Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
8.1
4.9
Journals
IJMS
Special Issues
Drug Design and Virtual Screening 2.0
ijms-logo
Submit to IJMS Review for IJMS Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Drug Design and Virtual Screening 2.0

A special issue of International Journal of Molecular Sciences (ISSN 1422-0067). This special issue belongs to the section "Molecular Informatics".

Deadline for manuscript submissions: closed (30 November 2022) | Viewed by 19281

Special Issue Editor

Dr. Jiazhong Li

E-Mail Website
Guest Editor
School of Pharmacy, Lanzhou University, Lanzhou, China
Interests: drug design; molecular simulations; molecular modeling; network pharmacology
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Since the end of the 19th century, when aspirin was synthesized by modifying the structure of a natural product, scientists have been engaged in drug design and discovery. The emergence of quantitative structure–activity relationship (QSAR) methods in the 1960s represented a new era of rational drug design. With the development of computational methods and large databases, various drug-design and in silico screening technologies have been devised. Researchers can identify the molecular structures essential for pharmacological activity more readily and screen large libraries of compounds with a high degree of specificity. From genes to proteins to drugs, computer-aided drug-design methodologies play an increasingly important role in modern drug discovery.

This Special Issue of the International Journal of Molecular Sciences focuses on drug design, in silico screening, and pharmacogenomic approaches. All articles relating to drug design and discovery are welcomed, including but not limited to the following aspects: CADD methodology, virtual screening of compound libraries by computational methods, structural optimization of lead compounds, modelling studies on the interaction between small molecules and macromolecules, and pharmacogenomics/transcriptomics. Cell-based or animal experiments are also encouraged involving any disease model, but this is not essential.

Dr. Jia-Zhong Li
Guest Editor

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. International Journal of Molecular Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. There is an Article Processing Charge (APC) for publication in this open access journal. For details about the APC please see here. 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.

Keywords

  • CADD methodology
  • QSAR
  • pharmacophore modelling
  • molecular docking
  • virtual screening
  • lead optimization
  • de novo drug design
  • molecular dynamics
  • network pharmacology
  • pharmacogenomics

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue polices can be found here.

Published Papers (7 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

21 pages, 8265 KiB  
Article
Identification of CBPA as a New Inhibitor of PD-1/PD-L1 Interaction
by Fengling Wang, Wenling Ye, Yongxing He, Haiyang Zhong, Yongchang Zhu, Jianting Han, Xiaoqing Gong, Yanan Tian, Yuwei Wang, Shuang Wang, Shaoping Ji, Huanxiang Liu and Xiaojun Yao
Int. J. Mol. Sci. 2023, 24(4), 3971; https://doi.org/10.3390/ijms24043971 - 16 Feb 2023
Cited by 4 | Viewed by 2724
Abstract
Targeting of the PD-1/PD-L1 immunologic checkpoint is believed to have provided a real breakthrough in the field of cancer therapy in recent years. Due to the intrinsic limitations of antibodies, the discovery of small-molecule inhibitors blocking PD-1/PD-L1 interaction has gradually opened valuable new [...] Read more.
Targeting of the PD-1/PD-L1 immunologic checkpoint is believed to have provided a real breakthrough in the field of cancer therapy in recent years. Due to the intrinsic limitations of antibodies, the discovery of small-molecule inhibitors blocking PD-1/PD-L1 interaction has gradually opened valuable new avenues in the past decades. In an effort to discover new PD-L1 small molecular inhibitors, we carried out a structure-based virtual screening strategy to rapidly identify the candidate compounds. Ultimately, CBPA was identified as a PD-L1 inhibitor with a KD value at the micromolar level. It exhibited effective PD-1/PD-L1 blocking activity and T-cell-reinvigoration potency in cell-based assays. CBPA could dose-dependently elevate secretion levels of IFN-γ and TNF-α in primary CD4+ T cells in vitro. Notably, CBPA exhibited significant in vivo antitumor efficacy in two different mouse tumor models (a MC38 colon adenocarcinoma model and a melanoma B16F10 tumor model) without the induction of observable liver or renal toxicity. Moreover, analyses of the CBPA-treated mice further showed remarkably increased levels of tumor-infiltrating CD4+ and CD8+ T cells and cytokine secretion in the tumor microenvironment. A molecular docking study suggested that CBPA embedded relatively well into the hydrophobic cleft formed by dimeric PD-L1, occluding the PD-1 interaction surface of PD-L1. This study suggests that CBPA could work as a hit compound for the further design of potent inhibitors targeting the PD-1/PD-L1 pathway in cancer immunotherapy. Full article
(This article belongs to the Special Issue Drug Design and Virtual Screening 2.0)
►▼ Show Figures

Figure 1

20 pages, 11857 KiB  
Article
PETrans: De Novo Drug Design with Protein-Specific Encoding Based on Transfer Learning
by Xun Wang, Changnan Gao, Peifu Han, Xue Li, Wenqi Chen, Alfonso Rodríguez Patón, Shuang Wang and Pan Zheng
Int. J. Mol. Sci. 2023, 24(2), 1146; https://doi.org/10.3390/ijms24021146 - 6 Jan 2023
Cited by 14 | Viewed by 3577
Abstract
Recent years have seen tremendous success in the design of novel drug molecules through deep generative models. Nevertheless, existing methods only generate drug-like molecules, which require additional structural optimization to be developed into actual drugs. In this study, a deep learning method for [...] Read more.
Recent years have seen tremendous success in the design of novel drug molecules through deep generative models. Nevertheless, existing methods only generate drug-like molecules, which require additional structural optimization to be developed into actual drugs. In this study, a deep learning method for generating target-specific ligands was proposed. This method is useful when the dataset for target-specific ligands is limited. Deep learning methods can extract and learn features (representations) in a data-driven way with little or no human participation. Generative pretraining (GPT) was used to extract the contextual features of the molecule. Three different protein-encoding methods were used to extract the physicochemical properties and amino acid information of the target protein. Protein-encoding and molecular sequence information are combined to guide molecule generation. Transfer learning was used to fine-tune the pretrained model to generate molecules with better binding ability to the target protein. The model was validated using three different targets. The docking results show that our model is capable of generating new molecules with higher docking scores for the target proteins. Full article
(This article belongs to the Special Issue Drug Design and Virtual Screening 2.0)
►▼ Show Figures

Figure 1

19 pages, 5616 KiB  
Article
Discovery of the Cryptic Sites of SARS-CoV-2 Papain-like Protease and Analysis of Its Druggability
by Yue Qiu, Qing Liu, Gao Tu and Xiao-Jun Yao
Int. J. Mol. Sci. 2022, 23(19), 11265; https://doi.org/10.3390/ijms231911265 - 24 Sep 2022
Cited by 2 | Viewed by 2033
Abstract
In late 2019, a new coronavirus (CoV) caused the outbreak of a deadly respiratory disease, resulting in the COVID-19 pandemic. In view of the ongoing pandemic, there is an immediate need to find drugs to treat patients. SARS-CoV-2 papain-like cysteine protease (PLpro) not [...] Read more.
In late 2019, a new coronavirus (CoV) caused the outbreak of a deadly respiratory disease, resulting in the COVID-19 pandemic. In view of the ongoing pandemic, there is an immediate need to find drugs to treat patients. SARS-CoV-2 papain-like cysteine protease (PLpro) not only plays an important role in the pathogenesis of the virus but is also a target protein for the development of inhibitor drugs. Therefore, to develop targeted inhibitors, it is necessary to analyse and verify PLpro sites and explore whether there are other cryptic binding pockets with better activity. In this study, first, we detected the site of the whole PLpro protein by sitemap of Schrödinger (version 2018), the cavity of LigBuilder V3, and DeepSite, and roughly judged the possible activated binding site area. Then, we used the mixed solvent dynamics simulation (MixMD) of probe molecules to induce conformational changes in the protein to find the possible cryptic active sites. Finally, the TRAPP method was used to predict the druggability of cryptic pockets and analyse the changes in the physicochemical properties of residues around these sites. This work will help promote the research of SARS-CoV-2 PLpro inhibitors. Full article
(This article belongs to the Special Issue Drug Design and Virtual Screening 2.0)
►▼ Show Figures

Figure 1

20 pages, 4885 KiB  
Article
Fluorine Atoms on C6H5-Corrole Affect the Interaction with Mpro and PLpro Proteases of SARS-CoV-2: Molecular Docking and 2D-QSAR Approaches
by Otávio Augusto Chaves, Cláudio Eduardo Rodrigues-Santos, Áurea Echevarria, Carolina Q. Sacramento, Natalia Fintelman-Rodrigues, Jairo R. Temerozo, Hugo Caire Castro-Faria-Neto and Thiago Moreno Lopes e Souza
Int. J. Mol. Sci. 2022, 23(18), 10936; https://doi.org/10.3390/ijms231810936 - 19 Sep 2022
Cited by 3 | Viewed by 1957
Abstract
The chymotrypsin-like cysteine protease (3CLpro, also known as main protease—Mpro) and papain-like protease (PLpro) of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) have been used as the main targets for screening potential synthetic inhibitors for posterior in [...] Read more.
The chymotrypsin-like cysteine protease (3CLpro, also known as main protease—Mpro) and papain-like protease (PLpro) of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) have been used as the main targets for screening potential synthetic inhibitors for posterior in vitro evaluation of the most promising compounds. In this sense, the present work reports for the first time the evaluation of the interaction between Mpro/PLpro with a series of 17 porphyrin analogues-corrole (C1), meso-aryl-corrole (C2), and 15 fluorinated-meso-aryl-corrole derivatives (C3–C17) via molecular docking calculations. The impact of fluorine atoms on meso-aryl-corrole structure was also evaluated in terms of binding affinity and physical-chemical properties by two-dimensional quantitative structure–activity relationship (2D-QSAR). The presence of phenyl moieties increased the binding capacity of corrole for both proteases and depending on the position of fluorine atoms might impact positively or negatively the binding capacity. For Mpro the para-fluorine atoms might decrease drastically the binding capacity, while for PLpro there was a certain increase in the binding affinity of fluorinated-corroles with the increase of fluorine atoms into meso-aryl-corrole structure mainly from tri-fluorinated insertions. The 2D-QSAR models indicated two separated regions of higher and lower affinity for Mpro:C1–C17 based on dual electronic parameters (σI and σR), as well as one model was obtained with a correlation between the docking score value of Mpro:C2–C17 and the corresponding 13C nuclear magnetic resonance (NMR) chemical shifts of the sp2 carbon atoms (δC-1 and δC-2) of C2–C17. Overall, the fluorinated-meso-aryl-corrole derivatives showed favorable in silico parameters as potential synthetic compounds for future in vitro assays on the inhibition of SARS-CoV-2 replication. Full article
(This article belongs to the Special Issue Drug Design and Virtual Screening 2.0)
►▼ Show Figures

Figure 1

16 pages, 2161 KiB  
Article
Machine Learning-Based Virtual Screening for the Identification of Cdk5 Inhibitors
by Miriana Di Stefano, Salvatore Galati, Gabriella Ortore, Isabella Caligiuri, Flavio Rizzolio, Costanza Ceni, Simone Bertini, Giulia Bononi, Carlotta Granchi, Marco Macchia, Giulio Poli and Tiziano Tuccinardi
Int. J. Mol. Sci. 2022, 23(18), 10653; https://doi.org/10.3390/ijms231810653 - 13 Sep 2022
Cited by 14 | Viewed by 3849
Abstract
Cyclin-dependent kinase 5 (Cdk5) is an atypical proline-directed serine/threonine protein kinase well-characterized for its role in the central nervous system rather than in the cell cycle. Indeed, its dysregulation has been strongly implicated in the progression of synaptic dysfunction and neurodegenerative diseases, such [...] Read more.
Cyclin-dependent kinase 5 (Cdk5) is an atypical proline-directed serine/threonine protein kinase well-characterized for its role in the central nervous system rather than in the cell cycle. Indeed, its dysregulation has been strongly implicated in the progression of synaptic dysfunction and neurodegenerative diseases, such as Alzheimer’s disease (AD) and Parkinson’s disease (PD), and also in the development and progression of a variety of cancers. For this reason, Cdk5 is considered as a promising target for drug design, and the discovery of novel small-molecule Cdk5 inhibitors is of great interest in the medicinal chemistry field. In this context, we employed a machine learning-based virtual screening protocol with subsequent molecular docking, molecular dynamics simulations and binding free energy evaluations. Our virtual screening studies resulted in the identification of two novel Cdk5 inhibitors, highlighting an experimental hit rate of 50% and thus validating the reliability of the in silico workflow. Both identified ligands, compounds CPD1 and CPD4, showed a promising enzyme inhibitory activity and CPD1 also demonstrated a remarkable antiproliferative activity in ovarian and colon cancer cells. These ligands represent a valuable starting point for structure-based hit-optimization studies aimed at identifying new potent Cdk5 inhibitors. Full article
(This article belongs to the Special Issue Drug Design and Virtual Screening 2.0)
►▼ Show Figures

Graphical abstract

14 pages, 5254 KiB  
Article
Binding Thermodynamics and Dissociation Kinetics Analysis Uncover the Key Structural Motifs of Phenoxyphenol Derivatives as the Direct InhA Inhibitors and the Hotspot Residues of InhA
by Qianqian Zhang, Jianting Han, Yongchang Zhu, Shuoyan Tan and Huanxiang Liu
Int. J. Mol. Sci. 2022, 23(17), 10102; https://doi.org/10.3390/ijms231710102 - 3 Sep 2022
Cited by 6 | Viewed by 1891
Abstract
Given the current epidemic of multidrug-resistant tuberculosis, there is an urgent need to develop new drugs to combat drug-resistant tuberculosis. Direct inhibitors of the InhA target do not require activation and thus can overcome drug resistance caused by mutations in drug-activating enzymes. In [...] Read more.
Given the current epidemic of multidrug-resistant tuberculosis, there is an urgent need to develop new drugs to combat drug-resistant tuberculosis. Direct inhibitors of the InhA target do not require activation and thus can overcome drug resistance caused by mutations in drug-activating enzymes. In this work, the binding thermodynamic and kinetic information of InhA to its direct inhibitors, phenoxyphenol derivatives, were explored through multiple computer-aided drug design (CADD) strategies. The results show that the van der Waals interactions were the main driving force for protein–ligand binding, among which hydrophobic residues such as Tyr158, Phe149, Met199 and Ile202 have high energy contribution. The AHRR pharmacophore model generated by multiple ligands demonstrated that phenoxyphenol derivatives inhibitors can form pi–pi stacking and hydrophobic interactions with InhA target. In addition, the order of residence time predicted by random acceleration molecular dynamics was consistent with the experimental values. The intermediate states of these inhibitors could form hydrogen bonds and van der Waals interactions with surrounding residues during dissociation. Overall, the binding and dissociation mechanisms at the atomic level obtained in this work can provide important theoretical guidance for the development of InhA direct inhibitors with higher activity and proper residence time. Full article
(This article belongs to the Special Issue Drug Design and Virtual Screening 2.0)
►▼ Show Figures

Graphical abstract

12 pages, 2892 KiB  
Article
Activity Screening of Fatty Acid Mimetic Drugs Identified Nuclear Receptor Agonists
by Moritz Helmstädter, Simone Schierle, Laura Isigkeit, Ewgenij Proschak, Julian Aurelio Marschner and Daniel Merk
Int. J. Mol. Sci. 2022, 23(17), 10070; https://doi.org/10.3390/ijms231710070 - 3 Sep 2022
Cited by 3 | Viewed by 2004
Abstract
Fatty acid mimetics (FAM) are bioactive molecules acting through the binding sites of endogenous fatty acid metabolites on enzymes, transporters, and receptors. Due to the special characteristics of these binding sites, FAMs share common chemical features. Pharmacological modulation of fatty acid signaling has [...] Read more.
Fatty acid mimetics (FAM) are bioactive molecules acting through the binding sites of endogenous fatty acid metabolites on enzymes, transporters, and receptors. Due to the special characteristics of these binding sites, FAMs share common chemical features. Pharmacological modulation of fatty acid signaling has therapeutic potential in multiple pathologies, and several FAMs have been developed as drugs. We aimed to elucidate the promiscuity of FAM drugs on lipid-activated transcription factors and tested 64 approved compounds for activation of RAR, PPARs, VDR, LXR, FXR, and RXR. The activity screening revealed nuclear receptor agonism of several FAM drugs and considerable promiscuity of NSAIDs, while other compound classes evolved as selective. These screening results were not anticipated by three well-established target prediction tools, suggesting that FAMs are underrepresented in bioactivity data for model development. The screening dataset may therefore valuably contribute to such tools. Oxaprozin (RXR), tianeptine (PPARδ), mycophenolic acid (RAR), and bortezomib (RAR) exhibited selective agonism on one nuclear receptor and emerged as attractive leads for the selective optimization of side activities. Additionally, their nuclear receptor agonism may contribute relevant and valuable polypharmacology. Full article
(This article belongs to the Special Issue Drug Design and Virtual Screening 2.0)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-7
Back to TopTop