Special Issue "Computer-Aided Drug Design and Drug Discovery"
Deadline for manuscript submissions: 31 March 2024 | Viewed by 1378
Interests: pharmacology; drug discovery; drug repurposing; virtual screening; small molecule drugs; neurodegenerative diseases; TRP channels
Interests: drug design; computer-assisted drug design studies; heterocyclic compounds; design and synthesis of new anticancer agents; design and synthesis of new antimicrobial compounds; studies and structural analysis; isolation and analysis of natural compounds
Special Issues, Collections and Topics in MDPI journals
Special Issue in AI: AI in Drug Design
Special Issue in Molecules: Privileged Heterocyclic Scaffolds in Anticancer Drug Development
Special Issue in Pharmacy: Dietary Supplements: From Manufacturing to Pharmacy Counseling
Special Issue in Plants: Pharmaceutical and Nutraceutical Potential of Polyphenolic Natural Compounds
Special Issue in Molecules: Advances in Synthesis and Biological Activity of Novel Derivatives Based on Five-Membered Heterocyclic Scaffolds and Their Intermediates
Special Issue in International Journal of Molecular Sciences: Techniques and Strategies in Drug Design and Discovery
Special Issue in Plants: Natural Phenolic and Polyphenolic Compounds and Their Impact on Human Health
Special Issue in International Journal of Molecular Sciences: Techniques and Strategies in Drug Design and Discovery, 2nd Edition
Special Issue in Molecules: Advances in Synthesis and Biological Activity of Novel Derivatives Based on Five-Membered Heterocyclic Scaffolds and Their Intermediates—Second Edition
Computer-aided methods play a crucial role in every stage of drug discovery and development, enabling a more efficient and cost-effective approach. The integration of computational methods with experimental approaches has become indispensable in modern drug discovery. At a time when advancements in technology are revolutionizing the pharmaceutical industry, this Special Issue aims to highlight the latest developments in computer-aided drug design and drug discovery. The iterative process of designing, synthesizing, and testing new compounds can lead to the identification of promising drug leads. This Special Issue will provide a platform to showcase cutting-edge research, innovative methodologies, and breakthroughs that leverage computational approaches in pharmaceutical research. It will explore various aspects, including but not limited to:
- Novel computational techniques and algorithms in drug design;
- Artificial intelligence and machine learning in drug discovery;
- Molecular modeling and simulation for drug development;
- Virtual screening and high-throughput screening methods;
- Structure-based drug design and ligand–receptor interactions;
- CADD in pharmacokinetics (ADME prediction);
- Target and off-target identification;
- Repurposing candidate prioritization;
- Optimization strategies for lead compounds;
- Predictive modeling and toxicology assessments;
- Case studies and successful applications of computer-aided drug design.
We invite you to contribute to this Special Issue by submitting your original research, review articles, or perspectives that encapsulate your expertise and insights. Your contribution would provide readers with valuable knowledge and inspire further advancements in the field.
Dr. Dragos Paul Mihai
Prof. Dr. George Mihai Nitulescu
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.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Pharmaceuticals is an international peer-reviewed open access monthly journal published by MDPI.
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.
- molecular modeling
- virtual screening
- molecular docking
- quantitative structure–activity relationship (QSAR)
- pharmacophore modeling
- target identification
- ADME-T prediction
The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.
In-silico design of metabolically stable SafD-binding peptidomimetics for pilus biogenesis suppression in the prevention of Salmonella-induced abdominal infections
Priyanka Samanta 1, * and Sourav Ghorai 2, *
1Department of BioMolecular Sciences, School of Pharmacy, University of Mississippi, University, MS, USA, 38677-1848; ORCID: 0000-0001-9127-5816
263 Lionel Avenue, Apt E, Waltham, MA, USA, 02452; ORCID: 0000-0001-8093-5655
*Correspondence: email@example.com; Tel.: +1-662-915-1853 (P.S.); firstname.lastname@example.org (S.G.)
Clinical isolates of Salmonella enterica contain Saf pili that establish bacterial attachment with the human epithelium which are a common cause of several abdominal complications. Saf pili undergo a chaperone-usher pathway assembly mechanism to generate its host-recognizing functional form, SafDAA. Preventing the biogenesis of the pili by targeting the SafD and SafA subunits polymerization will prevent host recognition. In this study, virtual mutagenesis and protein–peptide interaction studies have led to the design of peptidomimetics that exhibit enhanced binding with SafD. Molecular dynamics simulations and binding free energy calculations identified key pairwise interactions between the designed peptidomimetics and SafD. In silico ADMET machine-learning models were used to predict metabolic hotspots that helped in the design of metabolically stable peptidomimetics. In addition, a library of 200 peptidomimetics that are predicted to bind strongly with SafD is prepared which can serve as an excellent resource for the discovery of novel SafD-binding peptidomimetics. This work provided new insights into the design of novel anti-virulence therapies targeting Salmonella enterica.