Machine learning-enhanced drug testing for simultaneous morphine and methadone detection in urinary biofluids.

The simultaneous identification of drugs has considerable difficulties due to the intricate interplay of analytes and the interference present in biological matrices. In this study, we introduce an innovative electrochemical sensor that overcomes these hurdles, enabling the precise and simultaneous determination of morphine (MOR), methadone (MET), and uric acid (UA) in urine samples. The sensor harnesses the strategically adapted carbon nanotubes (CNT) modified with graphitic carbon nitride (g-C3N4) nanosheets to ensure exceptional precision and sensitivity for the targeted analytes. Through systematic optimization of pivotal parameters, we attained accurate and quantitative measurements of the analytes within intricate matrices employing the fast Fourier transform (FFT) voltammetry technique. The sensor's performance was validated using 17 training and 12 test solutions, employing the widely acclaimed machine learning method, partial least squares (PLS), for predictive modeling. The root mean square error of cross-validation (RMSECV) values for morphine, methadone, and uric acid were significantly low, measuring 0.1827 µM, 0.1951 µM, and 0.1584 µM, respectively, with corresponding root mean square error of prediction (RMSEP) values of 0.1925 µM, 0.2035 µM, and 0.1659 µM. These results showcased the robust resiliency and reliability of our predictive model. Our sensor's efficacy in real urine samples was demonstrated by the narrow range of relative standard deviation (RSD) values, ranging from 3.71 to 5.26%, and recovery percentages from 96 to 106%. This performance underscores the potential of the sensor for practical and clinical applications, offering precise measurements even in complex and variable biological matrices. The successful integration of g-C3N4-CNT nanocomposites and the robust PLS method has driven the evolution of sophisticated electrochemical sensors, initiating a transformative era in drug analysis.

Coumarin derivative-functionalized nanoporous silica as an on-off fluorescent sensor for detecting Fe3+ and Hg2+ ions: a circuit logic gate.

A highly efficient fluorescent sensor (S-DAC) was easily created by functionalizing the SBA-15 surface with N-(2-Aminoethyl)-3-Aminopropyltrimethoxysilane followed by the covalent attachment of 7-diethylamino 3-acetyl coumarin (DAC). This chemosensor (S-DAC) demonstrates selective and sensitive recognition of Fe3+ and Hg2+ in water-based solutions, with detection limits of 0.28 × 10-9 M and 0.2 × 10-9 M for Hg2+ and Fe3+, respectively. The sensor's fluorescence characteristics were examined in the presence of various metal ions, revealing a decrease in fluorescence intensity upon adding Fe3+ or Hg2+ ions at an emission wavelength of 400 nm. This sensor was also able to detect ferric and mercury ions in spinach and tuna fish. The quenching mechanism of S-DAC was investigated using UV-vis spectroscopy, which confirmed a static-type mechanism for fluorescence quenching. Moreovre, the decrease in fluorescence intensity caused by mercury and ferric ions can be reversed using trisodium citrate dihydrate and EDTA as masking agents, respectively. As a result, a circuit logic gate was designed using Hg2+, Fe3+, trisodium citrate dihydrate, and EDTA as inputs and the quenched fluorescence emission as the output.

The selective extraction of dietary polyamines from chicken breast using the application of a lab-on-a-chip electromembrane and dispersive liquid-liquid microextraction followed by gas chromatography-mass spectrometry.

Excessive dietary polyamines (PAs), including putrescine (PUT), spermine (SPM), and spermidine (SPD), have become a worldwide concern due to their carcinogenicity and reduced shelf life. A modern miniaturized on-chip electromembrane extraction (EME) has been applied to extract these compounds from chicken breast samples. This method is based fundamentally on ionic compounds' electrostatic attraction, diffusion, and solubility in the acceptor phase. The chemical structure of polyamines enables their efficient extraction using an electric driving force on a microchip device. HCl solution (0.1 mol L-1) was applied as an aqueous acceptor solvent. Dispersive liquid-liquid microextraction was performed after EME to facilitate joining three-phase EME to GC-MS and improve the merit figures. The total ranges of 3.77-7.89 µg g-1, 3.48-7.02 µg g-1, and 0.78-2.20 µg g-1 were acquired as PUT, SPM and SPD concentrations in chicken breast, respectively. The results demonstrate that the level of PAs in fresh chicken breast samples is not concerning, but it may reduce the quality of chicken meat over time. This novel analytical technique has several advantages: high recovery, substantial quickness, remarkable selectivity, and good enrichment factors. This emerging method could be generalized to other studies to analyze different foodstuffs.

CFSSynergy: Combining Feature-Based and Similarity-Based Methods for Drug Synergy Prediction.

Drug synergy prediction plays a vital role in cancer treatment. Because experimental approaches are labor-intensive and expensive, computational-based approaches get more attention. There are two types of computational methods for drug synergy prediction: feature-based and similarity-based. In feature-based methods, the main focus is to extract more discriminative features from drug pairs and cell lines to pass to the task predictor. In similarity-based methods, the similarities among all drugs and cell lines are utilized as features and fed into the task predictor. In this work, a novel approach, called CFSSynergy, that combines these two viewpoints is proposed. First, a discriminative representation is extracted for paired drugs and cell lines as input. We have utilized transformer-based architecture for drugs. For cell lines, we have created a similarity matrix between proteins using the Node2Vec algorithm. Then, the new cell line representation is computed by multiplying the protein-protein similarity matrix and the initial cell line representation. Next, we compute the similarity between unique drugs and unique cells using the learned representation for paired drugs and cell lines. Then, we compute a new representation for paired drugs and cell lines based on the similarity-based features and the learned features. Finally, these features are fed to XGBoost as a task predictor. Two well-known data sets were used to evaluate the performance of our proposed method: DrugCombDB and OncologyScreen. The CFSSynergy approach consistently outperformed existing methods in comparative evaluations. This substantiates the efficacy of our approach in capturing complex synergistic interactions between drugs and cell lines, setting it apart from conventional similarity-based or feature-based methods.

DeepCompoundNet: enhancing compound-protein interaction prediction with multimodal convolutional neural networks.

Virtual screening has emerged as a valuable computational tool for predicting compound-protein interactions, offering a cost-effective and rapid approach to identifying potential candidate drug molecules. Current machine learning-based methods rely on molecular structures and their relationship in the network. The former utilizes information such as amino acid sequences and chemical structures, while the latter leverages interaction network data, such as protein-protein interactions, drug-disease interactions, and protein-disease interactions. However, there has been limited exploration of integrating molecular information with interaction networks. This study presents DeepCompoundNet, a deep learning-based model that integrates protein features, drug properties, and diverse interaction data to predict chemical-protein interactions. DeepCompoundNet outperforms state-of-the-art methods for compound-protein interaction prediction, as demonstrated through performance evaluations. Our findings highlight the complementary nature of multiple interaction data, extending beyond amino acid sequence homology and chemical structure similarity. Moreover, our model's analysis confirms that DeepCompoundNet gets higher performance in predicting interactions between proteins and chemicals not observed in the training samples.Communicated by Ramaswamy H. Sarma.

Advancing chirality analysis through enhanced enantiomer characterization and quantification via fast Fourier transform capacitance voltammetry.

The exploration of the chiral configurations of enantiomers represents a highly intriguing realm of scientific inquiry due to the distinct roles played by each enantiomer (D and L) in chemical reactions and their practical utilities. This study introduces a pioneering analytical methodology, termed fast Fourier transform capacitance voltammetry (FFT-CPV), in conjunction with principal component analysis (PCA), for the identification and quantification of the chiral forms of tartaric acid (TA), serving as a representative model system for materials exhibiting pronounced chiral characteristics. The proposed methodology relies on the principle of chirality, wherein the capacitance signal generated by the adsorption of D-TA and L-TA onto the surface of a platinum electrode (Pt-electrode) in an acidic solution is harnessed. The capacitance voltammograms were meticulously recorded under optimized experimental conditions. To compile the final dataset for the analyte, the average of the FFT capacitance voltammograms of the acidic solution (without the presence of the analyte) was subtracted from those containing the analyte. A distinct arrangement was obtained by employing PCA as a linear data transformation method, representing D-TA and L-TA in a two/three-dimensional space. The outcomes of the study reveal the successful detection of the two chiral forms of TA with a considerable degree of precision and reproducibility. Moreover, the proposed method facilitated the establishment of two linear response ranges for the concentration values of each enantiomer, spanning from 1 to 20 µM, and 50 to 500 µM. The respective detection limits were also determined to be 0.4 µM for L-TA and 1.3 µM for D-TA. These findings underscore the satisfactory sensitivity and efficiency of the proposed method in both qualitative and quantitative assessments of the chiral forms of TA.

DeepTraSynergy: drug combinations using multimodal deep learning with transformers.

MOTIVATION: Screening bioactive compounds in cancer cell lines receive more attention. Multidisciplinary drugs or drug combinations have a more effective role in treatments and selectively inhibit the growth of cancer cells. RESULTS: Hence, we propose a new deep learning-based approach for drug combination synergy prediction called DeepTraSynergy. Our proposed approach utilizes multimodal input including drug-target interaction, protein-protein interaction, and cell-target interaction to predict drug combination synergy. To learn the feature representation of drugs, we have utilized transformers. It is worth noting that our approach is a multitask approach that predicts three outputs including the drug-target interaction, its toxic effect, and drug combination synergy. In our approach, drug combination synergy is the main task and the two other ones are the auxiliary tasks that help the approach to learn a better model. In the proposed approach three loss functions are defined: synergy loss, toxic loss, and drug-protein interaction loss. The last two loss functions are designed as auxiliary losses to help learn a better solution. DeepTraSynergy outperforms the classic and state-of-the-art models in predicting synergistic drug combinations on the two latest drug combination datasets. The DeepTraSynergy algorithm achieves accuracy values of 0.7715 and 0.8052 (an improvement over other approaches) on the DrugCombDB and Oncology-Screen datasets, respectively. Also, we evaluate the contribution of each component of DeepTraSynergy to show its effectiveness in the proposed method. The introduction of the relation between proteins (PPI networks) and drug-protein interaction significantly improves the prediction of synergistic drug combinations. AVAILABILITY AND IMPLEMENTATION: The source code and data are available at https://github.com/fatemeh-rafiei/DeepTraSynergy.

Design of ion channel blocking, toxin-like Kunitz inhibitor peptides from the tapeworm, Echinococcus granulosus, with potential anti-cancer activity.

Over-expression of K+ channels has been reported in human cancers and is associated with the poor prognosis of several malignancies. EAG1, a particular potassium ion channel, is widely expressed in the brain but poorly expressed in other normal tissues. Kunitz proteins are dominant in metazoan including the dog tapeworm, Echinococcus granulosus. Using computational analyses on one A-type potassium channel, EAG1, and in vitro cellular methods, including major cancer cell biomarkers expression, immunocytochemistry and whole-cell patch clamp, we demonstrated the anti-tumor activity of three synthetic small peptides derived from E. granulosus Kunitz4 protease inhibitors. Experiments showed induced significant apoptosis and inhibition of proliferation in both cancer cell lines via disruption in cell-cycle transition from the G0/G1 to S phase. Western blotting showed that the levels of cell cycle-related proteins including P27 and P53 were altered upon kunitz4-a and kunitz4-c treatment. Patch clamp analysis demonstrated a significant increase in spontaneous firing frequency in Purkinje neurons, and exposure to kunitz4-c was associated with an increase in the number of rebound action potentials after hyperpolarized current. This noteworthy component in nature could act as an ion channel blocker and is a potential candidate for cancer chemotherapy based on potassium channel blockage.

Gradient Heating Epitaxial Growth Gives Well Lattice-Matched Mo2 C-Mo2 N Heterointerfaces that Boost Both Electrocatalytic Hydrogen Evolution and Water Vapor Splitting.

An optimized approach to producing lattice-matched heterointerfaces for electrocatalytic hydrogen evolution has not yet been reported. Herein, we present the synthesis of lattice-matched Mo2 C-Mo2 N heterostructures using a gradient heating epitaxial growth method. The well lattice-matched heterointerface of Mo2 C-Mo2 N generates near-zero hydrogen-adsorption free energy and facilitates water dissociation in acid and alkaline media. The lattice-matched Mo2 C-Mo2 N heterostructures have low overpotentials of 73 mV and 80 mV at 10 mA cm-2 in acid and alkaline solutions, respectively, comparable to commercial Pt/C. A novel photothermal-electrocatalytic water vapor splitting device using the lattice-matched Mo2 C-Mo2 N heterostructure as a hydrogen evolution electrocatalyst displays a competitive cell voltage for electrocatalytic water splitting.

Ultrasound-assisted synthesis of europium doped BPO4 nanoparticles; a new approach for Zn2+ (aq) detection.

In this work, europium ion was doped into boron phosphate nanoparticles (BPO4) using an ultrasonic method followed by the calcination process. The nanoparticles were characterized by various techniques such as X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS), photoluminescence spectroscopy, transmission electron microscopy (TEM), and Fourier-transform infrared spectroscopy (FT-IR), Raman spectroscopy, and scanning electron microscopy (SEM). Doping of europium ion into the BPO4 host crystal was proved by cell volume calculation from XRD patterns, the shift in Raman spectra, and photoluminescence properties. In addition, the europium doped boron phosphate (BPE) as a fluorescence sensor for the quantification of Zn2+ cation was studied. The obtained results showed the enhancement and shift of the photoluminescence peak from 292 to 340 nm. The sensor's selectivity toward this ion was verified in the presence of a variety of common interfering cations. Surprisingly, BPE revealed excellent selectivity and sensitivity towards Zn2+ in the presence of Pb2+, Na+, Fe2+, Al3+, Ca2+, Mg2+, Cu2+, Co2+, Ni2+, Mn2+, Cd2+, Hg2+, Ba2+ and Fe3+ cations. The fluorescence response was linearly proportional to the Zn2+concentration. After the addition of trace amounts of Zn2+ ions into the aqueous solution, a significant enhancement of fluorescence emission occurred with the detection limit of 0.3 µM.

Spiroindeno-pyridineindoles (SIPIs) as new visible colorimetric pH indicators.

Some new, highly selective, and sensitive colorimetric pH indicators, spiro[4H-indeno-[1,2-b]pyridine-4,3'-[3H]indoles] (SIPIs) in aqueous solution were developed. SIPIs were synthesized via a one-pot four-component condensation of isatin derivatives, ß-diketones 1,3-indandione, and ammonium acetate using FSi-PrNH-BuSO3H as a nanocatalyst in EtOH. According to the experimental evaluations, it was found that SIPI derivatives are pH indicators for naked-eye detection of OH- ion with intense color changes from orange to purple in the pH range of 10.3-12.

Enhanced visible-light-driven photocatalysis of in-situ reduced of bismuth on BiOCl nanosheets and montmorillonite loading: Synergistic effect and mechanism insight.

Various improvement strategies have been developed to enhance the visible light photocatalytic properties of materials. In these enhancement strategies, bismuth, a non-noble metal-based plasma metal, is deposited on the surface of the photocatalyst, which can improve the visible light response and photocatalytic performance of the photocatalyst. Herein, we constructed montmorillonite loaded BiOCl nanosheets with in situ reduced bismuth by one-step hydrothermal method. As for the results of TEM analysis, the in-situ reduced bismuth nanoparticles with diameters of 5-20 nm were evenly distributed on the surface of BiOCl nanosheets. Due to the surface plasmon resonance (SPR) effect of semi metallic bismuth nanoparticles on the BiOCl nanosheets, the light absorption range of the modified photocatalyst was expanded and its absorption band gap (Eg) was reduced from 3.16 eV (pure BiOCl) to 2.26 eV. Besides, the results of dark adsorption experiments confirmed that the montmorillonite supporter greatly enhanced the adsorption capacity of the modified photocatalyst for pollutants. Moreover, the radical species trapping tests revealed that •O2- and h+ were the pivotal active agents in the pollutant degradation process. The visible light driven photocatalytic degradation rate of TCs and RhB by the modified photocatalyst was 3 and 4 times higher than that of pure BiOCl because of the synergistic effect of montmorillonite supporter and bismuth nanoparticles. The present work provides an innovative strategy for the great feasibility of fabricating low-cost clay and effective bismuth nanoparticles as a substitute for noble metal in environmental pollutants degradation.

Simultaneous electrochemical determination of morphine and methadone by using CMK-5 mesoporous carbon and multivariate calibration.

For the first time, a sensitive electrochemical sensor using a glassy carbon electrode modified with CMK-5 Ordered mesoporous carbon was fabricated for simultaneous analysis of morphine and methadone. Modern electrochemical FFT-SWV techniques and partial least-squares as a multivariable analysis were used in this method. CMK-5 nanostructures were characterized by field emission scanning electron microscopy, transmission electron microscopy, X-ray diffraction analysis, and Raman spectroscopy. Variables such as accumulation time and pH for the proposed sensor were optimized before quantitative analysis. To train the proposed sensor, standard mixtures of morphine (MOR), and methadone (MET) were prepared in the established linear ranges of the analyzes. The results obtained from training samples were used for PLS modeling. The efficiency of the model was determined using test and real matrix samples. The root mean square error of prediction and the squared correlation coefficients (R2p) for MET and MOR were estimated to be 0.00772 and 0.00892 and 0.948 to 0.990, respectively. The recoveries in urine samples were reported to be 97.0 and 105.6% for both MOR and MET, respectively.

Simultaneous Determination of Adulterants in Dietary Food Supplements Using Multivariate Data Analysis after Preconcentration with Novel Nanosorbents and Chromatographic Measurement.

BACKGROUND: The increasing popularity of dietary supplements and, consequently, related adulteration emphasizes the rising need to examine the association of food supplements with fraud. Intentional or unintentional fraud in food supplements by hazardous chemicals compounds is a problem that many countries are struggling with. Much effort have been made to effectively and reliably control the quality of food supplements. OBJECTIVE: Due to the importance of the subject, an analytical method for the simultaneous and reliable detection and quantitative determination of three key adulterants in dietary food supplements was developed. The proposed method benefits from analytical methods and multivariate calibration methods to progress the determination of adulterants in a complex matrix. METHODS: HPLC assisted by multivariate curve resolution-alternating least square (MCR-ALS) analysis was used to detect adulterants in real samples after separation and preconcentration using novel mesoporous carbon nanoparticles. Solid-phase extraction (SPE) optimization was accomplished by central composite design (CCD). In order to obtain the best results, the MCR-ALS model was compared with the parallel factor analysis 2 (PARAFAC2) model and validated by estimation of linearity, detection limits, and recovery. RESULTS: The detection limits and linear dynamics were calculated as 1.5, 4.27, and 4.77 µg/mL, and 1-50, 5-20, and 5-20 µg/mL for caffeine, ephedrine, and fluoxetine, respectively. Mean recovery for determination of caffeine, ephedrine, and fluoxetine using the developed method was reported as 101.75, 91.7, and 92.36, respectively. CONCLUSION: The results showed that to avoid negative health outcomes associated with the excessive consumption of adulterated food supplements releasing such products should be carefully regulated. The developed method was validated using statistical factors and showed acceptable and reliable results. HIGHLIGHTS: (1) The application of MCR-ALS coupled with HPLC-Diode-Array Detection data sets allowed the simultaneous identification and quantification of three key adulterants (caffeine, ephedrine, and fluoxetine) in dietary food supplements. (2) A small amount of the novel adsorbent was successfully used to preconcentrate the trace amounts of adulterants in samples. (3) This method benefits from the chemometrics tools and experimental design to significantly reduce the use of toxic solvents and complicated instruments to propose a less time-consuming method for quantification of multicomponents in the presence of uncalibrated interferents.

A novel fluorescence sensor based on a tripodal carboxylic acid for detection and measurement of Cu2+ in tomato: Experimental and computational studies.

A tripod organic compound, (4,4',4''-[1,3,5-Triazine-2,4,6-triyltris(oxy)] tribenzoic acid, TCPT), with donor triazine core and multiple fluorophore carboxylic motives, was prepared as an efficient ligand with high emission properties. The TCPT fluorescence emission properties as a chemical sensor were studied (λex = 370 nm) upon the addition of an appropriately diverse set of metal cations. The obtained results revealed the highly selective and efficient role of Cu2+ in quenching of TCPT, even with relevant interfering metal ions. The emission of TCPT was independent of the pH. The interaction of the sensor with Cu2+ and followed by absorption spectra and linear trend of the Stern-Volmer diagram, suggested a static quenching process. The density functional theory calculations were carried out to explore the identity of the electronic transition levels, HOMO-LUMO, and bandgap energies of TCPT. The linear range 1.00 × 10-7-1.00 × 10-6 M was obtained by fluorescence titration of a TCPT solution with Cu2+ ions at optimum conditions. The detection limit was calculated as 5.45 × 10-8 M from the established calibration of titration data. The effect of various ions was studied, and there was no significant interference from the studied metal ions. For the real sample analysis, trace levels of Cu2+ ions were successfully determined in the tomato.

Pomegranate Punica granatum peel waste as a naked-eye natural colorimetric sensor for the detection and determination of Fe+3 and I- ions in water.

The utilization of renewable and abundant agricultural waste such as Pomegranate (Punica granatum L.) peel extract has been developed wherein a simple extraction of dried peel in water offered a natural sensor; ensuing yellowish solution comprising phenolic compounds reacted explicitly to detect Fe+3 and I- solutions by naked-eye. The UV-Vis absorption spectrum of the resulting extracted mixture was drastically changed toward the longer wavelengths only after the addition of the Fe3+ and I- while there was no discernible spectral change due to the addition of a broad range of other common cations and anions. In the case of Fe3+ and I-, the transformation can be followed by the naked eye in the concentration range of 5 × 10-4 M and 1 × 10-2 M, respectively. An acceptable and reasonable detection with 47.05426 µM efficiency was attained in comparison to other Fe3+ indicators such as ferroin.

A theoretical and experimental study of polyaniline/GCE and DNA G-quadruplex conformation as an impedimetric biosensor for the determination of potassium ions.

An electrochemical aptasensor has been developed to determine K+ using electrochemical impedance spectroscopy. The polyaniline (PANI) coating was first electrodeposited on a GCE. Then, the potassium-selective aptamer [G3(T2AG3)3] was adsorbed through an electrostatic force between PANI and aptamer. In the presence of K+, the single-stranded DNA is folded into the G-quadruplex configuration, which acts as a barrier against electron transfer at the GCE surface. AFM and FE-SEM images characterize the surface morphology at each fabrication stage. As the K+ concentration increased, the charge transfer resistance (Rct) increased, and the plot of ΔRct versus the logarithm of the K+ concentration is linear over a wide range of 10 pM-60 µM with a low detection limit of 3.7 pM. Finally, the proposed sensor was used to determine K+ in water, serum, urine, and fruit samples. Moreover, the binding stability of the aptamer/PANI and K+/Aptamer/PANI and the interactions between the aptamer and PANI were analyzed through molecular dynamics simulation.

Multicomponent Synthesis and Investigations Fluorescence Activity of Chromenone-Pyrazole Compounds.

A synthetic method is described to produce some chromenone-pyrazole derivatives through a one-pot multicomponent reaction using SrFe12O19 as a magnetic catalyst. This method provides quite a few merits, including the use of an effective and easy separable nanocatalyst, high yields of products, short reaction time, and easy work-up. Two of the products showed fluorescence properties, which have detected mercury ions without any interference with other ions. They can detect a tiny amount of mercury ions, which were comparable with other chemosensors. The detection limit is 4 × 10-7 and 3 × 10-8 M, respectively, for the compound I and II, respectively, which were considered very low amounts. The effect of mercury on health and environmental pollution is essential in medical science.

An On-off Supramolecular Fluorescence Switch for Detection of Pb2+ Ions and Vitamin C.

ß-cyclodextrin-hydroxyquinoline functionalized graphene oxide (GO-CD-HQ) was facilely fabricated to monitor and quantitatively analyze cations in aqueous media. The optical probe was notably selective enhanced toward Pb2+ ions over the other tested ions like Cu2+, Hg2+, Ca2+, Na+, K+, Zn2+, Fe2+, Fe3+, Ag+, Mg2+, and Cd2+ at 468 nm as an emission wavelength. The probe was shown the best performance in pH value, 5, and optimum time 1 min. Absorption spectra have clearly confirmed the static type fluorescence enhancement mechanism of GO-CD-HQ. Under the optimal conditions, the detection limit of it and linear concentration range for Pb2+ ions were obtained as 3.72 × 10-5 M and (5-60) × 10-5 M, respectively. Additionally, the developed assay exhibited logic gate behavior with Pb2+ ions and vitamin C as a masking agent for cited ions.

Molecular dynamics simulation, 3D-pharmacophore and scaffold hopping analysis in the design of multi-target drugs to inhibit potential targets of COVID-19.

SARS-CoV-2 has posed serious threat to the health and has inflicted huge costs in the world. Discovering potent compounds is a critical step to inhibit coronavirus. 3CLpro and RdRp are the most conserved targets associated with COVID-19. In this study, three-dimensional pharmacophore modeling, scaffold hopping, molecular docking, structure-based virtual screening, QSAR-based ADMET predictions and molecular dynamics analysis were used to identify inhibitors for these targets. Binding free energies estimated by molecular docking for each ligand in different binding sites of RdRp were used to predict the active site. Previously reported active 3CLpro and RdRp inhibitors were used to build a pharmacophore model to develop different scaffolds. Structure-based simulations and pharmacophore modeling based on Hip Hop algorithm converged in a state that suggest hydrogen bond acceptor and donor features have a critical role in the two binding sites. Further validations indicated that the best pharmacophore model has fairly good correlation values compared with approved inhibitors. Structure-based simulation results approved that GLu166 and Gln189 in 3CLpro and Lys551 and Glu811 in RdRp, are critical residues for dual activities. Ten compounds were extracted from pharmacophore-based virtual screening in six databases. The results, gained by repurposing approach, suggest the effectiveness of these ten compounds with different scaffolds as possible inhibitors of the two targets. Some quinoline-based hybrid derivatives also were designed. QSAR descriptors plot predicted that the scaffolds have had accepted pharmacokinetic profiles. Multiple molecular dynamics simulations in 100 ns and MM/PBSA studies of some reference inhibitors and the novel compounds in complex with both targets demonstrated stable complexes and confirmed the interaction modes. Based on different computational methods, COVID-19 multi-target inhibitors are proposed. Communicated by Ramaswamy H. Sarma.