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Computational Drug Discovery and Design
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Computational Drug Discovery and Design

A special issue of Molecules (ISSN 1420-3049). This special issue belongs to the section "Chemical Biology".

Deadline for manuscript submissions: closed (1 May 2022) | Viewed by 10674

Special Issue Editors

Dr. Jacopo Sgrignani

E-Mail Website
Guest Editor
Istituto di Ricerca in Biomedicina (IRB), Università della Svizzera Italiana (USI), Via V. Vela 6, CH-6500 Bellinzona, Switzerland
Interests: molecular dynamics; QM/MM; computational drug design; microscale thermophoresis; ligand binding
Special Issues, Collections and Topics in MDPI journals
Dr. Antonella Paladino

E-Mail Website
Guest Editor
Biogem, Istituto di Ricerche Genetiche 'G. Salvatore' via Camporeale, 83031 Ariano Irpino, AV, Italy
Interests: computational biology and chemistry; structural modeling; molecular dynamics; docking; ppi; molecular interactions; protein structure/function
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The use of computational methods is still routine in drug discovery and optimization.

Recent advances in protein structures availability, from both novel experimental or in silico techniques, strongly boost the entire drug discovery process.

Currently, computer-aided drug design gains advantages from the application of artificial intelligence (AI) techniques at several steps of the drug development pipeline.

To address the central challenge of evaluating and predicting the biological effects of chemicals, computational approaches can be employed at differential levels (target/lead) across drug development, representing a key resource for medicinal scientists to enhance/fasten the optimization of small molecules.

For this Special Issue, we invite the submission of original contributions, short communications, or review articles that describe the application of simulations as well as artificial intelligence techniques for the design and optimization of therapeutic molecules such as small molecules, peptides and antibodies. We encourage the submission of purely in silico studies, as well as computational studies with experimental validations.

Dr. Jacopo Sgrignani
Dr. Antonella Paladino
Guest Editors

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. Molecules is an international peer-reviewed open access semimonthly 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 2700 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.

Keywords

  • computational drug design
  • docking
  • virtual screening
  • artificial intelligence
  • structure-based drug design
  • ligand-based drug design

Published Papers (4 papers)

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Research

29 pages, 9525 KiB  
Article
QSAR Evaluations to Unravel the Structural Features in Lysine-Specific Histone Demethylase 1A Inhibitors for Novel Anticancer Lead Development Supported by Molecular Docking, MD Simulation and MMGBSA
by Rahul D. Jawarkar, Ravindra L. Bakal, Nobendu Mukherjee, Arabinda Ghosh, Magdi E. A. Zaki, Sami A. AL-Hussain, Aamal A. Al-Mutairi, Abdul Samad, Ajaykumar Gandhi and Vijay H. Masand
Molecules 2022, 27(15), 4758; https://0-doi-org.brum.beds.ac.uk/10.3390/molecules27154758 - 25 Jul 2022
Cited by 3 | Viewed by 1884
Abstract
Using 84 structurally diverse and experimentally validated LSD1/KDM1A inhibitors, quantitative structure–activity relationship (QSAR) models were built by OECD requirements. In the QSAR analysis, certainly significant and understated pharmacophoric features were identified as critical for LSD1 inhibition, such as a ring Carbon atom with [...] Read more.
Using 84 structurally diverse and experimentally validated LSD1/KDM1A inhibitors, quantitative structure–activity relationship (QSAR) models were built by OECD requirements. In the QSAR analysis, certainly significant and understated pharmacophoric features were identified as critical for LSD1 inhibition, such as a ring Carbon atom with exactly six bonds from a Nitrogen atom, partial charges of lipophilic atoms within eight bonds from a ring Sulphur atom, a non-ring Oxygen atom exactly nine bonds from the amide Nitrogen, etc. The genetic algorithm–multi-linear regression (GA-MLR) and double cross-validation criteria were used to create robust QSAR models with high predictability. In this study, two QSAR models were developed, with fitting parameters like R2 = 0.83–0.81, F = 61.22–67.96, internal validation parameters such as Q2LOO = 0.79–0.77, Q2LMO = 0.78–0.76, CCCcv = 0.89–0.88, and external validation parameters such as, R2ext = 0.82 and CCCex = 0.90. In terms of mechanistic interpretation and statistical analysis, both QSAR models are well-balanced. Furthermore, utilizing the pharmacophoric features revealed by QSAR modelling, molecular docking experiments corroborated with the most active compound’s binding to the LSD1 receptor. The docking results are then refined using Molecular dynamic simulation and MMGBSA analysis. As a consequence, the findings of the study can be used to produce LSD1/KDM1A inhibitors as anticancer leads. Full article
(This article belongs to the Special Issue Computational Drug Discovery and Design)
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40 pages, 20566 KiB  
Article
Integration of Ligand-Based and Structure-Based Methods for the Design of Small-Molecule TLR7 Antagonists
by Sourav Pal, Uddipta Ghosh Dastidar, Trisha Ghosh, Dipyaman Ganguly and Arindam Talukdar
Molecules 2022, 27(13), 4026; https://0-doi-org.brum.beds.ac.uk/10.3390/molecules27134026 - 23 Jun 2022
Cited by 4 | Viewed by 2996
Abstract
Toll-like receptor 7 (TLR7) is activated in response to the binding of single-stranded RNA. Its over-activation has been implicated in several autoimmune disorders, and thus, it is an established therapeutic target in such circumstances. TLR7 small-molecule antagonists are not yet available for therapeutic [...] Read more.
Toll-like receptor 7 (TLR7) is activated in response to the binding of single-stranded RNA. Its over-activation has been implicated in several autoimmune disorders, and thus, it is an established therapeutic target in such circumstances. TLR7 small-molecule antagonists are not yet available for therapeutic use. We conducted a ligand-based drug design of new TLR7 antagonists through a concerted effort encompassing 2D-QSAR, 3D-QSAR, and pharmacophore modelling of 54 reported TLR7 antagonists. The developed 2D-QSAR model depicted an excellent correlation coefficient (R2training: 0.86 and R2test: 0.78) between the experimental and estimated activities. The ligand-based drug design approach utilizing the 3D-QSAR model (R2training: 0.95 and R2test: 0.84) demonstrated a significant contribution of electrostatic potential and steric fields towards the TLR7 antagonism. This consolidated approach, along with a pharmacophore model with high correlation (Rtraining: 0.94 and Rtest: 0.92), was used to design quinazoline-core-based hTLR7 antagonists. Subsequently, the newly designed molecules were subjected to molecular docking onto the previously proposed binding model and a molecular dynamics study for a better understanding of their binding pattern. The toxicity profiles and drug-likeness characteristics of the designed compounds were evaluated with in silico ADMET predictions. This ligand-based study contributes towards a better understanding of lead optimization and the future development of potent TLR7 antagonists. Full article
(This article belongs to the Special Issue Computational Drug Discovery and Design)
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23 pages, 8457 KiB  
Article
Computational Prediction and Experimental Validation of the Unique Molecular Mode of Action of Scoulerine
by Mahshad Moshari, Qian Wang, Marek Michalak, Mariusz Klobukowski and Jack Adam Tuszynski
Molecules 2022, 27(13), 3991; https://0-doi-org.brum.beds.ac.uk/10.3390/molecules27133991 - 21 Jun 2022
Cited by 2 | Viewed by 1335
Abstract
Scoulerine is a natural compound that is known to bind to tubulin and has anti-mitotic properties demonstrated in various cancer cells. Its molecular mode of action has not been precisely known. In this work, we perform computational prediction and experimental validation of the [...] Read more.
Scoulerine is a natural compound that is known to bind to tubulin and has anti-mitotic properties demonstrated in various cancer cells. Its molecular mode of action has not been precisely known. In this work, we perform computational prediction and experimental validation of the mode of action of scoulerine. Based on the existing data in the Protein Data Bank (PDB) and using homology modeling, we create human tubulin structures corresponding to both free tubulin dimers and tubulin in a microtubule. We then perform docking of the optimized structure of scoulerine and find the highest affinity binding sites located in both the free tubulin and in a microtubule. We conclude that binding in the vicinity of the colchicine binding site and near the laulimalide binding site are the most likely locations for scoulerine interacting with tubulin. Thermophoresis assays using scoulerine and tubulin in both free and polymerized form confirm these computational predictions. We conclude that scoulerine exhibits a unique property of a dual mode of action with both microtubule stabilization and tubulin polymerization inhibition, both of which have similar affinity values. Full article
(This article belongs to the Special Issue Computational Drug Discovery and Design)
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25 pages, 5872 KiB  
Article
Discovery of Bispecific Lead Compounds from Azadirachta indica against ZIKA NS2B-NS3 Protease and NS5 RNA Dependent RNA Polymerase Using Molecular Simulations
by Sanjay Kumar, Sherif A. El-Kafrawy, Shiv Bharadwaj, S. S. Maitra, Thamir A. Alandijany, Arwa A. Faizo, Aiah M. Khateb, Vivek Dhar Dwivedi and Esam I. Azhar
Molecules 2022, 27(8), 2562; https://0-doi-org.brum.beds.ac.uk/10.3390/molecules27082562 - 15 Apr 2022
Cited by 12 | Viewed by 3142
Abstract
Zika virus (ZIKV) has been characterized as one of many potential pathogens and placed under future epidemic outbreaks by the WHO. However, a lack of potential therapeutics can result in an uncontrolled pandemic as with other human pandemic viruses. Therefore, prioritized effective therapeutics [...] Read more.
Zika virus (ZIKV) has been characterized as one of many potential pathogens and placed under future epidemic outbreaks by the WHO. However, a lack of potential therapeutics can result in an uncontrolled pandemic as with other human pandemic viruses. Therefore, prioritized effective therapeutics development has been recommended against ZIKV. In this context, the present study adopted a strategy to explore the lead compounds from Azadirachta indica against ZIKV via concurrent inhibition of the NS2B-NS3 protease (ZIKVpro) and NS5 RNA dependent RNA polymerase (ZIKVRdRp) proteins using molecular simulations. Initially, structure-based virtual screening of 44 bioflavonoids reported in Azadirachta indica against the crystal structures of targeted ZIKV proteins resulted in the identification of the top four common bioflavonoids, viz. Rutin, Nicotiflorin, Isoquercitrin, and Hyperoside. These compounds showed substantial docking energy (−7.9 to −11.01 kcal/mol) and intermolecular interactions with essential residues of ZIKVpro (B:His51, B:Asp75, and B:Ser135) and ZIKVRdRp (Asp540, Ile799, and Asp665) by comparison to the reference compounds, O7N inhibitor (ZIKVpro) and Sofosbuvir inhibitor (ZIKVRdRp). Besides, long interval molecular dynamics simulation (500 ns) on the selected docked poses reveals stability of the respective docked poses contributed by intermolecular hydrogen bonds and hydrophobic interactions. The predicted complex stability was further supported by calculated end-point binding free energy using molecular mechanics generalized born surface area (MM/GBSA) method. Consequently, the identified common bioflavonoids are recommended as promising therapeutic inhibitors of ZIKVpro and ZIKVRdRp against ZIKV for further experimental assessment. Full article
(This article belongs to the Special Issue Computational Drug Discovery and Design)
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