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Computational Approaches: Drug Discovery and Design in Medicinal Chemistry and Bioinformatics, 3rd Edition

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

Deadline for manuscript submissions: closed (31 January 2025) | Viewed by 14226

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Prof. Dr. Anna Maria Almerico

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Dipartimento di Scienze e Tecnologie Biologiche Chimiche e Farmaceutiche (STEBICEF), Università di Palermo, Palermo, Italy
Interests: medicinal chemistry; molecular modeling; QSAR; pharmacophore modeling; molecular dynamics; docking
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Dr. Marco Tutone

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Dipartimento di Scienze e Tecnologie Biologiche Chimiche e Farmaceutiche (STEBICEF), Università degli Studi di Palermo, Via Archirafi 32, 90123 Palermo, Italy
Interests: oxidative stress; nutraceuticals; anticancer drugs; medicinal chemistry; drug design and discovery; molecular modeling; QSAR; pharmacophore modeling; molecular dynamics; docking; HTVS; cystic fibrosis translational readthrough inducing drugs (TRIDs)
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Special Issue Information

Dear Colleagues,

After the great success of the first two editions, we are pleased to inform you that Molecules will launch the third edition of the Special Issue “Computational Approaches: Drug Discovery and Design in Medicinal Chemistry and Bioinformatics”.

https://www.mdpi.com/journal/molecules/special_issues/comput_appr_drug_dis_des_med_chem_bio

https://www.mdpi.com/journal/molecules/special_issues/comput_approach_drug_II

In this latest Special Issue, we will continue to collect manuscripts concerning computational approaches that can improve the development of new drugs, or the repurposing of an old drug for the treatment of new diseases.

In this light, structure-based approaches such as docking, induced-fit docking, molecular dynamics simulations, free energy calculations, reverse docking, and ligand-based approaches, such as molecular similarity fingerprints, shape methods, pharmacophore modeling, and QSAR, represent efficient tools to obtain insights in hit/lead identification and the optimization of small molecules and/or natural compounds. Moreover, computational approaches help to predict the metabolic fate of a drug candidate and to highlight the potential toxicity of the drug candidate, reducing the number of compounds to be tested. In the end, computational approaches were revealed to be of great interest in the field of nutraceuticals, allowing the identification of the mechanisms of action. This Special Issue welcomes submissions from researchers in the field of drug discovery and design, including original research and review articles related to pharmaceutical sciences, pharmacology, chemical biology, and bioinformatics.

Papers combining both experimental and computational studies are encouraged.

Prof. Dr. Anna Maria Almerico
Dr. Marco Tutone
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

QSAR and 3D-QSAR
unbiased and biased molecular dynamics
pharmacophore modeling
reverse modeling
Ab initio calculations
protein–protein interactions
free energy profiling
modeling of nucleic acids (mRNA, rRNA, tRNA)
molecular docking
virtual screening
multitarget approaches
ADMET prediction
similarity analysis
computational approaches applied to natural compounds and nutraceuticals

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Related Special Issue

Computational Approaches: Drug Discovery and Design in Medicinal Chemistry and Bioinformatics, 4th Edition in Molecules

Published Papers (8 papers)

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Research

20 pages, 5122 KiB

Open AccessArticle

Machine Learning Tool for New Selective Serotonin and Serotonin–Norepinephrine Reuptake Inhibitors

by Natalia Łapińska, Jakub Szlęk, Adam Pacławski and Aleksander Mendyk

Molecules 2025, 30(3), 637; https://doi.org/10.3390/molecules30030637 - 31 Jan 2025

Viewed by 976

Abstract

Depression, a serious mood disorder, affects about 5% of the population. Currently, there are two groups of antidepressants that are the first-line treatment for depressive disorder: selective serotonin reuptake inhibitors and serotonin–norepinephrine reuptake inhibitors. The aim of the study was to develop Quantitative Structure–Activity Relationship (QSAR) models for serotonin (SERT) and norepinephrine (NET) transporters to predict the affinity and inhibition potential of new molecules. Models were developed using the Automated Machine Learning tool Mljar based on 80% of the dataset according to 10-fold cross-validation and externally validated on the remaining 20% of data. The molecular representation featured two-dimensional Mordred descriptors. For each model, Shapley additive explanations analysis was performed to clarify the influence of the descriptors on the models’ predictions. Based on the final QSAR models, the following results were obtained: NET and pIC50 value RMSE_test = 0.678, R²_test = 0.640; NET and pKi RMSE_test = 0.590, R²_test = 0.709; SERT and pIC50 RMSE_test = 0.645, R²_test = 0.678; SERT and pKi value RMSE_test = 0.540, R²_test = 0.828. QSAR models for serotonin and norepinephrine transporters have been made available in a new module of the SerotoninAI application to enhance usability for scientists. Full article

(This article belongs to the Special Issue Computational Approaches: Drug Discovery and Design in Medicinal Chemistry and Bioinformatics, 3rd Edition)

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34 pages, 17958 KiB

Open AccessArticle

Exploring the Potential of Malvidin and Echiodinin as Probable Antileishmanial Agents Through In Silico Analysis and In Vitro Efficacy

by Luis Daniel Goyzueta-Mamani, Daniela Pagliara Lage, Haruna Luz Barazorda-Ccahuana, Margot Paco-Chipana, Mayron Antonio Candia-Puma, Gonzalo Davila-Del-Carpio, Alexsandro Sobreira Galdino, Ricardo Andrez Machado-de-Avila, Rodolfo Cordeiro Giunchetti, Edward L. D’Antonio, Eduardo Antonio Ferraz Coelho and Miguel Angel Chávez-Fumagalli

Molecules 2025, 30(1), 173; https://doi.org/10.3390/molecules30010173 - 4 Jan 2025

Viewed by 1349

Abstract

Leishmaniasis, a neglected tropical disease caused by Leishmania species, presents serious public health challenges due to limited treatment options, toxicity, high costs, and drug resistance. In this study, the in vitro potential of malvidin and echioidinin is examined as antileishmanial agents against L. amazonensis, L. braziliensis, and L. infantum, comparing their effects to amphotericin B (AmpB), a standard drug. Malvidin demonstrated greater potency than echioidinin across all parasite stages and species. Against L. amazonensis, malvidin’s IC₅₀ values were 197.71 ± 17.20 µM (stationary amastigotes) and 258.07 ± 17 µM (axenic amastigotes), compared to echioidinin’s 272.99 ± 29.90 μM and 335.96 ± 19.35 μM. AmpB was more potent, with IC₅₀ values of 0.06 ± 0.01 µM and 0.10 ± 0.03 µM. Malvidin exhibited lower cytotoxicity (CC₅₀: 2920.31 ± 80.29 µM) than AmpB (1.06 ± 0.12 µM) and a favorable selectivity index. It reduced infection rates by 35.75% in L. amazonensis-infected macrophages. The in silico analysis revealed strong binding between malvidin and Leishmania arginase, with the residues HIS139 and PRO258 playing key roles. Gene expression analysis indicated malvidin’s modulation of oxidative stress and DNA repair pathways, involving genes like GLO1 and APEX1. These findings suggest malvidin’s potential as a safe, natural antileishmanial compound, warranting further in vivo studies to confirm its therapeutic efficacy and pharmacokinetics in animal models. Full article

(This article belongs to the Special Issue Computational Approaches: Drug Discovery and Design in Medicinal Chemistry and Bioinformatics, 3rd Edition)

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15 pages, 4015 KiB

Open AccessArticle

In Silico Prediction of Alkaline Phosphatase Interaction with the Natural Inhibitory 5-Azaindoles Guitarrin C and D

by Aleksandra Seitkalieva, Yulia Noskova, Marina Isaeva, Alla Guzii, Tatyana N. Makarieva, Sergey Fedorov and Larissa Balabanova

Molecules 2024, 29(23), 5701; https://doi.org/10.3390/molecules29235701 - 3 Dec 2024

Viewed by 1148

Abstract

The natural 5-azaindoles, marine sponge guitarrin C and D, were observed to exert inhibitory activity against a highly active alkaline phosphatase (ALP) CmAP of the PhoA family from the marine bacterium Cobetia amphilecti, with IC₅₀ values of 8.5 and 110 µM, respectively. The superimposition of CmAP complexes with p-nitrophenyl phosphate (pNPP), a commonly used chromogenic aryl substrate for ALP, and the inhibitory guitarrins C, D, and the non-inhibitory guitarrins A, B, and E revealed that the presence of a carboxyl group at C6 together with a hydroxyl group at C8 is a prerequisite for the inhibitory effect of 5-azaindoles on ALP activity. The 10-fold more active guitarrin C could compete with pNPP for binding sites in the ALP active site due to similarities in size, three-dimensional structure, and the orientation of the COOH group along the phosphate group. However, the inhibition of CmAP and calf intestinal ALP (CIAP) by guitarrin C was observed to occur via a non-competitive mode of action, as evidenced by a twofold decrease in V_max and an unchanged K_m. In contrast, the kinetic model with guitarrin D, with an additional OH group at C7, reflected a mixed type of inhibition, with a decrease in both values. The sensitivity of CIAP to guitarrins C and D was shown to be slightly lower than that of CmAP, with IC₅₀ values of 195 and 230 µM, respectively. Nevertheless, these findings prompted the prediction of complexes of human ALP isoenzymes with guitarrins C and D. Full article

(This article belongs to the Special Issue Computational Approaches: Drug Discovery and Design in Medicinal Chemistry and Bioinformatics, 3rd Edition)

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18 pages, 4222 KiB

Open AccessArticle

Exploring Radioiodinated Anastrozole and Epirubicin as AKT1-Targeted Radiopharmaceuticals in Breast Cancer: In Silico Analysis and Potential Therapeutic Effect with Functional Nuclear Imagining Implications

by Mazen Abdulrahman Binmujlli

Molecules 2024, 29(17), 4203; https://doi.org/10.3390/molecules29174203 - 4 Sep 2024

Cited by 1 | Viewed by 1392

Abstract

This study evaluates radio-iodinated anastrozole ([¹²⁵I]anastrozole) and epirubicin ([¹²⁵I]epirubicin) for AKT1-targeted breast cancer therapy, utilizing radiopharmaceutical therapy (RPT) for personalized treatment. Through molecular docking and dynamics simulations (200 ns), it investigates these compounds’ binding affinities and mechanisms to the AKT1 enzyme, compared to the co-crystallized ligand, a known AKT1 inhibitor. Molecular docking results show that [¹²⁵I]epirubicin has the highest ΔG_bind (−11.84 kcal/mol), indicating a superior binding affinity compared to [¹²⁵I] anastrozole (−10.68 kcal/mol) and the co-crystallized ligand (−9.53 kcal/mol). Molecular dynamics (MD) simulations confirmed a stable interaction with the AKT1 enzyme, with [¹²⁵I]anastrozole and [¹²⁵I]epirubicin reaching stability after approximately 68 ns with an average RMSD of around 2.2 Å, while the co-crystallized ligand stabilized at approximately 2.69 Å after 87 ns. RMSF analysis showed no significant shifts in residues or segments, with consistent patterns and differences of less than 2 Å, maintaining enzyme stability. The [¹²⁵I]epirubicin complex maintained an average of four H-bonds, indicating strong and stable interactions, while [¹²⁵I]anastrozole consistently formed three H-bonds. The average Rg values for both complexes were ~16.8 ± 0.1 Å, indicating no significant changes in the enzyme’s compactness, thus preserving structural integrity. These analyses reveal stable binding and minimal structural perturbations, suggesting the high potential for AKT1 inhibition. MM-PBSA calculations confirm the potential of these radio-iodinated compounds as AKT1 inhibitors, with [¹²⁵I]epirubicin exhibiting the most favorable binding energy (−23.57 ± 0.14 kcal/mol) compared to [¹²⁵I]anastrozole (−20.03 ± 0.15 kcal/mol) and the co-crystallized ligand (−16.38 ± 0.14 kcal/mol), highlighting the significant role of electrostatic interactions in stabilizing the complex. The computational analysis shows [¹²⁵I]anastrozole and [¹²⁵I]epirubicin may play promising roles as AKT1 inhibitors, especially [¹²⁵I]epirubicin for its high binding affinity and dynamic receptor interactions. These findings, supported by molecular docking scores and MM-PBSA binding energies, advocate for their potential superior inhibitory capability against the AKT1 enzyme. Nevertheless, it is crucial to validate these computational predictions through in vitro and in vivo studies to thoroughly evaluate the therapeutic potential and viability of these compounds for AKT1-targeted breast cancer treatment. Full article

(This article belongs to the Special Issue Computational Approaches: Drug Discovery and Design in Medicinal Chemistry and Bioinformatics, 3rd Edition)

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17 pages, 16690 KiB

Open AccessArticle

Ensemble-Based Virtual Screening Led to the Discovery of Novel Lead Molecules as Potential NMBAs

by Yi Zhang, Gonghui Ge, Xiangyang Xu and Jinhui Wu

Molecules 2024, 29(9), 1955; https://doi.org/10.3390/molecules29091955 - 24 Apr 2024

Viewed by 1736

Abstract

Neuromuscular blocking agents (NMBAs) are routinely used during anesthesia to relax skeletal muscle. Nicotinic acetylcholine receptors (nAChRs) are ligand-gated ion channels; NMBAs can induce muscle paralysis by preventing the neurotransmitter acetylcholine (ACh) from binding to nAChRs situated on the postsynaptic membranes. Despite widespread efforts, it is still a great challenge to find new NMBAs since the introduction of cisatracurium in 1995. In this work, an effective ensemble-based virtual screening method, including molecular property filters, 3D pharmacophore model, and molecular docking, was applied to discover potential NMBAs from the ZINC15 database. The results showed that screened hit compounds had better docking scores than the reference compound d-tubocurarine. In order to further investigate the binding modes between the hit compounds and nAChRs at simulated physiological conditions, the molecular dynamics simulation was performed. Deep analysis of the simulation results revealed that ZINC257459695 can stably bind to nAChRs’ active sites and interact with the key residue Asp165. The binding free energies were also calculated for the obtained hits using the MM/GBSA method. In silico ADMET calculations were performed to assess the pharmacokinetic properties of hit compounds in the human body. Overall, the identified ZINC257459695 may be a promising lead compound for developing new NMBAs as an adjunct to general anesthesia, necessitating further investigations. Full article

(This article belongs to the Special Issue Computational Approaches: Drug Discovery and Design in Medicinal Chemistry and Bioinformatics, 3rd Edition)

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16 pages, 5896 KiB

Open AccessArticle

Photophysical Exploration of Alectinib and Rilpivirine: Insights from Theory and Experiment

by Chun Zhang, Yuting Yang, Suya Gan, Aimin Ren, Yu-Bo Zhou, Jia Li, Da-Jun Xiang and Wen-Long Wang

Molecules 2023, 28(16), 6172; https://doi.org/10.3390/molecules28166172 - 21 Aug 2023

Cited by 6 | Viewed by 1868

Abstract

Due to the excellent characteristics of fluorescence-based imaging, such as non-invasive detection of biomarkers in vitro and in vivo with high sensitivity, good spatio-temporal resolution and fast response times, it has shown significant prospects in various applications. Compounds with both biological activities and fluorescent properties have the potential for integrated diagnosis and treatment application. Alectinib and Rilpivirine are two excellent drugs on sale that represent a clinically approved targeted therapy for ALK-rearranged NSCLC and have exhibited more favorable safety and tolerance profiles in Phase III clinical trials, ECHO and THRIVE, respectively. The optical properties of these two drugs, Alectinib and Rilpivirine, were deeply explored, firstly through the simulation of molecular structures, electrostatic potential, OPA/TPA and emission spectral properties and experiments on UV-vis spectra, fluorescence and cell imaging. It was found that Alectinib exhibited 7.8% of fluorescence quantum yield at the 450 nm excited wavelength, due to a larger electronic transition dipole moment (8.41 Debye), bigger charge transition quantity (0.682 e) and smaller reorganization energy (2821.6 cm⁻¹). The stronger UV-vis spectra of Rilpivirine were due to a larger electron–hole overlap index (Sr: 0.733) and were also seen in CDD plots. Furthermore, Alectinib possessed obvious active two-photon absorption properties (

δ_{m a x}^{T P A}

* ϕ = 201.75 GM), which have potential TPA imaging applications in bio-systems. Lastly, Alectinib and Rilpivirine displayed green fluorescence in HeLa cells, suggesting the potential ability for biological imaging. Investigation using theoretical and experimental methods is certainly encouraged, given the particular significance of developing integrated diagnosis and treatment. Full article

(This article belongs to the Special Issue Computational Approaches: Drug Discovery and Design in Medicinal Chemistry and Bioinformatics, 3rd Edition)

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16 pages, 4645 KiB

Open AccessArticle

mPGES-1 Inhibitor Discovery Based on Computer-Aided Screening: Pharmacophore Models, Molecular Docking, ADMET, and MD Simulations

by Qiqi Huang, Tianli Lai, Qu Wang and Lianxiang Luo

Molecules 2023, 28(16), 6059; https://doi.org/10.3390/molecules28166059 - 15 Aug 2023

Cited by 5 | Viewed by 2448

Abstract

mPGES-1 is an enzyme, which, when activated by inflammatory factors, can cause prostaglandin E synthesis. Traditional non-steroidal anti-inflammatory drugs are capable of inhibiting prostaglandin production, yet they can also cause gastrointestinal reactions and coagulation disorders. mPGES-1, the enzyme at the conclusion of prostaglandin production, does not cause any adverse reactions when inhibited. Numerous studies have demonstrated that mPGES-1 is more abundant in cancerous cells than in healthy cells, indicating that decreasing the expression of mPGES-1 could be a potential therapeutic strategy for cancer. Consequently, the invention of mPGES-1 inhibitors presents a fresh avenue for the treatment of inflammation and cancer. Incorporating a database of TCM compounds, we collected a batch of compounds that had an inhibitory effect on mPGES-1 and possessed IC50 value. Firstly, a pharmacophore model was constructed, and the TCM database was screened, and the compounds with score cut-off values of more than 1 were retained. Then, the compounds retained after being screened via the pharmacodynamic model were screened for docking at the mPGES-1 binding site, followed by high-throughput virtual screening [HTVS] and standard precision [SP] and super-precision [XP] docking, and the compounds in the top 20% of the XP docking score were selected to calculate the total free binding energy of MM-GBSA. The best ten compounds were chosen by comparing their score against the reference ligand 4U9 and the MM-GBSA_dG_Bind score. ADMET analysis resulted in the selection of ten compounds, three of which had desirable medicinal properties. Finally, the binding energy of the target protein mPGES-1 and the candidate ligand compound was analyzed using a 100 ns molecular dynamics simulation of the reference ligand 4U9 and three selected compounds. After a gradual screening study and analysis, we identified a structure that is superior to the reference ligand 4U9 in all aspects, namely compound 15643. Taken together, the results of this study reveal a structure that can be used to inhibit mPGES-1 compound 15643, thereby providing a new option for anti-inflammatory and anti-tumor drugs. Full article

(This article belongs to the Special Issue Computational Approaches: Drug Discovery and Design in Medicinal Chemistry and Bioinformatics, 3rd Edition)

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16 pages, 3492 KiB

Open AccessArticle

Collection of Partition Coefficients in Hexadecyltrimethylammonium Bromide, Sodium Cholate, and Lithium Perfluorooctanesulfonate Micellar Solutions: Experimental Determination and Computational Predictions

by Leila Saranjam, Miroslava Nedyalkova, Elisabet Fuguet, Vasil Simeonov, Francesc Mas and Sergio Madurga

Molecules 2023, 28(15), 5729; https://doi.org/10.3390/molecules28155729 - 28 Jul 2023

Cited by 2 | Viewed by 1569

Abstract

This study focuses on determining the partition coefficients (logP) of a diverse set of 63 molecules in three distinct micellar systems: hexadecyltrimethylammonium bromide (HTAB), sodium cholate (SC), and lithium perfluorooctanesulfonate (LPFOS). The experimental log p values were obtained through micellar electrokinetic chromatography (MEKC) experiments, conducted under controlled pH conditions. Then, Quantum Mechanics (QM) and machine learning approaches are proposed for the prediction of the partition coefficients in these three micellar systems. In the applied QM approach, the experimentally obtained partition coefficients were correlated with the calculated values for the case of the 15 solvent mixtures. Using Density Function Theory (DFT) with the B3LYP functional, we calculated the solvation free energies of 63 molecules in these 16 solvents. The combined data from the experimental partition coefficients in the three micellar formulations showed that the 1-propanol/water combination demonstrated the best agreement with the experimental partition coefficients for the SC and HTAB micelles. Moreover, we employed the SVM approach and k-means clustering based on the generation of the chemical descriptor space. The analysis revealed distinct partitioning patterns associated with specific characteristic features within each identified class. These results indicate the utility of the combined techniques when we want an efficient and quicker model for predicting partition coefficients in diverse micelles. Full article

(This article belongs to the Special Issue Computational Approaches: Drug Discovery and Design in Medicinal Chemistry and Bioinformatics, 3rd Edition)

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