Small Molecule Drug Discovery: Driven by In-Silico Techniques

A special issue of Pharmaceuticals (ISSN 1424-8247). This special issue belongs to the section "Medicinal Chemistry".

Deadline for manuscript submissions: 25 February 2025 | Viewed by 1975

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Dipartimento di Scienze della Salute, Università “Magna Graecia” di Catanzaro, Campus “Salvatore Venuta”, Viale Europa, 88100 Catanzaro, Italy
Interests: docking; nucleic acids; drug design; molecular modeling; molecular dynamics; virtual screening; drug repurposing; natural products
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Special Issue Information

Dear Colleagues,

Drug Discovery is a complex process that involves identifying molecules that can interact with specific biological targets in the body to produce therapeutic effects. The molecular structure of a drug plays a crucial role in determining its efficacy and safety. Small molecules are one type of drugs that are commonly used in medicine. They are typically synthesized chemically and are generally less complex than biologics. Generally, small molecules are designed to interact with specific targets involved in disease processes, such as enzymes or receptors. Often, they have a well-defined structure that allows them to bind to these targets with high specificity. This targeted binding can help minimize side effects by reducing the interaction of the drug with off-target molecules. The ability to design small molecules with specific structural features has revolutionized drug development, allowing for the creation of highly effective and safe drugs for a wide range of diseases. Moreover, the field of small-molecule drug design is a constantly developing area that benefits from advances in computational tools and methodologies. With the help of these tools, scientists can better design and optimize small-molecule drug candidates. In particular, ligand-based methods are critical tools for virtual screening, with pharmacophore modeling, 3D-QSARs, CoMSIA, and CoMFA being the most significant. By combining molecular dynamics and QSAR-computed descriptors, MD-QSAR models offer enhanced predictive power. Structure-based drug design is a highly specific, efficient, and rapid process that focuses on the 3D structure of a target protein and the disease knowledge at the molecular level. Recent advances in drug discovery, including molecular dynamics, free energy perturbation, machine learning, and de novo molecular generation, have revolutionized the field and opened exciting new possibilities for the future. This Special Issue aims to present the latest advances in the discovery, design, synthesis, and pharmacological evaluation of small molecules driven by computational approaches. This Special Issue will accept original research papers and high-quality reviews on the following topics:

  • The design, synthesis, and identification of new small molecules;
  • The discovery of new lead compounds
  • Virtual screening;
  • Rational drug design and lead optimization;
  • The evaluation of molecular mechanisms and biological targets of new, synthesized small molecules.

Dr. Roberta Rocca
Guest Editor

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Keywords

  • drug discovery
  • nutraceuticals
  • virtual screening
  • docking
  • molecular dynamics
  • pharmacophore
  • lead compound

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Published Papers (1 paper)

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Research

13 pages, 2719 KiB  
Article
Structure-Based Identification of Novel Histone Deacetylase 4 (HDAC4) Inhibitors
by Rupesh Agarwal, Pawat Pattarawat, Michael R. Duff, Hwa-Chain Robert Wang, Jerome Baudry and Jeremy C. Smith
Pharmaceuticals 2024, 17(7), 867; https://doi.org/10.3390/ph17070867 - 2 Jul 2024
Viewed by 1511
Abstract
Histone deacetylases (HDACs) are important cancer drug targets. Existing FDA-approved drugs target the catalytic pocket of HDACs, which is conserved across subfamilies (classes) of HDAC. However, engineering specificity is an important goal. Herein, we use molecular modeling approaches to identify and target potential [...] Read more.
Histone deacetylases (HDACs) are important cancer drug targets. Existing FDA-approved drugs target the catalytic pocket of HDACs, which is conserved across subfamilies (classes) of HDAC. However, engineering specificity is an important goal. Herein, we use molecular modeling approaches to identify and target potential novel pockets specific to Class IIA HDAC-HDAC4 at the interface between HDAC4 and the transcriptional corepressor component protein NCoR. These pockets were screened using an ensemble docking approach combined with consensus scoring to identify compounds with a different binding mechanism than the currently known HDAC modulators. Binding was compared in experimental assays between HDAC4 and HDAC3, which belong to a different family of HDACs. HDAC4 was significantly inhibited by compound 88402 but not HDAC3. Two other compounds (67436 and 134199) had IC50 values in the low micromolar range for both HDACs, which is comparable to the known inhibitor of HDAC4, SAHA (Vorinostat). However, both of these compounds were significantly weaker inhibitors of HDAC3 than SAHA and thus more selective, albeit to a limited extent. Five compounds exhibited activity on human breast carcinoma and/or urothelial carcinoma cell lines. The present result suggests potential mechanistic and chemical approaches for developing selective HDAC4 modulators. Full article
(This article belongs to the Special Issue Small Molecule Drug Discovery: Driven by In-Silico Techniques)
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