Ligand-based Pharmacophore Model Research Articles

Exploring a repurposed candidate with dual hIDO1/hTDO2 inhibitory potential for anticancer efficacy identified through pharmacophore-based virtual screening and in vitro evaluation

Discovering effective anti-cancer agents poses a formidable challenge given the limited efficacy of current therapeutic modalities against various cancer types due to intrinsic resistance mechanisms. Cancer immunochemotherapy is an alternative strategy for breast cancer treatment and overcoming cancer resistance. Human Indoleamine 2,3-dioxygenase (hIDO1) and human Tryptophan 2,3-dioxygenase 2 (hTDO2) play pivotal roles in tryptophan metabolism, leading to the generation of kynurenine and other bioactive metabolites. This process facilitates the de novo synthesis of Nicotinamide Dinucleotide (NAD), promoting cancer resistance. This study identified a new dual hIDO1/hTDO2 inhibitor using a drug repurposing strategy of FDA-approved drugs. Herein, we delineate the development of a ligand-based pharmacophore model based on a training set of 12 compounds with reported hIDO1/hTDO2 inhibitory activity. We conducted a pharmacophore search followed by high-throughput virtual screening of 2568 FDA-approved drugs against both enzymes, resulting in ten hits, four of them with high potential of dual inhibitory activity. For further in silico and in vitro biological investigation, the anti-hypercholesterolemic drug Pitavastatin deemed the drug of choice in this study. Molecular dynamics (MD) simulations demonstrated that Pitavastatin forms stable complexes with both hIDO1 and hTDO2 receptors, providing a structural basis for its potential therapeutic efficacy. At nanomolar (nM) concentration, it exhibited remarkable in vitro enzyme inhibitory activity against both examined enzymes. Additionally, Pitavastatin demonstrated potent cytotoxic activity against BT-549, MCF-7, and HepG2 cell lines (IC50 = 16.82, 9.52, and 1.84 µM, respectively). Its anticancer activity was primarily due to the induction of G1/S phase arrest as discovered through cell cycle analysis of HepG2 cancer cells. Ultimately, treating HepG2 cancer cells with Pitavastatin affected significant activation of caspase-3 accompanied by down-regulation of cellular apoptotic biomarkers such as IDO, TDO, STAT3, P21, P27, IL-6, and AhR.

Expanding the Chemical Space of Transforming Growth Factor-β (TGFβ) Receptor Type II Degraders with 3,4-Disubstituted Indole Derivatives.

The TGFβ type II receptor (TβRII) is a central player in all TGFβ signaling downstream events, has been linked to cancer progression, and thus, has emerged as an auspicious anti-TGFβ strategy. Especially its targeted degradation presents an excellent goal for effective TGFβ pathway inhibition. Here, cellular structure-activity relationship (SAR) data from the TβRII degrader chemotype 1 was successfully transformed into predictive ligand-based pharmacophore models that allowed scaffold hopping. Two distinct 3,4-disubstituted indoles were identified from virtual screening: tetrahydro-4-oxo-indole 2 and indole-3-acetate 3. Design, synthesis, and screening of focused amide libraries confirmed 2r and 3n as potent TGFβ inhibitors. They were validated to fully recapitulate the ability of 1 to selectively degrade TβRII, without affecting TβRI. Consequently, 2r and 3n efficiently blocked endothelial-to-mesenchymal transition and cell migration in different cancer cell lines while not perturbing the microtubule network. Hence, 2 and 3 present novel TβRII degrader chemotypes that will (1) aid target deconvolution efforts and (2) accelerate proof-of-concept studies for small-molecule-driven TβRII degradation in vivo.

Ligand-based pharmacophore modeling and machine learning for the discovery of potent aurora A kinase inhibitory leads of novel chemotypes.

Aurora-A (AURKA) is serine/threonine protein kinase involved in the regulation of numerous processes of cell division. Numerous studies have demonstrated strong association between AURKA and cancer. AURKA is overexpressed in many cancers, such as colon, breast and prostate cancers. Consequently, AURKA has emerged as promising target for therapeutic intervention in cancer management. Herein, we describe a computational workflow for the discovery of novel anti-AURKA inhibitory leads starting with ligand-based assessment of the pharmacophoric space of six diverse sets of inhibitors. Subsequently, machine learning/QSAR modeling was coupled with genetic function algorithm to search for the best possible combination of machine learner, ligand-based pharmacophore(s) and molecular descriptors capable of explaining variation in anti-AURKA bioactivities within a collected list of inhibitors. Two learners succeeded in achieving acceptable structure/activity correlations, namely, random forests and extreme gradient boosting (XGBoost). Three pharmacophores emerged in the successful ML models. These were then used as 3D search queries to mine the National Cancer Institute database for novel anti-AURKA leads. Top-ranking 38 hits were assessed in vitro for their anti-AURKA bioactivities. Among them, three compounds exhibited promising dose-response curves, demonstrating experimental IC50 values ranging from sub-micromolar to low micromolar values. Remarkably, two of these compounds are of novel chemotypes.

Novel-Type GABAB PAMs: Structure-Activity Relationship in Light of the Protein Structure.

Selecting a known HTS hit with the pyrazolo[1,5-a]pyrimidine core, our project was started from CMPPE, and its optimization was driven by a ligand-based pharmacophore model developed on the basis of published GABAB positive allosteric modulators (PAMs). Our primary goal was to improve the potency by finding new enthalpic interactions. Therefore, we included the lipophilic ligand efficiency (LLE or LipE) as an objective function in the optimization that led to a carboxylic acid derivative (34). This lead candidate offers the possibility to improve potency without drastically inflating the physicochemical properties. Although the discovery of the novel carboxyl feature was surprising, it turned out to be an important element of the GABAB PAM pharmacophore that can be perfectly explained based on the new protein structures. Rationalizing the binding mode of 34, we analyzed the intersubunit PAM binding site of GABAB receptor using the publicly available experimental structures.

Shaping the Future of Obesity Treatment: In Silico Multi-Modeling of IP6K1 Inhibitors for Obesity and Metabolic Dysfunction.

Recent research has uncovered a promising approach to addressing the growing global health concern of obesity and related disorders. The inhibition of inositol hexakisphosphate kinase 1 (IP6K1) has emerged as a potential therapeutic strategy. This study employs multiple ligand-based in silico modeling techniques to investigate the structural requirements for benzisoxazole derivatives as IP6K1 inhibitors. Firstly, we developed linear 2D Quantitative Structure-Activity Relationship (2D-QSAR) models to ensure both their mechanistic interpretability and predictive accuracy. Then, ligand-based pharmacophore modeling was performed to identify the essential features responsible for the compounds' high activity. To gain insights into the 3D requirements for enhanced potency against the IP6K1 enzyme, we employed multiple alignment techniques to set up 3D-QSAR models. Given the absence of an available X-ray crystal structure for IP6K1, a reliable homology model for the enzyme was developed and structurally validated in order to perform structure-based analyses on the selected dataset compounds. Finally, molecular dynamic simulations, using the docked poses of these compounds, provided further insights. Our findings consistently supported the mechanistic interpretations derived from both ligand-based and structure-based analyses. This study offers valuable guidance on the design of novel IP6K1 inhibitors. Importantly, our work exclusively relies on non-commercial software packages, ensuring accessibility for reproducing the reported models.

Virtual Screening Using a Ligand-based Pharmacophore Model from Ashitaba (Angelica keiskei K.) Isolates and Molecular Docking to Obtained New Candidates as -Glucosidase Inhibitors

Virtual Screening Using a Ligand-based Pharmacophore Model from Ashitaba (Angelica keiskei K.) Isolates and Molecular Docking to Obtained New Candidates as -Glucosidase Inhibitors

Identification of Novel EGFR Inhibitors for the Targeted Therapy of Colorectal Cancer Using Pharmacophore Modelling, Docking, Molecular Dynamic Simulation and Biological Activity Prediction.

Colorectal cancer (CRC) is considered the second deadliest cancer in the world. One of the reasons for the occurrence of this cancer is the deregulation of the Epidermal Growth Factor Receptor (EGFR), which plays a critical role in regulating cell division, persistence, differentiation, and migration. The overexpression of the EGFR protein leads to its dysregulation and causes CRC. Hence, this work aims to identify and validate novel EGFR inhibitors for the treatment of colorectal cancer employing various computer aided techniques such as pharmacophore modeling, docking, molecular dynamic simulation and Quantitative Structure-Activity Relationship (QSAR) analysis. In this work, a shared-featured ligand-based pharmacophore model was generated using the known inhibitors of EGFR. The best model was validated and screened against ZincPharmer and Maybridge databases, and 143 hits were obtained. Pharmacokinetic and toxicological properties of these hits were studied, and the acceptable ligands were docked against EGFR. The best five protein-ligand complexes with binding energy less than -5 kcal/mol were selected. The molecular dynamic simulation studies of these complexes were conducted for 100 nanoseconds (ns), and the results were analyzed. The biological activity of this ligand was calculated using QSAR analysis. The best complex with Root Mean Square Deviation (RMSD) 3.429 Å and Radius of Gyration (RoG) 20.181 Å was selected. The Root Mean Square Fluctuations (RMSF) results were also found to be satisfactory. The biological activity of this ligand was found to be 1.38 μM. This work hereby proposes the ligand 2-((1,6-dimethyl-4-oxo-1,4-dihydropyridin-3-yl)oxy)-N- (1H-indol-4-yl)acetamide as a potential EGFR inhibitor for the treatment of colorectal cancer. The wet lab analysis must be conducted, however, to confirm this hypothesis.

Lead Identification Through In Silico Studies: Targeting Acetylcholinesterase Enzyme Against Alzheimer's Disease.

In this work, we aimed to acquire the best potential small molecule for Alzheimer's disease (AD) treatment using different models in Biovia Discovery Studio to identify new potential inhibitors of acetylcholinesterase (AChE) via in silico studies. The prevalence of cognitive impairment-related neurodegenerative disorders, such as AD, has been observed to escalate rapidly. However, we still know little about the underlying functions, outcome predictors, or intervention targets causing AD. The objective of the study was to optimize and identify the lead compound to target AChE against Alzheimer's disease. Different in silico studies were employed, including the pharmacophore model, virtual screening, molecular docking, de novo evolution model, and molecular dynamics. The pharmacophoric features of AChE inhibitors were determined by ligand-based pharmacophore models and 3D QSAR pharmacophore generation. Further validation of the best pharmacophore model was done using the cost analysis method, Fischer's randomization method, and test set. The molecules that harmonized the best pharmacophore model with the estimated activity < 1 nM and ADMET parameters were filtered, and 12 molecules were subjected to molecular docking studies to obtain binding energy. 3vsp_EK8_1 secured the highest binding energy of 65.60 kcal/mol. Further optimization led to a 3v_Evo_4 molecule with a better binding energy of 70.17 kcal/mol. The molecule 3v_evo_4 was subjected to 100 ns molecular simulation compared to donepezil, which showed better stability at the binding site. A lead compound, 3v_Evo_4 molecule, was identified to inhibit AChE, and it could be further studied to develop as a drug with better efficacy than the existing available drugs for treating AD.

New triazole based oxadiazolo/thiadiazolo–phthalazines as potent antimycobacterial agents: Design, synthesis, molecular modelling and in silico ADMET profiles

AbstractThe study of the chemical and biological properties of molecules simultaneously comprising various heterocycles, such as fused 1,3,4–oxadiazolyl–1,2,3–triazoles and 1,2,4–triazolyl–1,3,4–thiadiazole have been conducted as part of our ongoing research in the field of medicinal and organic chemistry. In this study, we performed ligand–based pharmacophore modelling as a promising design strategy of substituted phthalazinone derivatives (5 and 7). These were synthesized from key intermediate mercapto‐containing oxadiazo‐phthalazinone derivative 2. The synthesized compounds were characterized by IR, 1H and 13C NMR, elemental analysis and mass spectrometric techniques. In preliminary antibacterial studies, analogues 5 d, 5 f, 7 a and 7 d were found to be most effective against S. epidermidis, whereas 5 d displayed excellent activity against P. aeruginosa microorganism. Later, explored the probable key active site and inhibitors of Cytochrome P450 CYP121A1 interactions from M. tuberculosis H37Rv based antitubercular drug candidate with 5O4K protein and ligand 7 b possesses highest hydrophobic amino acid residues. The physicochemical and medicinal properties were also evaluated using SwissADME and ADMETlab2.0 web to function as effective oral bioavailability medications.

Optimization of virtual screening against phosphoinositide 3-kinase delta: Integration of common feature pharmacophore and multicomplex-based molecular docking

Extensive research has accumulated which suggests that phosphatidylinositol 3-kinase delta (PI3Kδ) is closely related to the occurrence and development of various human diseases, making PI3Kδ a highly promising drug target. However, PI3Kδ exhibits high homology with other members of the PI3K family, which poses significant challenges to the development of PI3Kδ inhibitors. Therefore, in the present study, a hybrid virtual screening (VS) approach based on a ligand-based pharmacophore model and multicomplex-based molecular docking was developed to find novel PI3Kδ inhibitors. 13 crystal structures of the human PI3Kδ-inhibitor complex were collected to establish models. The inhibitors were extracted from the crystal structures to generate the common feature pharmacophore. The crystallographic protein structures were used to construct a naïve Bayesian classification model that integrates molecular docking based on multiple PI3Kδ conformations. Subsequently, three VS protocols involving sequential or parallel molecular docking and pharmacophore approaches were employed. External predictions demonstrated that the protocol combining molecular docking and pharmacophore resulted in a significant improvement in the enrichment of active PI3Kδ inhibitors. Finally, the optimal VS method was utilized for virtual screening against a large chemical database, and some potential hit compounds were identified. We hope that the developed VS strategy will provide valuable guidance for the discovery of novel PI3Kδ inhibitors.

Ligand based pharmacophore modelling and integrated computational approaches in the quest for small molecule inhibitors against hCA IX.

Carbonic anhydrase IX is an important biomarker to fight hypoxic tumours in both initial and metastatic stages of many forms of cancer. Overexpression of hCA IX in the hypoxic environment, has an active role in pH maintenance and makes the hCA IX a better target for the inhibitors targeting specific types of cancer stages. Being a member of the carbonic anhydrase family and having sixteen isoforms, it is important to have a selective inhibition of hCA IX to limit the disruption in the biological and metabolic pathways where other isoforms of hCA are localised and to avoid the other toxicity and adverse effects we try to find selective hCA IX inhibitors from a natural derivative. In the process of finding selective hCA inhibitors we developed a pharmacophore model based on existing inhibitors with IC50 values of less than 50 nm, which is then validated with the external decoy set and used for database searching followed by virtual screening to identify the hits based on the pharmacophore fit score and RMSD. Molecular docking studies were performed to identify protein ligand interaction and molecular dynamics simulation studies to analyse the stability of the complex and DFT studies were carried out. The initial screening yielded 43 hits with the RMSD value less than 1, which when subjected to docking exhibited very good interaction with key residues ZN301, HIS94, HIS96 and HIS119. The top 4 compounds in the molecular dynamics simulation studies for 100 ns provided useful insights on the stability of the complex and the DFT studies confirmed the energy variation between HOMO and LUMO is within an acceptable range. An average binding score of -7.8 Kcal mol-1 for the lead compounds and high stability margin in the dynamics study concludes that these lead compounds demonstrated outstanding potential for hCA IX inhibitory action theoretically and that further experimental studies for selective inhibition are inevitable.

Exploration of SAM-I riboswitch inhibitors: In-Silico discovery of ligands to a new target employing multistage CADD approaches

Targeting riboswitches, regulatory elements responsible for the expression of essential genes, is taking central stage in the new era of antibacterial medications discovery due to the emergence of antibiotic resistance. The S-Adenosyl methionine-I (SAM-I) riboswitch works through transcription termination in a negative feedback manner modulated by the natural ligand SAM. SAM-I riboswitch is specific to bacteria and found mainly in gram-positive bacteria such as Bacillus anthracis. Analyzing the interactions of the co-crystallized structure of SAM-I riboswitch aptamer with its native ligand SAM clarified the needed chemical structural features to achieve binding. Acknowledging those features, structure-based and ligand-based pharmacophore models were built for filtration use in screening the OTAVA Chemical library and the Pubchem database. For further filtration enhancement, the physicochemical properties of SAM were used as a second filtration criterion. Compounds obtained as output from previous steps were energy minimized, and the lowest energy conformer structures were docked to SAM-I using MOE, v.2019.01. S-score and ligand interactions were used to assess the best hits. This yielded eight promising compounds to which molecular dynamics (MD) simulations with SAM-I aptamer were applied using GROMACS 2020.3 package affirming stable binding interactions and binding energetics similar to SAM. Moreover, pharmacokinetic and drug-like properties of those eight hits were assessed using SWISS-ADME. According to the combined computational methods and PK/Tox assessment, compound 20 was the most promising and thus can be considered a lead for future evaluation and optimization as a candidate new antibacterial agent targeting a new biomolecule eliciting a new mechanism of action.

Discovery of Potential Inhibitors for SFRP3: Ligand-Based 3D Pharmacophore, Virtual Screening, Molecular Docking, and Dynamics Studies

Cancer is a complicated and diverse family of diseases characterized by the body’s aberrant cells growing and spreading out of control. It is one of the major causes of mortality in the world and may affect almost every organ or tissue in the body. One of the most prevalent forms of malignant tumors that affect both men and women is bone cancer. The study introduces the soluble frizzled-related protein SFRP3 as a significant participant. It functions as a tumor suppressor and regulator as well as an antagonist of the Wnt signaling pathway. It interacts directly with Wnts, regulating Wnt signaling and directing cell proliferation and differentiation in certain cell types. The study focuses on SFRP3 as a possible target for bone cancer therapy. The purpose is to find prospective medication candidates. Several databases are listed as possible drug compound sources, including NCI_Natural Products, Enamine, and specifications. Potent drugs from the selected databases are scrutinized and identified using ligand-based pharmacophore modeling. The binding affinity and interactions between the selected compounds and the SFRP3-modeled protein are evaluated using virtual screening and molecular docking approaches. The selection of lead compounds is based on their scores, binding affinity, and interactions, which indicate their potential as therapeutic candidates. To ensure that the contacts stay stable throughout time, molecular dynamics modeling is used to analyze the stability of the Protein-Ligand complex. The investigation leads to the identification of the top three compounds from each of the three databases that show potential against the target protein, SFRP3.

Ligand-based pharmacophore modelling, structure optimisation, and biological evaluation for the identification of 2-heteroarylthio-N-arylacetamides as novel HSP90 C-terminal inhibitors

Targeting Heat shock protein 90 (HSP90) C-terminus is an important strategy to develop HSP90 inhibitors without inducing heat shock response. The development of C-terminal inhibitors, however, is hampered by a lack of understanding regarding the interaction between the HSP90 C-terminus and the present inhibitors. We collected seven classical and structurally diverse HSP90 C-terminal inhibitors and constructed a ligand-based pharmacophore model. The subsequent virtual screening and structural optimisation led to the identification of 2-heteroarylthio-N-arylacetamides as novel HSP90 C-terminal inhibitors. 9 and 27 exhibited strong antitumour activity in vitro by inhibiting proliferation and inducing apoptosis in multiple cancer cell lines. These compounds disrupted the interaction between HSP90 C-terminus and peptidylprolyl isomerase D, exerting a stronger inhibitory effect than novobiocin. 27 significantly induced the degradation of HSP90 clients without triggering heat shock response. In an in vivo study using 4T1 mice breast cancer models, 9 showed a potent antitumour effect without obvious toxicity.

Identification of Dual-Target Inhibitors for Epidermal Growth Factor Receptor and AKT: Virtual Screening Based on Structure and Molecular Dynamics Study.

Epidermal growth factor EGFR is an important target for non-small cell lung (NSCL) cancer, and inhibitors of the AKT protein have been used in many cancer treatments, including those for NSCL cancer. Therefore, searching small molecular inhibitors which can target both EGFR and AKT may help cancer treatment. In this study, we applied a ligand-based pharmacophore model, molecular docking, and MD simulation methods to search for potential inhibitors of EGFR and then studied dual-target inhibitors of EGFR and AKT by screening the immune-oncology Chinese medicine (TCMIO) database and the human endogenous database (HMDB). It was found that TCMIO89212, TCMIO90156, and TCMIO98874 had large binding free energies with EGFR and AKT, and HMDB0012243 also has the ability to bind to EGFR and AKT. These results may provide valuable information for further experimental study.

Virtual screening approach for the discovery of selective 5α-reductase type II inhibitors for benign prostatic hyperplasia treatment.

Background: 5α-Reductase type II (5αR2) inhibition is a promising strategy for benign prostatic hyperplasia treatment. A computational approach including virtual screening, ligand-based 3Dpharmacophore modeling, 2D quantitative structure-activity relationship and molecular docking simulations were adopted to develop novel inhibitors. Results: Hits were first filtered via the validated pharmacophore and 2D quantitative structure-activity relationship models. Docking on the recently determined cocrystallized structure of 5αR2 showed three promising hits. Visual inspection results were compared with finasteride ligand and dihydrotestosterone as reference, to explain the role of binding to Glu57 and Tyr91 for 5αR2 selective inhibition. Conclusion: Alignment between Hit 2 and finasteride in the binding pocket showed similar binding modes. The biological activity prediction showed antitumor and androgen targeting activity of the new hits.

Theoretical investigation on known renin inhibitors and generation of ligand-based pharmacophore models for hypertension treatment

The renin enzyme is considered a promising target for hypertension and renal diseases. Over the last three decades, several experimental and theoretical studies have been engaged in the discovery of potent renin inhibitors. The identified inhibitors that undergo clinical trials are still failing to meet the criteria of potency and safety. To date, there is no specific FDA-approved drug for renin inhibition. Our theoretical opinion describes that the most potent compounds identified in experimental studies but lacking safety and overdose issues could be solved by finding similar molecules that are stable, very active, and have no side effects, which will kick start the drug discovery process. Here, we utilized the most potent direct renin inhibitors reported earlier, followed further by our theoretical study reported in 2019. Ligand-based virtual screening, density functional theory, and dynamic simulation studies were employed to explore the identified compounds and co-crystallized molecule in the protein structure. From the diverse databases, we have identified several identical molecules based on their structural features, such as functional groups like hydrophobic (H), aromatic rings (R), hydrogen bond acceptor (A), and donor (D). The HHHPR five-point pharmacophore feature was identified as a template pharmacophore to search the potential compounds from the Enamine and LifeChemical databases and have a good fitness score with known renin inhibitors. Furthermore, theoretical validation was done through several studies that confirmed the activity of the identified molecules. Overall, we propose that these compounds might break the failure in adverse events and improve the potency of hypertension treatment. Communicated by Ramaswamy H. Sarma

Identification of novel small molecule inhibitors for solute carrier SGLT1; a computational exploration

Diabetes results in substantial disabilities, diminished quality of life, and mortality that imposes a huge economic burden on societies and governments worldwide. Despite the absence of specific oral therapies at present, there exists an urgent requirement to develop a novel drug for the treatment of diabetes mellitus. The membrane protein sodium glucose co-transporters (SGLT1) present a captivating therapeutic target for diabetes, given its pivotal role in facilitating glucose absorption in the small intestine, offering immense promise for potential therapeutic intervention. In this connection, the present study is aimed at identifying potential inhibitors of SGLT1 from a small molecule database, including compounds from both natural as well as synthetic origins. A comprehensive approach was employed, by integrating homology modeling, ligand-based pharmacophore modeling, virtual screening, and molecular docking simulation. The process resulted in the identification of 16 new compounds, featuring similar attributes as observed for the documented actives. In a systematic screening procedure, five potential virtual hits were selected for simulation studies followed by subsequent binding free energy calculations, providing deeper insight into the time-dependent behavior of protein-ligand complexes in a dynamic state. In conclusion, our findings demonstrated that the identified compounds, particularly compounds 81 and 91, exhibit enhanced stability and favorable binding affinities with the target protein, marking them promising candidates for further investigations. Communicated by Ramaswamy H. Sarma

Identification of novel dual acting ligands targeting the adenosine A2A and serotonin 5-HT1A receptors

GPCRs are a family of transmembrane receptors that are profoundly linked to various neurological disorders, among which is Parkinson’s disease (PD). PD is the second most ubiquitous neurological disorder after Alzheimer’s disease, characterized by the depletion of dopamine in the central nervous system due to the impairment of dopaminergic neurons, leading to involuntary movements or dyskinesia. The current standard of care for PD is Levodopa, a dopamine precursor, yet the chronic use of this agent can exacerbate motor symptoms. Recent studies have investigated the effects of combining A2AR antagonist and 5-HT1A agonist on dyskinesia and motor complications in animal models of PD. It has been proved that the drug combination has significantly improved involuntary movements while maintaining motor activity, highlighting as a result new lines of therapy for PD treatments, through the regulation of both receptors. Using a combination of ligand-based pharmacophore modelling, virtual screening, and molecular dynamics simulation, this study intends on identifying potential dual-target compounds from IBScreen. Results showed that the selected models displayed good enrichment metrics with a near perfect receiver operator characteristic (ROC) and Area under the accumulation curve (AUAC) values, signifying that the models are both specific and sensitive. Molecular docking and ADMET analysis revealed that STOCK2N-00171 could be potentially active against A2AR and 5-HT1A. Post-MD analysis confirmed that the ligand exhibits a stable behavior throughout the simulation while maintaining crucial interactions. These results imply that STOCK2N-00171 can serve as a blueprint for the design of novel and effective dual-acting ligands targeting A2AR and 5-HT1A. Communicated by Ramaswamy H. Sarma

Identification of novel curcumin derivatives against pancreatic cancer: a comprehensive approach integrating 3D-QSAR pharmacophore modeling, virtual screening, and molecular dynamics simulations

Pancreatic cancer, known as the “silent killer,” poses a daunting challenge in cancer therapy. The dysregulation of the PI3Kα signaling pathway in pancreatic cancer has attracted considerable interest as a promising target for therapeutic intervention. In this regard, the use of curcumin derivatives as inhibitors of PI3Kα has emerged, providing a novel and promising avenue for developing effective treatments for this devastating disease. Computational approaches were employed to explore this potential and investigate 58 curcumin derivatives with cytotoxic activity against the Panc-1 cell line. Our approach involved ligand-based pharmacophore modeling and atom-based 3D-QSAR analysis. The resulting QSAR model derived from the best-fitted pharmacophore hypothesis (AAHRR_1) demonstrated remarkable performance with high correlation coefficients (R2) of 0.990 for the training set and 0.977 for the test set. The cross-validation coefficient (Q2) of 0.971 also validated the model’s predictive power. Tropsha’s recommended criteria, including the Y-randomization test, were employed to ensure its reliability. Furthermore, an enrichment study was conducted to evaluate the model’s performance in identifying active compounds. AAHRR_1 was used to screen a curated PubChem database of curcumin-related compounds. Two molecules (CID156189304 and CID154728220) exhibited promising pharmacokinetic properties and higher docking scores than Alpelisib, warranting further investigation. Extensive molecular dynamics simulations provided crucial insights into the conformational dynamics within the binding site, validating their stability and behavior. These findings contribute to our understanding of the potential therapeutic effectiveness of these compounds as PI3Kα inhibitors in pancreatic cancer. Communicated by Ramaswamy H. Sarma