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Crystals
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Advances in Computer-Aided Drug Design and Molecular Dynamics Simulations
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Advances in Computer-Aided Drug Design and Molecular Dynamics Simulations

A special issue of Crystals (ISSN 2073-4352). This special issue belongs to the section "Biomolecular Crystals".

Deadline for manuscript submissions: closed (28 February 2025) | Viewed by 2435

Special Issue Editors

Dr. Xueping Hu

E-Mail Website
Guest Editor
Institute of Frontier Chemistry, School of Chemistry and Chemical Engineering, Shandong University, Qingdao 266237, China
Interests: computer-aided drug design; computational chemistry; medicinal chemistry; molecular dynamics simulation; virtual screening; multiscale simulations
Prof. Dr. William J Welsh

E-Mail Website
Guest Editor
Department of Pharmacology, Robert Wood Johnson Medical School, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
Interests: computational chemistry; molecular and polymer modeling; computer-aided drug design (CADD)
Dr. Shaoqing Du

E-Mail Website
Guest Editor Assistant
Department of Medicinal Chemistry, Key Laboratory of Chemical Biology (Ministry of Education), School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, 44 West Culture Road, Jinan 250012, China
Interests: medicinal chemistry

Special Issue Information

Dear Colleagues,

With the rapid advancement of science and technology, computer-aided drug design (CADD) and molecular dynamics (MD) simulations have become increasingly prominent in drug development. These methods provide crucial support for the efficient and accurate prediction of drug–target interactions. To further promote research and application in this field, we are organizing a Special Issue in the Biomolecular Crystals Section of the journal Crystals, titled "Advances in Computer-Aided Drug Design and Molecular Dynamics Simulations".

This Special Issue aims to compile the latest research findings related to CADD and MD simulations, particularly their innovative applications in biomolecular crystal studies. We welcome submissions in the following areas:

Computer-Aided Drug Design: Including structure-based drug design, virtual screening, quantitative structure–activity relationship (QSAR) models, etc.

Molecular Dynamics Simulations: Covering classical MD simulations, enhanced sampling techniques, free energy calculations, and large-scale molecular simulations.

Biomolecular Crystal Studies: Involving the analysis of biomacromolecular crystal structures, crystal growth mechanisms, and the interactions between drugs and biomolecular crystals.

Multiscale Simulations and Integrated Methods: Combining quantum mechanics, molecular mechanics, and coarse-grained models, as well as computational simulations integrated with experimental data.

Case Studies and Applications: Demonstrating the successful applications of CADD and MD simulations in actual drug development, including but not limited to the design and optimization of anticancer, antiviral, and antibacterial drugs.

Dr. Xueping Hu
Prof. Dr. William J Welsh
Guest Editors

Dr. Shaoqing Du 
Guest Editor Assistant

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. Crystals 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 2100 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

  • computer-aided drug design
  • molecular dynamics simulations
  • biomolecular crystals
  • structure-based drug design
  • virtual screening
  • quantitative structure–activity relationship
  • free energy calculations
  • multiscale simulations
  • enhanced sampling techniques
  • protein–ligand interactions

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

Published Papers (3 papers)

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Research

27 pages, 8944 KiB  
Article
Machine Learning-Based Virtual Screening and Molecular Modeling Reveal Potential Natural Inhibitors for Non-Small Cell Lung Cancer
by Zafer Saad Al Shehri and Faez Falah Alshehri
Crystals 2025, 15(5), 383; https://doi.org/10.3390/cryst15050383 - 22 Apr 2025
Viewed by 354
Abstract
Non-Small Cell Lung Cancer (NSCLC) is the most typical kind of lung cancer. Chemotherapy, radiation therapy, and other traditional cancer therapies are ineffective. Advancements in understanding cancer’s molecular causes have led to targeted therapies, such as those addressing NTRK gene fusions in NSCLC. [...] Read more.
Non-Small Cell Lung Cancer (NSCLC) is the most typical kind of lung cancer. Chemotherapy, radiation therapy, and other traditional cancer therapies are ineffective. Advancements in understanding cancer’s molecular causes have led to targeted therapies, such as those addressing NTRK gene fusions in NSCLC. Several machine-learning techniques were used in our work, including k-Nearest Neighbors (kNN), Support Vector Machine (SVM), Random Forest (RF), and Naive Bayes (NB). As a result, the RF model outperformed the other studied machine-learning methods, achieving an astonishing 93.12% accuracy for both training as well as testing datasets, and it was employed to screen 9000 chemicals, resulting in the discovery of 65 putative NTRK potential inhibitors. The active sites of NTRK proteins were then docked with these 65 active chemicals. Our findings show that Gancaonin X, 5-hydroxy-2-(4-methoxyphenyl)-8,8-dimethyl-2,3-dihydropyrano[2,3-h]chromen-4-one, (2S)-7-[[(2R)-3,3-dimethyloxiran-2-yl]methoxy]-5-hydroxy-2-phenyl-2,3-dihydrochromen-4-one, (2S)-5-hydroxy-2-(4-methoxyphenyl)-8,8-dimethyl-2,3-dihydropyrano[2,3-h]chromen-4-one, and methyl 2-(methylamino)-5-[(3S)-1,2,3,9-tetrahydropyrrolo[2,1-b]quinazolin-3-yl]benzoate establish strong interactions inside the binding region of NTRK, as a result of which stable complexes are formed. This study employs 100 ns molecular dynamics simulations to investigate the dynamic behavior of phytochemical-NTRK complexes, revealing stable interactions through RMSD, RMSF, Rg, and SASA analyses. The detailed examination of protein–ligand interactions provides crucial atomic-level insights, enhancing our understanding of potential neurotrophic receptor kinase-targeted therapeutic strategies. This highlights their significant ability as NTRK antagonists, giving novel treatment options for NSCLC therapy. To summarize, the application of machine learning in combination with virtual screening in this study not only can discover new NSCLC therapeutics but also highlight new computer approaches in the field of drug discovery. Full article
(This article belongs to the Special Issue Advances in Computer-Aided Drug Design and Molecular Dynamics Simulations)
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17 pages, 5603 KiB  
Article
Drug Repurposing for the Discovery of Potential Inhibitors Targeting DJ-1 (PARK7) Against Parkinson’s Disease
by Taibah Aldakhil and Ali Altharawi
Crystals 2025, 15(3), 239; https://doi.org/10.3390/cryst15030239 - 28 Feb 2025
Viewed by 581
Abstract
Parkinson’s disease (PD) is a neurodegenerative disease characterized by increased movement dysfunction and cognitive loss. DJ-1 (PARK7) is an antioxidant that protects cells from oxidative stress, a major contributor to cellular damage and neurodegeneration in PD. Mutations in the DJ-1 gene reduce its [...] Read more.
Parkinson’s disease (PD) is a neurodegenerative disease characterized by increased movement dysfunction and cognitive loss. DJ-1 (PARK7) is an antioxidant that protects cells from oxidative stress, a major contributor to cellular damage and neurodegeneration in PD. Mutations in the DJ-1 gene reduce its neuroprotective ability contributing to PD onset and progression. The neuroprotective and antioxidant properties of DJ-1 make it a viable therapeutic target for developing novel PD therapeutics. A drug repurposing approach was applied to identify promising inhibitors for DJ-1. Three drugs—droxicam, pteroylglutamic acid, and niraparib—were identified based on their binding affinities and interactions. Further molecular dynamics simulations revealed that niraparib and pteroylglutamic acid were the most stable among the three complexes. Moreover, the binding strength of the complexes was confirmed by MMPBSA binding free energy analysis, with Niraparib (−13.50 kcal/mol) and pteroylglutamic Acid (−11.41 kcal/mol) as the most promising candidates. These results suggest that pteroylglutamic acid and niraparib may serve as useful DJ-1 inhibitors for PD-associated protein DJ-1. Further experimental validation and in vivo assessments are required to confirm the efficacy and safety of these drugs against PD. Full article
(This article belongs to the Special Issue Advances in Computer-Aided Drug Design and Molecular Dynamics Simulations)
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17 pages, 4196 KiB  
Article
Integrative Machine Learning, Virtual Screening, and Molecular Modeling for BacA-Targeted Anti-Biofilm Drug Discovery Against Staphylococcal Infections
by Ahmad Almatroudi
Crystals 2024, 14(12), 1057; https://doi.org/10.3390/cryst14121057 - 6 Dec 2024
Cited by 1 | Viewed by 1093
Abstract
The rise in antibiotic-resistant Staphylococcal infections necessitates innovative approaches to identify new therapeutic agents. This study investigates the application of machine learning models to identify potential phytochemical inhibitors against BacA, a target related to Staphylococcal infections. Active compounds were retrieved from BindingDB while [...] Read more.
The rise in antibiotic-resistant Staphylococcal infections necessitates innovative approaches to identify new therapeutic agents. This study investigates the application of machine learning models to identify potential phytochemical inhibitors against BacA, a target related to Staphylococcal infections. Active compounds were retrieved from BindingDB while the decoy was generated from DUDE. The RDKit was utilized for feature engineering. Machine learning models such as k-nearest neighbors (KNN), the support vector machine (SVM), random forest (RF), and naive Bayes (NB) were trained on an initial dataset consisting of 226 active chemicals and 2550 inert compounds. Accompanied by an MCC of 0.93 and an accuracy of 96%, the RF performed better. Utilizing the RF model, a library of 9000 phytochemicals was screened, identifying 300 potentially active compounds, of which 192 exhibited drug-like properties and were further analyzed through molecular docking studies. Molecular docking results identified Ergotamine, Withanolide E, and DOPPA as top inhibitors of the BacA protein, accompanied by interaction affinities of −8.8, −8.1, and −7.9 kcal/mol, respectively. Molecular dynamics (MD) was applied for 100 ns to these top hits to evaluate their stability and dynamic behavior. RMSD, RMSF, SASA, and Rg analyses showed that all complexes remained stable throughout the simulation period. Binding energy calculations using MMGBSA analysis revealed that the BacA_Withanolide E complex exhibited the most favorable binding energy profile with significant van der Waals interactions and a substantial reduction in gas-phase energy. It also revealed that van der Waals interactions contributed significantly to the binding stability of Withanolide E, while electrostatic interactions played a secondary role. The integration of machine learning models with molecular docking and MD simulations proved effective in identifying promising phytochemical inhibitors, with Withanolide E emerging as a potent candidate. These findings provide a pathway for developing new antibacterial agents against Staphylococcal infections, pending further experimental validation and optimization. Full article
(This article belongs to the Special Issue Advances in Computer-Aided Drug Design and Molecular Dynamics Simulations)
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