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Application of 2D and 3D-QSAR Models in Drug Design

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

Deadline for manuscript submissions: closed (22 April 2025) | Viewed by 12925

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Dr. Jaime Mella Raipan

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Instituto de Química y Bioquímica, Universidad de Valparaíso, Valparaíso, Chile
Interests: 2D-QSAR; 3D-QSAR; Hansch analysis; Free–Wilson; structure–activity relationships; CoMFA; CoMSIA; drug design; molecular docking; molecular dynamics; medicinal chemistry; heterocycles; benzimidazole; organic synthesis; cancer
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Dear Colleagues,

Quantitative structure–activity relationship (QSAR) studies enable the correlation of the experimental biological activity of a series of molecules with their physicochemical properties. This allows for a) understanding the structure–activity relationship, and b) predicting the biological activity of new molecules before their synthesis. Therefore, QSAR studies make efficient the design and synthesis of new drugs. Another great advantage of QSAR studies is their versatility, as it is not necessary to know the structure of the target where the molecules act in order to formulate the equation.

We invite the scientific community to publish their work on the design and synthesis of new bioactive molecules in this Special Issue. All works that have direct or indirect applications of QSAR are welcome, such as:

- Formulation of retrospective QSAR studies that discover the pharmacophore of a family of compounds.
- QSAR studies that enable the systematization of the structure–activity relationship of new compounds or databases obtained from the literature.
- Studies that propose the creation of classical Hansch equations, Free–Wilson equations, combined equations, and 3D-QSAR studies such as CoMFA and CoMSIA.
- Works that propose new methodologies or the use of new descriptors.
- Works that employ the integrated use of QSAR techniques, docking, and molecular dynamics for the design of new drugs.
- General design of new bioactive molecules using QSAR equations and prediction of the biological activity value of the compounds, plus a retrosynthesis approach to assess their synthetic feasibility.

Dr. Jaime Mella Raipan
Guest Editor

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Keywords

2D-QSAR
3D-QSAR
Hansch analysis
Free–Wilson
structure-activity relationships
CoMFA
CoMSIA
drug design
molecular docking
molecular dynamics.

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Published Papers (9 papers)

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Research

36 pages, 8994 KiB

Open AccessArticle

Identification of Microbial-Based Natural Products as Potential CYP51 Inhibitors for Eumycetoma Treatment: Insights from Molecular Docking, MM-GBSA Calculations, ADMET Analysis, and Molecular Dynamics Simulations

by Tilal Elsaman, Mohamed Khalid Alhaj Awadalla, Malik Suliman Mohamed, Eyman Mohamed Eltayib and Magdi Awadalla Mohamed

Pharmaceuticals 2025, 18(4), 598; https://doi.org/10.3390/ph18040598 - 20 Apr 2025

Viewed by 330

Abstract

Background/Objectives: Eumycetoma, caused by Madurella mycetomatis, is a chronic fungal infection with limited treatment options and increasing drug resistance. CYP51, a key enzyme in ergosterol biosynthesis, is a well-established target for azole antifungals. However, existing azole drugs demonstrate limited efficacy in treating eumycetoma. Microbial-based natural products, with their structural diversity and bioactivity, offer a promising source for novel CYP51 inhibitors. This study aimed to identify potential Madurella mycetomatis CYP51 inhibitors from microbial natural products using molecular docking, MM-GBSA calculations, ADMET analysis, and molecular dynamics (MD) simulations. Methods: Virtual screening was conducted on a library of microbial-based natural products using an in-house homology model of Madurella mycetomatis CYP51, with itraconazole as the reference drug. The top compounds from initial docking were refined through Standard and Extra Precision docking. MM-GBSA calculations assessed binding affinities, and ADMET analysis evaluated drug-like properties. Compounds with favorable properties underwent MD simulations. Results: The computational investigations identified 34 compounds with better docking scores and binding affinity than itraconazole. Of these, 9 compounds interacted with the heme group and key residues in the active site of Madurella mycetomatis CYP51. In silico pharmacokinetic profiling identified 3 compounds as promising candidates, and MD simulations confirmed their potential as CYP51 inhibitors. Conclusions: The study highlights microbial-derived natural products, particularly monacyclinone G, H, and I, as promising candidates for Madurella mycetomatis CYP51 inhibition, with the potential for treating eumycetoma, requiring further experimental validation. Full article

(This article belongs to the Special Issue Application of 2D and 3D-QSAR Models in Drug Design)

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36 pages, 9116 KiB

Open AccessArticle

Computational Investigation of Montelukast and Its Structural Derivatives for Binding Affinity to Dopaminergic and Serotonergic Receptors: Insights from a Comprehensive Molecular Simulation

by Nasser Alotaiq and Doni Dermawan

Pharmaceuticals 2025, 18(4), 559; https://doi.org/10.3390/ph18040559 - 10 Apr 2025

Viewed by 462

Abstract

Background/Objectives: Montelukast (MLK), a leukotriene receptor antagonist, has been associated with neuropsychiatric side effects. This study aimed to rationally modify MLK’s structure to reduce these risks by optimizing its interactions with dopamine D2 (DRD2) and serotonin 5-HT1A receptors using computational molecular simulation techniques. Methods: A library of MLK derivatives was designed and screened using structural similarity analysis, molecular docking, molecular dynamics (MD) simulations, MM/PBSA binding free energy calculations, and ADME-Tox predictions. Structural similarity analysis, based on Tanimoto coefficient fingerprinting, compared MLK derivatives to known neuropsychiatric drugs. Docking was performed to assess initial receptor binding, followed by 100 ns MD simulations to evaluate binding stability. MM/PBSA calculations quantified binding affinities, while ADME-Tox profiling predicted pharmacokinetic and toxicity risks. Results: Several MLK derivatives showed enhanced DRD2 and 5-HT1A binding. MLK_MOD-42 and MLK_MOD-43 emerged as the most promising candidates, exhibiting MM/PBSA binding free energies of −31.92 ± 2.54 kcal/mol and −27.37 ± 2.22 kcal/mol for DRD2 and −30.22 ± 2.29 kcal/mol and −28.19 ± 2.14 kcal/mol for 5-HT1A, respectively. Structural similarity analysis confirmed that these derivatives share key pharmacophoric features with atypical antipsychotics and anxiolytics. However, off-target interactions were not assessed, which may influence their overall safety profile. ADME-Tox analysis predicted improved oral bioavailability and lower neurotoxicity risks. Conclusions: MLK_MOD-42 and MLK_MOD-43 exhibit optimized receptor interactions and enhanced pharmacokinetics, suggesting potential neuropsychiatric applications. However, their safety and efficacy remain to be validated through in vitro and in vivo studies. Until such validation is performed, these derivatives should be considered as promising candidates with optimized receptor binding rather than confirmed safer alternatives. Full article

(This article belongs to the Special Issue Application of 2D and 3D-QSAR Models in Drug Design)

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13 pages, 1718 KiB

Open AccessArticle

Py-CoMSIA: An Open-Source Implementation of Comparative Molecular Similarity Indices Analysis in Python

by Christopher L. Haga, Crystal N. Le, Xue D. Yang and Donald G. Phinney

Pharmaceuticals 2025, 18(3), 440; https://doi.org/10.3390/ph18030440 - 20 Mar 2025

Viewed by 520

Abstract

Background/Objectives: The progression of three-dimensional (3D) quantitative structure–activity relationship (QSAR) methodologies has significantly contributed to the advancement of medicinal chemistry and pharmaceutical discovery. Comparative Molecular Similarity Indices Analysis (CoMSIA) is a widely used 3D-QSAR technique. However, its reliance on discontinued proprietary software creates accessibility challenges. This work aims to develop an open-source Python library to address these limitations and broaden access to grid-based 3D-QSAR methods. Methods: Py-CoMSIA was developed in Python using RDKit and NumPy for calculations and PyVista for visualizations. Results: Py-CoMSIA provides a functional open-source alternative to proprietary CoMSIA software. It successfully implements the core CoMSIA algorithm and generates comparable similarity indices, as demonstrated by testing several benchmarking datasets including the original CoMSIA steroid dataset. Conclusions: The Py-CoMSIA library addresses the accessibility issues associated with proprietary 3D-QSAR software by providing an open-source Python implementation of CoMSIA. This tool broadens access to complex grid-based 3D-QSAR methodologies and offers a flexible platform for integrating advanced statistical and machine learning techniques. Full article

(This article belongs to the Special Issue Application of 2D and 3D-QSAR Models in Drug Design)

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15 pages, 3155 KiB

Open AccessArticle

Repurposing FDA-Approved Agents to Develop a Prototype Helicobacter pylori Shikimate Kinase (HPSK) Inhibitor: A Computational Approach Using Virtual Screening, MM-GBSA Calculations, MD Simulations, and DFT Analysis

by Abdulaziz H. Al Khzem, Tagyedeen H. Shoaib, Rua M. Mukhtar, Mansour S. Alturki, Mohamed S. Gomaa, Dania Hussein, Nada Tawfeeq, Mohsina Bano, Mohammad Sarafroz, Raghad Alzahrani, Hanin Alghamdi and Thankhoe A. Rants’o

Pharmaceuticals 2025, 18(2), 174; https://doi.org/10.3390/ph18020174 - 27 Jan 2025

Viewed by 1128

Abstract

Background/Objectives: Helicobacter pylori infects approximately half of the global population, causing chronic gastritis, peptic ulcers, and gastric cancer, a leading cause of cancer mortality. While current therapies face challenges from rising antibiotic resistance, particularly to clarithromycin, alongside treatment complexity and costs, the World Health Organization has prioritized the development of new antibiotics to combat this high-risk pathogen. In this study, we employed computer-aided drug design (CADD) methodologies, including molecular docking, Molecular Mechanics-Generalized Born Surface Area (MM-GBSA) analysis, molecular dynamics (MD) simulations, and Density Functional Theory (DFT) calculations, to explore the potential repurposing of FDA-approved agents as inhibitors of Helicobacter pylori shikimate kinase (HpSK). Methods: Using the Glide module, the HTVS method was initially applied to screen 1615 FDA-approved agents followed by extra-precision (XP) docking for the obtained 111 hits. The obtained XP scores were used to confine the results to those hits with a score above the reference ligand, shikimate, score. This yielded 31 final hits with an XP score above −5.867. MM-GBSA calculations were performed on these top candidates and the reference ligand to refine the analysis and compounds’ prioritization. Results: The 31 compounds displayed binding free energy (ΔG_bind) values ranging from 3.61 to −55.92 kcal/mol, with shikimate exhibiting a ΔG_bind of −34.24 kcal/mol and 10 hits having a lower ΔG_bind value. Out of these ten, three drugs—Dolutegravir, Cangrelor, and Isavuconazonium—were selected for further analysis based on their drug-like properties. Robust and stable binding profiles for both Isavuconazonium and Cangrelor were verified via molecular dynamics simulation. Additionally, Density Functional Theory (DFT) analysis was conducted, and the Highest Occupied Molecular Orbitals (HOMOs), Lowest Unoccupied Molecular Orbitals (LUMOs), and the energy gap (HLG) between them were calculated. All three drug candidates displayed lower HLG values than shikimate, suggesting higher reactivity and more efficient electronic transitions than the reference ligand. Conclusions: These findings suggest that the identified drugs, although not optimal for direct repurposing, would serve as promising leads against Helicobacter pylori shikimate kinase. These drugs could be valuable leads for experimental assessment and further optimization, particularly with no prototype yet identified. In terms of potential for clinical repurposing, the results point to diflunisal as a promising candidate for further testing. Full article

(This article belongs to the Special Issue Application of 2D and 3D-QSAR Models in Drug Design)

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18 pages, 3941 KiB

Open AccessArticle

A Simple Machine Learning-Based Quantitative Structure–Activity Relationship Model for Predicting pIC₅₀ Inhibition Values of FLT3 Tyrosine Kinase

by Jackson J. Alcázar, Ignacio Sánchez, Cristian Merino, Bruno Monasterio, Gaspar Sajuria, Diego Miranda, Felipe Díaz and Paola R. Campodónico

Pharmaceuticals 2025, 18(1), 96; https://doi.org/10.3390/ph18010096 - 14 Jan 2025

Cited by 1 | Viewed by 1326

Abstract

Background/Objectives: Acute myeloid leukemia (AML) presents significant therapeutic challenges, particularly in cases driven by mutations in the FLT3 tyrosine kinase. This study aimed to develop a robust and user-friendly machine learning-based quantitative structure–activity relationship (QSAR) model to predict the inhibitory potency (pIC₅₀ values) of FLT3 inhibitors, addressing the limitations of previous models in dataset size, diversity, and predictive accuracy. Methods: Using a dataset which was 14 times larger than those employed in prior studies (1350 compounds with 1269 molecular descriptors), we trained a random forest regressor, chosen due to its superior predictive performance and resistance to overfitting. Rigorous internal validation via leave-one-out and 10-fold cross-validation yielded

Q^{2}

values of 0.926 and 0.922, respectively, while external validation on 270 independent compounds resulted in an R² value of 0.941 with a standard deviation of 0.237. Results: Key molecular descriptors influencing the inhibitor potency were identified, thereby improving the interpretability of structural requirements. Additionally, a user-friendly computational tool was developed to enable rapid prediction of pIC₅₀ values and facilitate ligand-based virtual screening, leading to the identification of promising FLT3 inhibitors. Conclusions: These results represent a significant advancement in the field of FLT3 inhibitor discovery, offering a reliable, practical, and efficient approach for early-stage drug development, potentially accelerating the creation of targeted therapies for AML. Full article

(This article belongs to the Special Issue Application of 2D and 3D-QSAR Models in Drug Design)

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28 pages, 21855 KiB

Open AccessArticle

ANN-QSAR, Molecular Docking, ADMET Predictions, and Molecular Dynamics Studies of Isothiazole Derivatives to Design New and Selective Inhibitors of HCV Polymerase NS5B

by Maroua Fattouche, Salah Belaidi, Oussama Abchir, Walid Al-Shaar, Khaled Younes, Muneerah Mogren Al-Mogren, Samir Chtita, Fatima Soualmia and Majdi Hochlaf

Pharmaceuticals 2024, 17(12), 1712; https://doi.org/10.3390/ph17121712 - 18 Dec 2024

Viewed by 1457

Abstract

Background/Objectives: RNA polymerase (NS5B), serves as a crucial target for pharmaceutical interventions aimed at combating the hepatitis C virus (HCV), which poses significant health challenges worldwide. The present research endeavors to explore and implement a variety of advanced molecular modeling techniques that aim to create and identify innovative and highly effective inhibitors that specifically target the RNA polymerase enzyme. Methods: In this study, a QSAR investigation was carried out on a set of thirty-eight isothiazole derivatives targeting NS5B inhibition and thus hepatitis C virus (HCV) treatment. The research methodology made use of various statistical techniques including multiple linear regression (MLR) and artificial neural networks (ANNs) to develop satisfactory models in terms of internal and external validation parameters, indicating their reliability in predicting the activity of new inhibitors. Accordingly, a series of potent NS5B inhibitors is designed, and their inhibitory potential is confirmed through molecular docking simulations. Results: These simulations showed that the interactions between these inhibitors and the active site 221 binding pocket of the NS5B protein are hydrophobic and hydrogen bond interactions, as well as carbon–hydrogen bonds and electrostatic interactions. Additionally, these newly formulated compounds displayed favorable ADMET characteristics, with molecular dynamics investigations revealing a stable energetic state and dynamic equilibrium. Conclusions: Our work highlights the importance of NS5B inhibition for the treatment of HCV. Full article

(This article belongs to the Special Issue Application of 2D and 3D-QSAR Models in Drug Design)

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20 pages, 6138 KiB

Open AccessArticle

Employing Machine Learning-Based QSAR for Targeting Zika Virus NS3 Protease: Molecular Insights and Inhibitor Discovery

by Hisham N. Altayb and Hanan Ali Alatawi

Pharmaceuticals 2024, 17(8), 1067; https://doi.org/10.3390/ph17081067 - 15 Aug 2024

Cited by 2 | Viewed by 1446

Abstract

Zika virus infection is a mosquito-borne viral disease that has become a global health concern recently. Zika virus belongs to the Flavivirus genus and is primarily transmitted by Aedes mosquitoes. Prevention of Zika virus infection involves avoiding mosquito bites by using repellent, wearing protective clothing, and staying in screened areas, especially for pregnant women. Treatment focuses on managing symptoms with rest, fluids, and acetaminophen, with close monitoring for pregnant women. Currently, there is no specific antiviral treatment or vaccine for the Zika virus, highlighting the importance of prevention strategies to control its spread. Therefore, in this study, the Zika virus non-structural protein NS3 was targeted to inhibit Zika infection by identifying the novel inhibitor through an in silico approach. Here, 2864 natural compounds were screened using a machine learning-based QSAR model, and later docking was performed to select the potential target. Subsequently, Tanimoto similarity and clustering were performed to obtain the potential target. The three most potential compounds were obtained: (a) 5297, (b) 432449, and (c) 85137543. The protein–ligand complex’s stability and flexibility were then investigated by dynamic modelling. The 300 ns simulation showed that 5297 exhibited the steadiest deviation and constant creation of hydrogen bonds. Compared to the other compounds, 5297 demonstrated a superior binding free energy (ΔG = −20.81 kcal/mol) with the protein when the MM/GBSA technique was used. The study determined that 5297 showed significant therapeutic potential and justifies further experimental investigation as a possible inhibitor of the NS2B-NS3 protease target implicated in Zika virus infection. Full article

(This article belongs to the Special Issue Application of 2D and 3D-QSAR Models in Drug Design)

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23 pages, 14661 KiB

Open AccessArticle

An In Silico Study Based on QSAR and Molecular Docking and Molecular Dynamics Simulation for the Discovery of Novel Potent Inhibitor against AChE

by Meriem Khedraoui, Oussama Abchir, Hassan Nour, Imane Yamari, Abdelkbir Errougui, Abdelouahid Samadi and Samir Chtita

Pharmaceuticals 2024, 17(7), 830; https://doi.org/10.3390/ph17070830 - 25 Jun 2024

Cited by 10 | Viewed by 2202

Abstract

Acetylcholinesterase (AChE) is one of the main drug targets for treating Alzheimer’s disease. This current study relies on multiple molecular modeling approaches to develop new potent inhibitors of AChE. We explored a 2D QSAR study using the statistical method of multiple linear regression based on a set of substituted 5-phenyl-1,3,4-oxadiazole and N-benzylpiperidine analogs, which were recently synthesized and proved their inhibitory activities against acetylcholinesterase (AChE). The molecular descriptors, polar surface area, dipole moment, and molecular weight are the key structural properties governing AChE inhibition activity. The MLR model was selected based on its statistical parameters: R² = 0.701, R²test = 0.76, Q²CV = 0.638, and RMSE = 0.336, demonstrating its predictive reliability. Randomization tests, VIF tests, and applicability domain tests were adopted to verify the model’s robustness. As a result, 11 new molecules were designed with higher anti-Alzheimer’s activities than the model molecule. We demonstrated their improved pharmacokinetic properties through an in silico ADMET study. A molecular docking study was conducted to explore their AChE inhibition mechanisms and binding affinities in the active site. The binding scores of compounds M1, M2, and M6 were (−12.6 kcal/mol), (−13 kcal/mol), and (−12.4 kcal/mol), respectively, which are higher than the standard inhibitor Donepezil with a binding score of (−10.8 kcal/mol). Molecular dynamics simulations over 100 ns were used to validate the molecular docking results, indicating that compounds M1 and M2 remain stable in the active site, confirming their potential as promising anti-AChE inhibitors. Full article

(This article belongs to the Special Issue Application of 2D and 3D-QSAR Models in Drug Design)

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16 pages, 1774 KiB

Open AccessArticle

QSAR Studies, Synthesis, and Biological Evaluation of New Pyrimido-Isoquinolin-Quinone Derivatives against Methicillin-Resistant Staphylococcus aureus

by Juan Andrades-Lagos, Javier Campanini-Salinas, Gianfranco Sabadini, Victor Andrade, Jaime Mella and David Vásquez-Velásquez

Pharmaceuticals 2023, 16(11), 1621; https://doi.org/10.3390/ph16111621 - 17 Nov 2023

Cited by 2 | Viewed by 1907

Abstract

According to the WHO, antimicrobial resistance is among the top 10 threats to global health. Due to increased resistance rates, an increase in the mortality and morbidity of patients has been observed, with projections of more than 10 million deaths associated with infections caused by antibacterial resistant microorganisms. Our research group has developed a new family of pyrimido-isoquinolin-quinones showing antibacterial activities against multidrug-resistant Staphylococcus aureus. We have developed 3D-QSAR CoMFA and CoMSIA studies (r² = 0.938; 0.895), from which 13 new derivatives were designed and synthesized. The compounds were tested in antibacterial assays against methicillin-resistant Staphylococcus aureus and other bacterial pathogens. There were 12 synthesized compounds active against Gram-positive pathogens in concentrations ranging from 2 to 32 µg/mL. The antibacterial activity of the derivatives is explained by the steric, electronic, and hydrogen-bond acceptor properties of the compounds. Full article

(This article belongs to the Special Issue Application of 2D and 3D-QSAR Models in Drug Design)

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