Dear Colleagues,

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
Guest Editors

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• chemoinformatics
• bioinformatics
• molecular modeling
• virtual screening
• molecular docking
• quantitative structure–activity relationship (QSAR)
• pharmacophore modeling
• target identification
• repurposing
• ADME-T prediction

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: psamanta@go.olemiss.edu; Tel.: +1-662-915-1853 (P.S.); sghora92@gmail.com (S.G.)

Abstract

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.