QSAR Techniques Research Articles

Hansch analysis by QSAR model of curcumin and eight of its transformed derivatives with antimicrobial activity against Staphylococcus aureus

Context: In the last decade, antimicrobial resistance cases have been widespread. The discovery and development of new drugs need to be done to overcome the case. Some research has found that some compounds, which are curcumin transformation derivatives, are able to inhibit the growth of Staphylococcus aureus. Aims: To evaluate the development of antimicrobial candidates of curcumin versus S. aureus. Methods: The in silico approach method, along with the QSAR technique, plays an important role in the process of discovery and development of new drugs. In this study, we focused on developing curcumin transformation derivatives that are much more potent by making the best QSAR equation of curcumin and eight curcumin transformation derivatives that have been tested in vitro for their antimicrobial activity against Staphylococcus aureus. Results: The best QSAR equation was obtained from curcumin transformation derivatives as antimicrobial activity against S. aureus, with pMIC = 0.812 (± 0.162)EHOMO +5.443 (± 1.659) (n = 9; Sig = 0.002; R = 0.884; R2 = 0.782; F = 25.153; Q2 = 0.57. Conclusions: In this study, an increase in the antimicrobial activity of curcumin transformation derivatives against S. aureus by increasing EHOMO was observed. The best QSAR equation can be a tool to obtain a more potential new chemical structure model and reduce trials and errors.

Multicellular target QSAR models for predicting of novel inhibitors against pancreatic cancer by Monte Carlo approach

Pancreatic cancer is one of the most fatal malignant tumors, with a poor prognosis. Nowadays, computational methods for designing and predicting anticancer agents have gained increased attention. In this work, a QSAR study is conducted to further investigate the activity of 5797 agents against pancreatic cancer cell lines. QSAR models are a system of classification approaches that are used to construct and validate the predictive potential of created models. This approach demonstrates the prediction in the active (1) and inactive (−1) format. Here, the classification QSAR technique is employed to construct and evaluate models. The classification QSAR model has been constructed by the Monte Carlo method based on the representation of the molecular structure by SMILES (simplified molecular input line entry systems) using the CORAL software. Four random splits of data by making four sets such as active training, passive training, calibration, and validation sets are assessed. The numerical values of sensitivity, specificity, and accuracy for the validation set of splits 1 to 4 are in the range of 0.7726–0.7901, 0.9007–0.9220, and 0.8548–0.8730 respectively. The Matthews correlation coefficient (MCC) of these models for external validation sets is found to be in the range of 0.6832 to 0.7215.

Molecular modeling studies of Indoline Scaffold derivatives as PD‐1/PD‐L1 pathway inhibitors by QSAR, molecular docking and molecular dynamics simulation techniques

AbstractIn recent years, blocking the interaction of programmed cell death receptor‐1 (PD‐1) and programmed cell death ligand‐1 (PD‐L1) has been recognized as one of the promising cancer therapies. Due to the many limitations of monoclonal antibodies, the development of small molecule inhibitors targeting PD‐1/PD‐L1 is imminent. This study aims to systematically investigate the interaction mechanisms of novel PD‐1/PD‐L1 inhibitors containing indoline backbone structures using 3D/2D‐QSAR modeling, molecular docking, molecular dynamics (MD) simulation, and ADMET prediction. The performance and predictive ability of the constructed QSAR models were evaluated by internal and external validation techniques. Topomer CoMFA and HQSAR models showed good reliability and predictive capability. By searching for R‐groups in the Topomer search module and combining the existing molecules with high activity contribution values, 24 new compounds with theoretically high activity were obtained, which showed desirable docking scores, appropriate ADMET properties, and good stability. This work not only provides a reliable QSAR model as a valuable screening tool for the development of future drugs targeting PD‐1/PD‐L1, but also makes the newly designed inhibitors expected to be potential PD‐1/PD‐L1 inhibitors that can be used for further synthesis and characterization to obtain novel anti‐cancer drugs targeting PD‐1/PD‐L1.

Identification of Antibiotic Drugs against SARS-CoV2 Mpro: A Computational Approach for Drug Repurposing

The coronavirus pandemic has posed a significant challenge for researchers seeking to develop new compounds and repurpose existing drugs to manage this disease. It has been found that the Main protease enzyme (Mpro) is critical to the replication of the virus, making it an attractive target for drug development. Different antibiotics have been proven effective against different viruses, leading to their recommendation for COVID-19. In this study, virtual screening, pharmacokinetics, QSAR, and molecular docking techniques were used to investigate the best antibiotic drugs for COVID-19 by targeting the active and inactive conformations of the Mpro enzyme. The results of the study demonstrate that Praziquantel is a promising candidate for COVID-19 treatment. This is due to several reasons: First, Praziquantel exhibits better binding energy in both the conformations of Mpro. Second, it binds in S-3A site in native conformation and S-1B in active state. Third, Praziquantel has excellent absorption properties, strong blood-brain barrier penetration power, and reasonably good solubility. Therefore, the study nominates Praziquantel as the best option for future experimental and pre-clinical investigations for COVID-19.

Identification of a 2-aminothiazole framework using classical QSAR model targeting chloroquine-sensitive Plasmodium falciparum

Chloroquine-sensitive Plasmodium falciparum is the most deadly form of human malaria. It is associated with a number of mutations in P. falciparum. Chloroquine-resistant transporter is a protein that serves as a transporter in the parasite's digesting vacuole membrane. In order to combat chloroquine-sensitive P. falciparum strains (NF54), this study employs QSAR modelling to examine possible structural alterations of 2-amino-thiazole derivatives. The traditional QSAR model was built using the PaDEL descriptor via QSARINS software. The model was found to have an internal cross-validation value of Q2loo = 0.7890 and an external validation parameter of RMSE ext = 0.6938. The predicted pIC50 values from the QSAR techniques for the case study chemicals were compared and found to be well fitted to the model and well predicted for the external set of compounds. The outcome demonstrates the value of using the suggested method in the creation of new medication candidates could fill the critical gap in scientific knowledge and open up novel possibilities for pharmaceutical development.

Development of 2-dimensional and 3-dimensional QSAR models of Indazole derivatives as TTK inhibitors having Anticancer potential

The study aimed to explore the anticancer efficacy of indazole pharmacophore by analyzing a series of 109 derivatives of indazole as Tyrosine Threonine Kinase (TTK) inhibitors through quantitative activity relationship analysis using 2D and 3D QSAR techniques. The best 2D-QSAR model was generated by the MLR method, showing a high correlation coefficient (r2) of 0.9512, and good internal (q2), and external (pred_r2) cross-validation regression coefficients of 0.8998, and 0.8661, respectively. The residual values were modest, indicating good agreement between the observed and predicted pIC50 values, which suggested that the chosen model was predictably accurate. The 3D QSAR model, built using the SWF kNN approach, displayed a high internal cross-validation regression coefficient (q2) of 0.9132. Essential structural features/considerations in developing indazole as prospective anticancer medicines have been suggested. The study provides a reliable and predictive model for the prediction of anticancer activity of indazole derivatives. The identified essential structural features/considerations may be useful for the development of prospective anticancer medicines.

Combining QSAR techniques, molecular docking, and molecular dynamics simulations to explore anti-tumor inhibitors targeting Focal Adhesion Kinase

Focal Adhesion Kinase (FAK) is an important target for tumor therapy and is closely related to tumor cell genesis and progression. In this paper, we selected 46 FAK inhibitors with anticancer activity in the pyrrolo pyrimidine backbone to establish 3D/2D-QSAR models to explore the relationship between inhibitory activity and molecular structure. We have established two ideal models, namely, the Topomer CoMFA model (= 0.715, = 0.984) and the Holographic Quantitative Structure-Activity Relationship (HQSAR) model (= 0.707, = 0.899). Both models demonstrate excellent external prediction capabilities.Based on the QSAR results, we designed 20 structurally modified novel compounds, which were subjected to molecular docking and molecular dynamics studies, and the results showed that the new compounds formed many robust interactions with residues within the active pocket and could maintain stable binding to the receptor proteins. This study not only provides a powerful screening tool for designing novel FAK inhibitors, but also presents a series of novel FAK inhibitors with high micromolar activity that can be used for further characterization. It provides a reference for addressing the shortcomings of drug metabolism and drug resistance of traditional FAK inhibitors, as well as the development of novel clinically applicable FAK inhibitors. Communicated by Ramaswamy H. Sarma

A fragment-based exploration of diverse MMP-9 inhibitors through classification-dependent structural assessment

Matrix metalloproteinases (MMPs) are belonging to the Zn2+-dependent metalloenzymes. These can degenerate the extracellular matrix (ECM) that is entailed with various biological processes. Among the MMP family members, MMP-9 is associated with several pathophysiological circumstances. Apart from wound healing, remodeling of bone, inflammatory mechanisms, and rheumatoid arthritis, MMP-9 has also significant roles in tumor invasion and metastasis. Therefore, MMP-9 has been in the spotlight of anticancer drug discovery programs for more than a decade. In this present study, classification-based QSAR techniques along with fragment-based data mining have been carried out on divergent MMP-9 inhibitors to point out the important structural attributes. This current study may be able to elucidate the importance of several pivotal molecular fragments such as sulfonamide, hydroxamate, i-butyl, and ethoxy functions for imparting potential MMP-9 inhibition. These observations are in correlation with the ligand-bound co-crystal structures of MMP-9. Therefore, these findings are beneficial for the design and discovery of effective MMP-9 inhibitors in the future.

QSAR study of benzylidene hydrazine benzamides derivatives with in vitro anticancer activity against human lung cancer cell line A459

Context: In the last decade, resistance to epidermal growth factor receptor-tyrosine kinase inhibitors (EGFR-TKIs) in lung cancer cases has been widespread. The discovery and development of new drugs need to be done to overcome the case. Aims: To develop lung anticancer candidates with benzylidene hydrazine benzamides derivatives that can inhibit the growth of human lung cancer cell line A459. Methods: The in silico approach method, along with the QSAR technique, plays an important role in the process of discovery and development of new drugs. In this study, we focused on developing benzylidene hydrazine benzamides derivatives that are much more potent by making the best QSAR equation of 11 benzylidene hydrazine benzamides that have been tested in vitro for its anticancer activity against human lung cancer cell line A459. Results: The best QSAR equation was obtained from benzylidene hydrazine benzamides derivatives as anticancer activity against human lung cancer cell line A459, with PIC50 = 0.738 (± 0.217) Log S - 0.031 (± 0.007) rerank + 0.017 (± 0.016) MR -1.359 ± (1.381) (n = 11; Sig = 0.003; r = 0.921; R2 = 0.849; F= 13.096; Q2 = 0.61). Conclusions: The best QSAR equation can be a tool to obtain a new chemical structure model with more potential and reduce trials and errors.

Employing comparative QSAR techniques for the recognition of dibenzofuran and dibenzothiophene derivatives toward MMP-12 inhibition

Among various matrix metalloproteinases (MMPs), MMP-12 is one of the potential targets for cancer and other diseases. However, none of the MMP-12 inhibitors has passed the clinical trials to date. Therefore, designing potential MMP-12 inhibitors as new drug molecules can provide effective therapeutic strategies for several diseases. In this study, a series of dibenzofuran and dibenzothiophene derivatives were subjected to different 2D and 3D-QSAR techniques to point out the crucial structural contributions highly influential toward the MMP-12 inhibitory activity. These techniques identified some structural attributes of these compounds that are responsible for influencing their MMP-12 inhibition. The carboxylic group may enhance proper binding with catalytic Zn2+ ion at the MMP-12 active site. Again, the i-propyl sulfonamido carboxylic acid function contributed positively toward MMP-12 inhibition. Moreover, the dibenzofuran moiety conferred stable binding at the S1′ pocket for higher MMP-12 inhibition. The steric and hydrophobic groups were found favourable near the furan ring substituted at the dibenzofuran moiety. Besides these ligand-based approaches, molecular docking and molecular dynamic (MD) simulation studies not only elucidated the importance of several aspects of these MMP-12 inhibitors while disclosing the significance of the finding of these QSAR studies and their influences toward MMP-12 inhibition. The MD simulation study also revealed stable and compact binding between such compounds at the MMP-12 active site. Therefore, the findings of these validated ligand-based and structure-based molecular modeling studies can aid the development of selective and potent lead molecules that can be used for the treatment of MMP-12-associated diseases. Communicated by Ramaswamy H. Sarma

The anti-SARS-CoV-2 activity of novel 9, 10-dihydrophenanthrene derivatives: an insight into molecular docking, ADMET analysis, and molecular dynamics simulation

Originating in Wuhan, the COVID-19 pandemic wave has had a profound impact on the global healthcare system. In this study, we used a 2D QSAR technique, ADMET analysis, molecular docking, and dynamic simulations to sort and evaluate the performance of thirty-nine bioactive analogues of 9,10-dihydrophenanthrene. The primary goal of the study is to use computational approaches to create a greater variety of structural references for the creation of more potent SARS-CoV-2 3Clpro inhibitors. This strategy is to speed up the process of finding active chemicals. Molecular descriptors were calculated using ‘PaDEL’ and ‘ChemDes’ software, and then redundant and non-significant descriptors were eliminated by a module in ‘QSARINS ver. 2.2.2’. Subsequently, two statistically robust QSAR models were developed by applying multiple linear regression (MLR) methods. The correlation coefficients obtained by the two models are 0.89 and 0.82, respectively. These models were then subjected to internal and external validation tests, Y-randomization, and applicability domain analysis. The best model developed is applied to designate new molecules with good inhibitory activity values against severe acute respiratory syndrome coronavirus 2 (SARS CoV-2). We also examined various pharmacokinetic properties using ADMET analysis. Then, through molecular docking simulations, we used the crystal structure of the main protease of SARS-CoV-2 (3CLpro/Mpro) in a complex with the covalent inhibitor "Narlaprevir" (PDB ID: 7JYC). We also supported our molecular docking predictions with an extended molecular dynamics simulation of a docked ligand-protein complex. We hope that the results obtained in this study can be used as good anti-SARS-CoV-2 inhibitors.

2D & 3D-QSAR Studies on a Series of Quinoline-Amino-piperidine Derivatives as Potent Mycobacterium DNA-Gyrase-B Inhibitors

Introduction: Mycobacterium tuberculosis is a familiar infectious bacillus that causes tuberculosis, which primarily affects the lungs and the spinal cord. To combat the growing difficulties in treating MTB, it is necessary to create safe medications with novel mechanisms of action.
 Objective: To design and develop some novel quinolone-amino piperidine derivatives with potent mycobacterium DNAgyraseB inhibitory using the QSAR technique.
 Methods: Multiple linear regression (MLR), partial least squares (PLS), and k-nearest neighbour molecular field analysis ((kNN-MFA) were utilised in the development of 2D and 3D-QSAR models, respectively; these models were then validated. 
 Results: The recently developed 2D-QSAR model can explain 85.07% (r2 = 0.8507) of the total variance incorporated into the training set. In addition, the model has an internal prediction capacity (q2) of 77.65% and an external prediction capacity (pred r2) of 83.64%, respectively. The F test confirms that the likelihood of the model failing is extremely low. The 3D-QSAR model explains the values of k (2), q2 = 0.5707, pred r2 = 0.7843, q2 se = 0.3167, and pred r2 se = 0.3111. This demonstrates that the QSAR equation obtained in that way is statistically significant and that the model has a predictive capacity of 78.43%.
 Conclusion: The robustness of the developed 2D or 3D-QSAR models provides the necessary information and is expected to provide an excellent option for drug design.

Design, synthesis and biological evaluation of novel thiosemicarbazones as cruzipain inhibitors

Based on the activity of 23 TSCs on CZ taken from the literature, we have developed a QSAR model for predicting the activity of TSCs. New TSCs were designed and then tested against CZP, resulting in inhibitors with IC50 values in the nanomolar range. The modelling of the corresponding TSC-CZ complexes by molecular docking and QM/QM ONIOM refinement indicates a binding mode compatible with what was expected for active TSCs, according to a geometry-based theoretical model previously developed by our research group. Kinetic experiments on CZP suggest that the new TSCs act by a mechanism that involves the formation of a reversible covalent adduct with slow association and dissociation kinetics. These results demonstrate the strong inhibitory effect of the new TSCs and the benefit of the combined use of QSAR and molecular modelling techniques in the design of new and potent CZ/CZP inhibitors.

Exploration of the flavonoid content of Ziziphus spina-christi leaf extract and antioxidant activity assay through in vitro and in silico methods

One plant that has the potential as an antioxidant is Ziziphus spina-christi (ZSC) because it contains phenolics and flavonoids. This study aims to determine the flavonoid content both qualitatively and quantitatively and to test the antioxidant activity of ZSC leaf extract using in vitro and in silico attenuation methods. Determination of the total flavonoid content of ZSC leaf extract using a comparison of quercetin. In vitro the antioxidant activity assay of ZSC leaf extract was carried out by measuring the reducing activity of ZSC leaf extract against the radical DPPH using ascorbic acid as comparison, while the in silico method using QSAR and pharmacophore modeling techniques. The results showed that the total flavonoid content obtained from ZSC leaf extract was 0.2515 ± 0.0013 mg QE/g D.W with an IC 50 of 58.9296 ppm. This value indicates that ZSC leaf extract has potential as a strong antioxidant. Furthermore, from the in silico method using pharmacophore modeling and QSAR techniques, 8 hit compounds were obtained from the content of ZSC with IC 50 QSAR ranging from 6.57 to 0.0004, which was thought to be the metabolite that had the most role in its antioxidant activity. This value indicates that ZSC leaf extract has potential as a very strong antioxidant. It also proves that QSAR and pharmacophore modeling techniques can be used as confirmatory tests for in vitro results in determining the antioxidant activity of natural materials.

Fragment-based investigation of thiourea derivatives as VEGFR-2 inhibitors: a cross-validated approach of ligand-based and structure-based molecular modeling studies

Angiogenesis is mediated by the vascular endothelial growth factor (VEGF) that plays a key role in the modulation of progression, invasion and metastasis, related to solid tumors and hematological malignancies. Several small-molecule VEGFR-2 inhibitors are marketed, but their usage is restricted to specific cancers due to severe toxicities. Therefore, cost-effective novel small molecule VEGFR-2 inhibitors may be an alternative to overcome these adverse effects. Here, a set of thiourea-based VEGFR-2 inhibitors were considered for a combined fragment-based QSAR technique, structure-based molecular docking followed by molecular dynamics simulation studies to acquire insights into the key structural attributes and the binding pattern of enzyme-ligand interactions. Noticeably, amine-substituted quinazoline phenyl ring and a higher number of nitrogen atoms, and the hydrazide function in the molecular structure are crucial for VEGFR-2 inhibition whereas methoxy groups are detrimental to VEGFR-2 inhibition. The MD simulation study of sorafenib and thiourea derivatives explored the significance of urea and thiourea moiety binding at VEGFR-2 active site that can be utilized further in the future to design molecules for greater binding stability and better VEGFR-2 selectivity. Therefore, such findings can be beneficial for the development of newer VEGFR-2 inhibitors for further refinement to acquire better therapeutic efficacy. Communicated by Ramaswamy H. Sarma

Discovery of novel BRD4-BD2 inhibitors via in silico approaches: QSAR techniques, molecular docking, and molecular dynamics simulations.

Bromodomain-containing protein 4(BRD4) plays an important role in the occurrence and development of various malignant tumors, which has attracted the attention of scientific research institutions and pharmaceutical companies. The structural modification of most currently available BRD4 inhibitors is relatively simple, but the drug effectiveness is limited. Research has found that the inhibition of BD1 may promote the differentiation of oligodendrocyte progenitor cell; however, the inhibition of BD2 will not cause this outcome. Therefore, newly potential drugs which target BRD4-BD2 need further research. Herein, we initially built QSAR models out of 49 compounds using HQSAR, CoMFA, CoMSIA, and Topomer CoMFA technology. All of the models have shown suitable reliabilities (q2 = 0.778, 0.533, 0.640, 0.702, respectively) and predictive abilities (r2pred = 0.716, 0.6289, 0.6153, 0.7968, respectively) for BRD4-BD2 inhibitors. On the basis of QSAR results and the search of the R-group in the topomer search module, we designed 20 new compounds with high activity that showed appropriate docking score and suitable ADMET. Docking studies and MD simulation were carried out to reveal the amino acid residues (Asn351, Cys347, Tyr350, Pro293, and Asp299) at the active site of BRD4-BD2. Free energy calculations and free energy landscapes verified the stable binding results and indicated stable conformations of the complexes. These theoretical studies provide guidance and theoretical basis for designing and developing novel BRD4-BD2 inhibitors.

Pharmacophore Modeling and 3D QSAR Analysis of Pyrazole3-Carbohydrazone Derivatives as Dipeptidyl Peptidase IV Inhibitors for Type II Anti-Diabetic Therapy

This study delves into the design of promising Type II anti-diabetic agents acting as inhibitors of Dipeptidyl Peptidase-IV (DPP-IV). Given the significance of Type 2 Diabetes Mellitus (T2DM) as a prevalent metabolic disorder, the pursuit of improved therapies is essential. Leveraging 3D QSAR and pharmacophore modeling techniques, this research identifies critical structural elements pivotal to the biological efficacy of cyanopyrrolidine derivatives. The objective is to provide invaluable insights fostering the development of potent Type II anti-diabetic agents.

Synthesis, Characterization and Evaluation of Some Novel 2-Mercaptobenzothiazole Derivatives: In Silico and Experimental Approach

Recently, QSAR technique and molecular docking have become widespread in designing new series of compounds, depending upon the different models developed, before actually synthesizing and testing them. Due to the diverse activity profile of substituted Benzothiazole, it has become a pharmacophore of interest. The main motive of the study was to design some novel 2- Mercaptobenzothiazole molecules with potential anti-inflammatory effect. Thus, in order to get a better insight into the structural features of 2- Mercaptobenzothiazole, a series of analogues were taken from the literature and 2D, 3D QSAR models were developed and molecular docking and ADME studies were also performed. For 2D QSAR study, stepwise forwarding variable selection method, coupled with Partial Least Square (SWF-PLS) regression-based method, was used to generate the best 2D model giving r2= 0.9425, q2=0.9018, F-value=90.0118, pred_r2=0.8976. In contrast, the forward stepwise variable selection k-nearest neighbour molecular field analysis approach was used to generate best 3D QSAR model (q2=0.8171 and pred_r2=0.7006). Docking studies were performed on 29 derivatives of 2-Mercaptobenzothiazoles. The ligands were prepared and docked against protein S. aureus TYrRS with a resolution of 3.2Å. The three derivatives K6, K18 and K20 were found to have the best docking scores (-48.44, -47.46, -48.52). Further, synthesis of these three derivatives was carried out. Spectral characterization was carried out by IR, 1H NMR and 13C NMR and all the structures were found to be in agreement with the data obtained. In vitro anti-inflammatory evaluation of these compounds was carried out by following the protein denaturation method and HRBC membrane stabilization method. Compound K20 displayed potential anti-inflammatory activity, which may be attributed to the electronegative group on the phenyl ring substituted on the pyrazole part of the pharmacophore.

A potent estrogen receptor and microtubule specific purine-benzothiazole-based fluorescent molecular probe induces apoptotic death of breast cancer cells

Breast cancer is the most common malignancy in women and is a heterogeneous disease at molecular level. Early detection and specificity are the key prerequisite for the treatment of this deadly cancer. To address these issues attention on the breast cancer specific receptor protein(s) is the most realistic option. Herein estrogen (E) and progesterone (Pg) receptors(R) were considered to design fluorescent molecular probes with possible therapeutic option. We adopted QSAR technique to design a library of benzothiazole-purine hybrid molecules. Molecular docking offers us three screened molecules as most potential. Among these molecules one abbreviated as “CPIB” showed blue fluorescence and detected ER positive cancer cells at 1 nM concentration. At elevated concentration, CPIB induces apoptotic deaths of same cancer cells through targeting intracellular microtubules without affecting normal cells or ER negative cells. CPIB is one of its kind with two-in-one potential of “Detection and Destroy” ability targeting ER positive breast cancer cells.

An Exploration of the Antiproliferative Potential of Chalcones and Dihydropyrazole Derivatives in Prostate Cancer via Androgen Receptor: Combined QSAR, Machine Learning, and Molecular Docking Techniques

In this study, the antiproliferative activities of some chalcones and dihydro pyrazole derivatives in prostate cancer were investigated via the androgen receptor using the QSAR, machine learning, molecular docking techniques. A total of 30 dichloro substituted chalcones and dihydro pyrazole derivatives were collected from literature and optimized using Density Functional Theory. Genetic Function Approximation was employed for the model development. The model generated was thoroughly validated. Its generalizations and predictive capacities were improved with the Extreme Learning Machine algorithm. Molecular docking and drug-like screening of the compounds were carefully performed. A reduction in the negative coefficient of descriptors and an increase in the positive coefficient of the descriptors favor good bioactivities. An R2 pred value of 0.737 shows a high correlation between the experimental activities and the predicted activities. A correlation coefficient R2, 0.8305 authenticates the predictability of the model. The ELM-Sine model showed an improvement of 66.7% and 8.3% over the QSAR and ELM-Sig models respectively. Molecular docking validated the chalcones and dihydropyrazole derivatives as promising anti-prostate cancer compounds, with pi-pi stacking and hydrogen bond interactions favoring their inhibition of the androgen receptor. The leads are drug-like and novel anti-prostate cancer compounds.