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17 pages, 13401 KiB

Open AccessArticle

Theoretical Investigations on Free Energy of Binding Cilostazol with Different Cyclodextrins as Complex for Selective PDE3 Inhibition

by Marta Hoelm, Nilkanta Chowdhury, Sima Biswas, Angshuman Bagchi and Magdalena Małecka

Molecules 2024, 29(16), 3824; https://doi.org/10.3390/molecules29163824 - 12 Aug 2024

Viewed by 892

Abstract

Cilostazol is a phosphodiesterase III inhibitor characterized by poor solubility. This limitation can be overcome by using a drug carrier capable of delivering the drug to the target site. Cyclodextrins are essential as drug carriers because of their outstanding complexation abilities and their [...] Read more.

Cilostazol is a phosphodiesterase III inhibitor characterized by poor solubility. This limitation can be overcome by using a drug carrier capable of delivering the drug to the target site. Cyclodextrins are essential as drug carriers because of their outstanding complexation abilities and their capacity to improve drug bioavailability. This study comprises two stages: The first involves verifying different cyclodextrins and their complexation abilities towards cilostazol. This was accomplished using molecular docking simulations (MDS) and density functional theory (DFT). Both techniques indicate that the largest Sulfobutyl Ether-β-Cyclodextrin forms the most stable complex with cilostazol. Additionally, other important parameters of the complex are described, including binding sites, dominant interactions, and thermodynamic parameters such as complexation enthalpy, Gibbs free energy, and Gibbs free energy of solvation. The second stage involves a binding study between cilostazol and Phosphodiesterse3 (PDE3). This study was conducted using molecular docking simulations, and the most important energetic parameters are detailed. This is the first such report, and we believe that the results of our predictions will pave the way for future drug development efforts using cyclodextrin–cilostazol complexes as potential therapeutics. Full article

(This article belongs to the Special Issue Computational Studies on the Development and Characterization of Pharmaceutical Materials)

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16 pages, 4217 KiB

Open AccessArticle

Substitution-Induced Mechanistic Switching in S_NAr-Warheads for Cysteine Proteases

by Collin Zimmer, Jan Brauer, Dorota Ferenc, Jessica Meyr, Patrick Müller, Hans-Joachim Räder, Bernd Engels, Till Opatz and Tanja Schirmeister

Molecules 2024, 29(11), 2660; https://doi.org/10.3390/molecules29112660 - 4 Jun 2024

Cited by 1 | Viewed by 1114

Abstract

The aim of this study was to investigate the transition from non-covalent reversible over covalent reversible to covalent irreversible inhibition of cysteine proteases by making delicate structural changes to the warhead scaffold. To this end, dipeptidic rhodesain inhibitors with different N-terminal electrophilic [...] Read more.

The aim of this study was to investigate the transition from non-covalent reversible over covalent reversible to covalent irreversible inhibition of cysteine proteases by making delicate structural changes to the warhead scaffold. To this end, dipeptidic rhodesain inhibitors with different N-terminal electrophilic arenes as warheads relying on the S_NAr mechanism were synthesized and investigated. Strong structure–activity relationships of the inhibition potency, the degree of covalency, and the reversibility of binding on the arene substitution pattern were found. The studies were complemented and substantiated by molecular docking and quantum-mechanical calculations of model systems. Furthermore, the improvement in the membrane permeability of peptide esters in comparison to their corresponding carboxylic acids was exemplified. Full article

(This article belongs to the Special Issue Computational Studies on the Development and Characterization of Pharmaceutical Materials)

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29 pages, 7395 KiB

Open AccessArticle

Identification of Phytochemicals from Arabian Peninsula Medicinal Plants as Strong Binders to SARS-CoV-2 Proteases (3CL^Pro and PL^Pro) by Molecular Docking and Dynamic Simulation Studies

by Quaiser Saquib, Ahmed H. Bakheit, Sarfaraz Ahmed, Sabiha M. Ansari, Abdullah M. Al-Salem and Abdulaziz A. Al-Khedhairy

Molecules 2024, 29(5), 998; https://doi.org/10.3390/molecules29050998 - 25 Feb 2024

Cited by 1 | Viewed by 1502

Abstract

We provide promising computational (in silico) data on phytochemicals (compounds 1–10) from Arabian Peninsula medicinal plants as strong binders, targeting 3-chymotrypsin-like protease (3CL^Pro) and papain-like proteases (PL^Pro) of SARS-CoV-2. Compounds 1–10 followed the Lipinski [...] Read more.

We provide promising computational (in silico) data on phytochemicals (compounds 1–10) from Arabian Peninsula medicinal plants as strong binders, targeting 3-chymotrypsin-like protease (3CL^Pro) and papain-like proteases (PL^Pro) of SARS-CoV-2. Compounds 1–10 followed the Lipinski rules of five (RO5) and ADMET analysis, exhibiting drug-like characters. Non-covalent (reversible) docking of compounds 1–10 demonstrated their binding with the catalytic dyad (CYS145 and HIS41) of 3CL^Pro and catalytic triad (CYS111, HIS272, and ASP286) of PL^Pro. Moreover, the implementation of the covalent (irreversible) docking protocol revealed that only compounds 7, 8, and 9 possess covalent warheads, which allowed the formation of the covalent bond with the catalytic dyad (CYS145) in 3CL^Pro and the catalytic triad (CYS111) in PL^Pro. Root-mean-square deviation (RMSD), root-mean-square fluctuation (RMSF), and radius of gyration (Rg) analysis from molecular dynamic (MD) simulations revealed that complexation between ligands (compounds 7, 8, and 9) and 3CL^Pro and PL^Pro was stable, and there was less deviation of ligands. Overall, the in silico data on the inherent properties of the above phytochemicals unravel the fact that they can act as reversible inhibitors for 3CL^Pro and PL^Pro. Moreover, compounds 7, 8, and 9 also showed their novel properties to inhibit dual targets by irreversible inhibition, indicating their effectiveness for possibly developing future drugs against SARS-CoV-2. Nonetheless, to confirm the theoretical findings here, the effectiveness of the above compounds as inhibitors of 3CL^Pro and PL^Pro warrants future investigations using suitable in vitro and in vivo tests. Full article

(This article belongs to the Special Issue Computational Studies on the Development and Characterization of Pharmaceutical Materials)

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16 pages, 5437 KiB

Open AccessArticle

LCK-SafeScreen-Model: An Advanced Ensemble Machine Learning Approach for Estimating the Binding Affinity between Compounds and LCK Target

by Ying Cheng, Cong Ji, Jun Xu, Roufen Chen, Yu Guo, Qingyu Bian, Zheyuan Shen and Bo Zhang

Molecules 2023, 28(21), 7382; https://doi.org/10.3390/molecules28217382 - 1 Nov 2023

Cited by 1 | Viewed by 1524

Abstract

The lymphocyte-specific protein tyrosine kinase (LCK) is a critical target in leukemia treatment. However, potential off-target interactions involving LCK can lead to unintended consequences. This underscores the importance of accurately predicting the inhibitory reactions of drug molecules with LCK during the research and [...] Read more.

The lymphocyte-specific protein tyrosine kinase (LCK) is a critical target in leukemia treatment. However, potential off-target interactions involving LCK can lead to unintended consequences. This underscores the importance of accurately predicting the inhibitory reactions of drug molecules with LCK during the research and development stage. To address this, we introduce an advanced ensemble machine learning technique designed to estimate the binding affinity between molecules and LCK. This comprehensive method includes the generation and selection of molecular fingerprints, the design of the machine learning model, hyperparameter tuning, and a model ensemble. Through rigorous optimization, the predictive capabilities of our model have been significantly enhanced, raising test R² values from 0.644 to 0.730 and reducing test RMSE values from 0.841 to 0.732. Utilizing these advancements, our refined ensemble model was employed to screen an MCE -like drug library. Through screening, we selected the top ten scoring compounds, and tested them using the ADP-Glo bioactivity assay. Subsequently, we employed molecular docking techniques to further validate the binding mode analysis of these compounds with LCK. The exceptional predictive accuracy of our model in identifying LCK inhibitors not only emphasizes its effectiveness in projecting LCK-related safety panel predictions but also in discovering new LCK inhibitors. For added user convenience, we have also established a webserver, and a GitHub repository to share the project. Full article

(This article belongs to the Special Issue Computational Studies on the Development and Characterization of Pharmaceutical Materials)

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19 pages, 16933 KiB

Open AccessArticle

Evaluation of the Cytotoxic and Antiviral Effects of Small Molecules Selected by In Silico Studies as Inhibitors of SARS-CoV-2 Cell Entry

by Francisca Carvalhal, Ana Cristina Magalhães, Rita Rebelo, Andreia Palmeira, Diana I. S. P. Resende, Fernando Durães, Miguel Maia, Cristina P. R. Xavier, Luísa Pereira, Emília Sousa, Marta Correia-da-Silva and M. Helena Vasconcelos

Molecules 2023, 28(20), 7204; https://doi.org/10.3390/molecules28207204 - 21 Oct 2023

Cited by 2 | Viewed by 1493

Abstract

The spike protein of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) relies on host cell surface glycans to facilitate interaction with the angiotensin-converting enzyme 2 (ACE-2) receptor. This interaction between ACE2 and the spike protein is a gateway for the virus to [...] Read more.

The spike protein of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) relies on host cell surface glycans to facilitate interaction with the angiotensin-converting enzyme 2 (ACE-2) receptor. This interaction between ACE2 and the spike protein is a gateway for the virus to enter host cells and may be targeted by antiviral drugs to inhibit viral infection. Therefore, targeting the interaction between these two proteins is an interesting strategy to prevent SARS-CoV-2 infection. A library of glycan mimetics and derivatives was selected for a virtual screening performed against both ACE2 and spike proteins. Subsequently, in vitro assays were performed on eleven of the most promising in silico compounds to evaluate: (i) their efficacy in inhibiting cell infection by SARS-CoV-2 (using the Vero CCL-81 cell line as a model), (ii) their impact on ACE2 expression (in the Vero CCL-81 and MDA-MB-231 cell lines), and (iii) their cytotoxicity in a human lung cell line (A549). We identified five synthetic compounds with the potential to block SARS-CoV-2 infection, three of them without relevant toxicity in human lung cells. Xanthene 1 stood out as the most promising anti-SARS-CoV-2 agent, inhibiting viral infection and viral replication in Vero CCL-81 cells, without causing cytotoxicity to human lung cells. Full article

(This article belongs to the Special Issue Computational Studies on the Development and Characterization of Pharmaceutical Materials)

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15 pages, 9491 KiB

Open AccessArticle

In Silico Discovery of Small-Molecule Inhibitors Targeting SARS-CoV-2 Main Protease

by Menghan Gao, Dongwei Kang, Na Liu and Yanna Liu

Molecules 2023, 28(14), 5320; https://doi.org/10.3390/molecules28145320 - 10 Jul 2023

Cited by 6 | Viewed by 1845

Abstract

The COVID-19 pandemic has caused severe health threat globally, and novel SARS-Cov-2 inhibitors are urgently needed for antiviral treatment. The main protease (M^pro) of the virus is one of the most effective and conserved targets for anti-SARS-CoV-2 drug development. In this [...] Read more.

The COVID-19 pandemic has caused severe health threat globally, and novel SARS-Cov-2 inhibitors are urgently needed for antiviral treatment. The main protease (M^pro) of the virus is one of the most effective and conserved targets for anti-SARS-CoV-2 drug development. In this study, we utilized a molecular docking-based virtual screening approach against the conserved catalytic site to identify small-molecule inhibitors of SARS-CoV-2 M^pro. Further biological evaluation helped us identify two compounds, AF-399/40713777 and AI-942/42301830, with moderate inhibitory activity. Besides that, the in silico data, including molecular dynamics (MD) simulation, binding free energy calculations, and AMDET profiles, suggested that these two hits could serve as the starting point for the future development of COVID-19 intervention treatments. Full article

(This article belongs to the Special Issue Computational Studies on the Development and Characterization of Pharmaceutical Materials)

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14 pages, 4931 KiB

Open AccessArticle

Computational Insights into Novel Inhibitor N-(3-(tert-Butylcarbamoyl)-4-methoxyphenyl)-indole and Ingliforib Specific against GP Isoenzyme Dimers Interaction Mechanism

by Youde Wang, Shuai Li, Zhiwei Yan and Liying Zhang

Molecules 2023, 28(13), 4909; https://doi.org/10.3390/molecules28134909 - 22 Jun 2023

Viewed by 1154

Abstract

The high conservation of the three subtypes of glycogen phosphorylase (GP) presents significant challenges for specific inhibitor studies targeting GP. Our prior screening revealed that compound 1 exhibited unequal inhibitory activity against the three GP subtypes, with a noticeable effect against brain GP [...] Read more.

The high conservation of the three subtypes of glycogen phosphorylase (GP) presents significant challenges for specific inhibitor studies targeting GP. Our prior screening revealed that compound 1 exhibited unequal inhibitory activity against the three GP subtypes, with a noticeable effect against brain GP (PYGB). The commercially available ingliforib demonstrated potent inhibitory activity specifically against liver GP (PYGL). To guide the further design and screening of high-specificity inhibitors, the possible reasons for the differential inhibitory activity of two compounds against different GP subtypes were analyzed, with ingliforib as a reference, through molecular docking and molecular dynamics simulations. Initially, the study predicted the binding modes of ligands with the three GP receptor subtypes using molecular docking. Subsequently, this was validated by molecular dynamics experiments, and possible amino acid residues that had important interactions were explored. The strong correlation between the calculated interaction free energies and experimental inhibitory activity implied the reasonable binding conformations of the compounds. These findings offer insight into the different inhibitory activity of compound 1 and ingliforib against all three GP subtypes and provide guidance for the design of specific target molecules that regulate subtype selectivity. Full article

(This article belongs to the Special Issue Computational Studies on the Development and Characterization of Pharmaceutical Materials)

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14 pages, 6880 KiB

Open AccessArticle

In Silico and In Vitro Methods in the Characterization of Beta-Carotene as Pharmaceutical Material via Acetylcholine Esterase Inhibitory Actions

by Arunachalam Muthuraman, Muthusamy Ramesh, Fazlina Mustaffa, Ahmed Nadeem, Shamama Nishat, Nallupillai Paramakrishnan and Khian Giap Lim

Molecules 2023, 28(11), 4358; https://doi.org/10.3390/molecules28114358 - 26 May 2023

Cited by 7 | Viewed by 1942

Abstract

Molecular docking is widely used in the assessment of the therapeutic potential of pharmaceutical agents. The binding properties of beta-carotene (BC) to acetylcholine esterase (AChE) proteins were characterized using the molecular docking method. The mechanism of AChE inhibition was assessed by an experimental [...] Read more.

Molecular docking is widely used in the assessment of the therapeutic potential of pharmaceutical agents. The binding properties of beta-carotene (BC) to acetylcholine esterase (AChE) proteins were characterized using the molecular docking method. The mechanism of AChE inhibition was assessed by an experimental in vitro kinetic study. In addition, the role of BC action was tested by the zebrafish embryo toxicity test (ZFET). The results of the docking ability of BC to AChE showed significant ligand binding mode. The kinetic parameter, i.e., the low AICc value shown as the compound was the competitive type of inhibition of AChE. Further, BC also showed mild toxicity at a higher dose (2200 mg/L) in ZFET assessment with changes in biomarkers. The LC₅₀ value of BC is 1811.94 mg/L. Acetylcholine esterase (AChE) plays a pivotal role in the hydrolysis of acetylcholine, which leads to the development of cognitive dysfunction. BC possesses the regulation of acetylcholine esterase (AChE) and acid phosphatase (AP) activity to prevent neurovascular dysfunction. Therefore, the characterization of BC could be used as a pharmaceutical agent for the treatment of cholinergic neurotoxicity-associated neurovascular disorders such as developmental toxicity, vascular dementia, and Alzheimer’s disease due to its AChE and AP inhibitory actions. Full article

(This article belongs to the Special Issue Computational Studies on the Development and Characterization of Pharmaceutical Materials)

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14 pages, 4694 KiB

Open AccessArticle

Bioinformatic Analysis of Key Regulatory Genes in Adult Asthma and Prediction of Potential Drug Candidates

by Shaojun Chen, Jiahao Lv, Yiyuan Luo, Hongjiang Chen, Shuwei Ma and Lihua Zhang

Molecules 2023, 28(10), 4100; https://doi.org/10.3390/molecules28104100 - 15 May 2023

Cited by 6 | Viewed by 2015

Abstract

Asthma is a common chronic disease that is characterized by respiratory symptoms including cough, wheeze, shortness of breath, and chest tightness. The underlying mechanisms of this disease are not fully elucidated, so more research is needed to identify better therapeutic compounds and biomarkers [...] Read more.

Asthma is a common chronic disease that is characterized by respiratory symptoms including cough, wheeze, shortness of breath, and chest tightness. The underlying mechanisms of this disease are not fully elucidated, so more research is needed to identify better therapeutic compounds and biomarkers to improve disease outcomes. In this present study, we used bioinformatics to analyze the gene expression of adult asthma in publicly available microarray datasets to identify putative therapeutic molecules for this disease. We first compared gene expression in healthy volunteers and adult asthma patients to obtain differentially expressed genes (DEGs) for further analysis. A final gene expression signature of 49 genes, including 34 upregulated and 15 downregulated genes, was obtained. Protein–protein interaction and hub analyses showed that 10 genes, including POSTN, CPA3, CCL26, SERPINB2, CLCA1, TPSAB1, TPSB2, MUC5B, BPIFA1, and CST1, may be hub genes. Then, the L1000CDS² search engine was used for drug repurposing studies. The top approved drug candidate predicted to reverse the asthma gene signature was lovastatin. Clustergram results showed that lovastatin may perturb MUC5B expression. Moreover, molecular docking, molecular dynamics simulation, and computational alanine scanning results supported the notion that lovastatin may interact with MUC5B via key residues such as Thr80, Thr91, Leu93, and Gln105. In summary, by analyzing gene expression signatures, hub genes, and therapeutic perturbation, we show that lovastatin is an approved drug candidate that may have potential for treating adult asthma. Full article

(This article belongs to the Special Issue Computational Studies on the Development and Characterization of Pharmaceutical Materials)

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12 pages, 21057 KiB

Open AccessArticle

Unlocking the Conformational Changes of P2Y₁₂: Exploring an Acridinone Compound’s Effect on Receptor Activity and Conformation

by Belal O. Al-Najjar and Fadi G. Saqallah

Molecules 2023, 28(9), 3878; https://doi.org/10.3390/molecules28093878 - 4 May 2023

Cited by 1 | Viewed by 1557

Abstract

The P2Y₁₂ receptor is an important member of the purinergic receptor family, known for its critical role in platelet activation and thrombosis. In our previously published study, the acridinone analogue NSC618159 was identified as a potent antagonist of P2Y₁₂. In [...] Read more.

The P2Y₁₂ receptor is an important member of the purinergic receptor family, known for its critical role in platelet activation and thrombosis. In our previously published study, the acridinone analogue NSC618159 was identified as a potent antagonist of P2Y₁₂. In this work, we investigate the conformational changes in P2Y₁₂ when bound to NSC618159 using molecular dynamics simulations on the receptor’s active and inactive forms (4PXZ and 4NTJ, respectively). It was observed that it took the systems about 7 ns and 12 ns to stabilise when NSC618159 was in complex with the active and inactive forms of P2Y₁₂, respectively. Additionally, the binding pocket of the crystal structure 4PXZ expanded from 172.34 Å³ to an average of 661.55 Å³ when bound to NSC618159, with a maximum pocket volume of 820.49 Å³. This expansion was attributed to the pulled away transmembrane (TM) helices and the adoption of a more open conformation by extracellular loop 2 (EL2). In contrast, 4NTJ’s pocket volume was mostly consistent and had an average of 1203.82 Å³. Moreover, the RMSF profile of the NSC618159-4PXZ complex showed that residues of TM-I and TM-VII had similar fluctuations to the 4NTJ crystal structure, representing the inactive form of P2Y₁₂. Finally, the energy components and binding affinities of NSC618159 towards the active and inactive forms of P2Y₁₂ were predicted using the MM-PBSA approach. According to the results, the binding affinity of NSC618159 towards both active (4PXZ) and inactive (4NTJ) forms of P2Y₁₂ was found to be almost identical, with values of −43.52 and −41.68 kcal/mol, respectively. In conclusion, our findings provide new insights into the conformational changes of P2Y₁₂ upon binding to NSC618159 and may have implications for the development of new P2Y₁₂ antagonists with enhanced potency and specificity. Full article

(This article belongs to the Special Issue Computational Studies on the Development and Characterization of Pharmaceutical Materials)

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21 pages, 5000 KiB

Open AccessArticle

Quantum Computational, Spectroscopic (FT-IR, FT-Raman, NMR, and UV–Vis) Hirshfeld Surface and Molecular Docking-Dynamics Studies on 5-Hydroxymethyluracil (Monomer and Trimer)

by Mohit Kumar, Gautam Jaiswar, Mohd. Afzal, Mohd. Muddassir, Abdullah Alarifi, Aysha Fatima, Nazia Siddiqui, Rashid Ayub, Naaser A. Y. Abduh, Waseem Sharaf Saeed and Saleem Javed

Molecules 2023, 28(5), 2116; https://doi.org/10.3390/molecules28052116 - 24 Feb 2023

Cited by 4 | Viewed by 2527

Abstract

For many decades, uracil has been an antineoplastic agent used in combination with tegafur to treat various human cancers, including breast, prostate, and liver cancer. Therefore, it is necessary to explore the molecular features of uracil and its derivatives. Herein, the molecule’s 5-hydroxymethyluracil [...] Read more.

For many decades, uracil has been an antineoplastic agent used in combination with tegafur to treat various human cancers, including breast, prostate, and liver cancer. Therefore, it is necessary to explore the molecular features of uracil and its derivatives. Herein, the molecule’s 5-hydroxymethyluracil has been thoroughly characterized by NMR, UV–Vis, and FT-IR spectroscopy by means of experimental and theoretical analysis. Density functional theory (DFT) using the B3LYP method at 6-311++G(d,p) was computed to achieve the optimized geometric parameters of the molecule in the ground state. For further investigation and computation of the NLO, NBO, NHO analysis, and FMO, the improved geometrical parameters were utilized. The potential energy distribution was used to allocate the vibrational frequencies using the VEDA 4 program. The NBO study determined the relationship between the donor and acceptor. The molecule’s charge distribution and reactive regions were highlighted using the MEP and Fukui functions. Maps of the hole and electron density distribution in the excited state were generated using the TD-DFT method and PCM solvent model in order to reveal electronic characteristics. The energies and diagrams for the lowest unoccupied molecular orbital (LUMO) and the highest occupied molecular orbital (HOMO) were also provided. The HOMO–LUMO band gap estimated the charge transport within the molecule. When examining the intermolecular interactions in 5-HMU, Hirshfeld surface analysis was used, and fingerprint plots were also produced. The molecular docking investigation involved docking 5-HMU with six different protein receptors. Molecular dynamic simulation has given a better idea of the binding of the ligand with protein. Full article

(This article belongs to the Special Issue Computational Studies on the Development and Characterization of Pharmaceutical Materials)

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16 pages, 5058 KiB

Open AccessArticle

In Silico Computational Studies of Bioactive Secondary Metabolites from Wedelia trilobata against Anti-Apoptotic B-Cell Lymphoma-2 (Bcl-2) Protein Associated with Cancer Cell Survival and Resistance

by Hittanahallikoppal Gajendramurthy Gowtham, Faiyaz Ahmed, Satish Anandan, C. S. Shivakumara, Ashween Bilagi, Sushma Pradeep, Chandan Shivamallu, Ali A. Shati, Mohammad Y. Alfaifi, Serag Eldin I. Elbehairi, Raghu Ram Achar, Ekaterina Silina, Victor Stupin, Mahadevamurthy Murali and Shiva Prasad Kollur

Molecules 2023, 28(4), 1588; https://doi.org/10.3390/molecules28041588 - 7 Feb 2023

Cited by 21 | Viewed by 3291

Abstract

In the present study, the binding affinity of 52 bioactive secondary metabolites from Wedelia trilobata towards the anti-apoptotic B-cell lymphoma-2 (Bcl-2) protein (PDB: 2W3L) structure was identified by using in silico molecular docking and molecular dynamics simulation. The molecular docking results demonstrated that [...] Read more.

In the present study, the binding affinity of 52 bioactive secondary metabolites from Wedelia trilobata towards the anti-apoptotic B-cell lymphoma-2 (Bcl-2) protein (PDB: 2W3L) structure was identified by using in silico molecular docking and molecular dynamics simulation. The molecular docking results demonstrated that the binding energies of docked compounds with Bcl-2 protein ranged from −5.3 kcal/mol to −10.1 kcal/mol. However, the lowest binding energy (−10.1 kcal/mol) was offered by Friedelin against Bcl-2 protein when compared to other metabolites and the standard drug Obatoclax (−8.4 kcal/mol). The molecular dynamics simulations revealed that the Friedelin-Bcl-2 protein complex was found to be stable throughout the simulation period of 100 ns. Overall, the predicted Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET) properties of Friedelin are relatively better than Obatoclax, with the most noticeable differences in many parameters where Friedelin has no AMES toxicity, hepatotoxicity, and skin sensitization. The ADMET profiling of selected compounds supported their in silico drug-likeness properties. Based on the computational analyses, the present study concluded that Friedelin of W. trilobata was found to be the potential inhibitor of the Bcl-2 protein, which merits attention for further in vitro and in vivo studies before clinical trials. Full article

(This article belongs to the Special Issue Computational Studies on the Development and Characterization of Pharmaceutical Materials)

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Review

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37 pages, 6074 KiB

Open AccessReview

The Millennia-Long Development of Drugs Associated with the 80-Year-Old Artificial Intelligence Story: The Therapeutic Big Bang?

by Aurore Crouzet, Nicolas Lopez, Benjamin Riss Yaw, Yves Lepelletier and Luc Demange

Molecules 2024, 29(12), 2716; https://doi.org/10.3390/molecules29122716 - 7 Jun 2024

Viewed by 1855

Abstract

The journey of drug discovery (DD) has evolved from ancient practices to modern technology-driven approaches, with Artificial Intelligence (AI) emerging as a pivotal force in streamlining and accelerating the process. Despite the vital importance of DD, it faces challenges such as high costs [...] Read more.

The journey of drug discovery (DD) has evolved from ancient practices to modern technology-driven approaches, with Artificial Intelligence (AI) emerging as a pivotal force in streamlining and accelerating the process. Despite the vital importance of DD, it faces challenges such as high costs and lengthy timelines. This review examines the historical progression and current market of DD alongside the development and integration of AI technologies. We analyse the challenges encountered in applying AI to DD, focusing on drug design and protein–protein interactions. The discussion is enriched by presenting models that put forward the application of AI in DD. Three case studies are highlighted to demonstrate the successful application of AI in DD, including the discovery of a novel class of antibiotics and a small-molecule inhibitor that has progressed to phase II clinical trials. These cases underscore the potential of AI to identify new drug candidates and optimise the development process. The convergence of DD and AI embodies a transformative shift in the field, offering a path to overcome traditional obstacles. By leveraging AI, the future of DD promises enhanced efficiency and novel breakthroughs, heralding a new era of medical innovation even though there is still a long way to go. Full article

(This article belongs to the Special Issue Computational Studies on the Development and Characterization of Pharmaceutical Materials)

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20 pages, 2997 KiB

Open AccessReview

Applications and Potential of In Silico Approaches for Psychedelic Chemistry

by Sedat Karabulut, Harpreet Kaur and James W. Gauld

Molecules 2023, 28(16), 5966; https://doi.org/10.3390/molecules28165966 - 9 Aug 2023

Cited by 3 | Viewed by 2485

Abstract

Molecular-level investigations of the Central Nervous System have been revolutionized by the development of computational methods, computing power, and capacity advances. These techniques have enabled researchers to analyze large amounts of data from various sources, including genomics, in vivo, and in vitro drug [...] Read more.

Molecular-level investigations of the Central Nervous System have been revolutionized by the development of computational methods, computing power, and capacity advances. These techniques have enabled researchers to analyze large amounts of data from various sources, including genomics, in vivo, and in vitro drug tests. In this review, we explore how computational methods and informatics have contributed to our understanding of mental health disorders and the development of novel drugs for neurological diseases, with a special focus on the emerging field of psychedelics. In addition, the use of state-of-the-art computational methods to predict the potential of drug compounds and bioinformatic tools to integrate disparate data sources to create predictive models is also discussed. Furthermore, the challenges associated with these methods, such as the need for large datasets and the diversity of in vitro data, are explored. Overall, this review highlights the immense potential of computational methods and informatics in Central Nervous System research and underscores the need for continued development and refinement of these techniques and more inclusion of Quantitative Structure-Activity Relationships (QSARs). Full article

(This article belongs to the Special Issue Computational Studies on the Development and Characterization of Pharmaceutical Materials)

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