximately 200,000–250,000 natural products have been identified, but it is estimated that less than 10% of the world’s biodiversity has been evaluated for possible therapeutic applications. Despite being a proven source for modern small-molecule drug discovery, natural products also present challenges for drug discovery, such as technical barriers to isolation, characterization, screening, and optimization, which have contributed to a decline in their pursuit by the pharmaceutical industry. Natural products, indeed, are often structurally complex, presenting several hydroxyl and ketone substituents and many chiral centers, and usually have high polarity and molecular weight, which makes them unique and extremely diverse compared to synthetic molecules, but also very difficult to synthetize at a large scale. However, the advent of new technologies, such as improved analytical tools, genome mining and engineering strategies, microbial culturing advances, and computational methods, has allowed focus on the most promising candidates, thus opening new opportunities for natural products in drug discovery. In silico virtual screening is one of the most useful computational methods for filtering large databases of natural compounds. Several virtual screening protocols could be applied to natural product libraries, including, but not limited to, cheminformatics, ligand-, pharmacophore-, and structure-based approaches, machine learning, and molecular dynamics. This Special Issue will cover all different aspects of virtual screening methodology and applications, including ligand-, pharmacophore-, and structure-based approaches leading to the identification of hit compounds or to optimized leads. Potential topics include, but are not limited to:
- Ligand-, pharmacophore-, and structure-based virtual screening of natural compounds;
- Artificial intelligence applied to the discovery of drugs from natural sources;
- Repurposing of natural drugs;
- Hit-to-lead optimization of natural compounds;
- Computational approaches for the prediction of the ADME properties of natural products.
Please note: experimental validation of in silico results is mandatory for this special issue.
Dr. Paolo Governa
Prof. Dr. Fabrizio Manetti
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2900 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
- virtual screening
- natural products
- pharmacophore modeling
- molecular docking
- drug discovery
- hit-to-lead optimization
- drug repurposing
- artificial intelligence
- machine learning