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Peptides have emerged as promising therapeutic agents. However, their potential is hindered by hemotoxicity. Understanding the hemotoxicity of peptides is crucial for developing safe and effective peptide-based therapeutics. Here, we employed chemical space complex networks (CSNs) to unravel the hemotoxicity tapestry of peptides. CSNs are powerful tools for visualizing and analyzing the relationships between peptides based on their physicochemical properties and structural features. We constructed CSNs from the StarPepDB database, encompassing 2004 hemolytic peptides, and explored the impact of seven different (dis)similarity measures on network topology and cluster (communities) distribution. Our findings revealed that each CSN extracts orthogonal information, enhancing the motif discovery and enrichment process. We identified 12 consensus hemolytic motifs, whose amino acid composition unveiled a high abundance of lysine, leucine, and valine residues, while aspartic acid, methionine, histidine, asparagine and glutamine were depleted. Additionally, physicochemical properties were used to characterize clusters/communities of hemolytic peptides. To predict hemolytic activity directly from peptide sequences, we constructed multi-query similarity searching models (MQSSMs), which outperformed cutting-edge machine learning (ML)-based models, demonstrating robust hemotoxicity prediction capabilities. Overall, this novel in silico approach uses complex network science as its central strategy to develop robust model classifiers, to characterize the chemical space and to discover new motifs from hemolytic peptides. This will help to enhance the design/selection of peptides with potential therapeutic activity and low toxicity.

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Despite the benefits derived from the use of pharmaceuticals, these compounds are currently considered contaminants of emerging concern because of their presence and persistence in the environment. This study aimed to determine the toxicity of 27 pharmaceuticals and the interaction effects of binary mixtures of selected compounds towards two model organisms: the microcrustacean Daphnia magna and the bacterium Aliivibrio fischeri (Microtox test). Six compounds, namely polymyxin B, polymyxin E, fluoxetine, diphenhydramine, clenbuterol and ketoprofen exhibited moderate toxicity towards D. magna. Additionally, three compounds (cefotaxime, polymyxin B, polymyxin E) also showed a moderate toxic effect on A. fischeri. The comparison of such results with model estimations showed inaccuracy in the predicted data, highlighting the relevance of experimental ecotoxicological assays. The assayed mixtures contained four selected drugs of high-hazard according to their reported concentrations in wastewater and surface water (diphenhydramine, trimethoprim, ketoprofen, and fluoxetine); data revealed interactions only in the fluoxetine-containing mixtures for D. magna, while all mixtures showed interactions (mostly synergistic) for Microtox. Chronic effects on the reproduction of D. magna were observed after exposure to fluoxetine and diphenhydramine, although higher sensitivity was determined for the latter, while the mixture of these compounds (which showed acute synergy in both models) also affected the reproduction patterns. Nonetheless, all the effects described at the acute or chronic level (for individual compounds or mixtures) were determined at concentrations higher than commonly reported at environmental levels. This work provides valuable ecotoxicological information for the risk assessment of pharmaceuticals and their mixtures in the environment.

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The prediction of possible lead compounds from already-known drugs that may present DPP-4 inhibition activity imply a advantage in the drug development in terms of time and cost to find alternative medicines for the treatment of Type 2 Diabetes Mellitus (T2DM). The inhibition of dipeptidyl peptidase-4 (DPP-4) has been one of the most explored strategies to develop potential drugs against this condition. A diverse dataset of molecules with known experimental inhibitory activity against DPP-4 was constructed and used to develop predictive models using different machine-learning algorithms. Model M36 is the most promising one based on the internal and external performance showing values of Q2CV = 0.813, and Q2EXT = 0.803. The applicability domain evaluation and Tropsha's analysis were conducted to validate M36, indicating its robustness and accuracy in predicting pIC50 values for organic molecules within the established domain. The physicochemical properties of the ligands, including electronegativity, polarizability, and van der Waals volume were relevant to predict the inhibition process. The model was then employed in the virtual screening of potential DPP4 inhibitors, finding 448 compounds from the DrugBank and 9 from DiaNat with potential inhibitory activity. Molecular docking and molecular dynamics simulations were used to get insight into the ligand-protein interaction. From the screening and the favorable molecular dynamic results, several compounds including Skimmin (pIC50 = 3.54, Binding energy = -8.86 kcal/mol), bergenin (pIC50 = 2.69, Binding energy = -13.90 kcal/mol), and DB07272 (pIC50 = 3.97, Binding energy = -25.28 kcal/mol) seem to be promising hits to be tested and optimized in the treatment of T2DM. This results imply a important reduction in cost and time on the application of this drugs because all the information about the its metabolism is already available.

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In this article, three unsymmetrical 7-(diethylamino)quinolone chalcones with D-π-A-D and D-π-A-π-D type push-pull molecular arrangements were synthesized via a Claisen-Schmidt reaction. Using 7-(diethylamino)quinolone and vanillin as electron donor (D) moieties, these were linked together through the α,ß-unsaturated carbonyl system acting as a linker and an electron acceptor (A). The photophysical properties were studied, revealing significant Stokes shifts and strong solvatofluorochromism caused by the ICT and TICT behavior produced by the push-pull effect. Moreover, quenching caused by the population of the TICT state in THF-H2O mixtures was observed, and the emission in the solid state evidenced a red shift compared to the emission in solution. These findings were corroborated by density functional theory (DFT) calculations employing the wb97xd/6-311G(d,p) method. The cytotoxic activity of the synthesized compounds was assessed on BHK-21, PC3, and LNCaP cell lines, revealing moderate activity across all compounds. Notably, compound 5b exhibited the highest activity against LNCaP cells, with an LC50 value of 10.89 µM. Furthermore, the compounds were evaluated for their potential as imaging agents in living prostate cells. The results demonstrated their favorable cell permeability and strong emission at 488 nm, positioning them as promising candidates for cancer cell imaging applications.

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Microplastic (MP) accumulation in the environment is accelerating rapidly, which has led to their effects on both the ecosystem and human life garnering much attention. This study is the first to examine the degradation of high-density polyethylene (HDPE) MPs via photoelectrocatalysis (PEC) using a TiO2-modified boron-doped diamond (BDD/TiO2) photoanode. This study was divided into three stages: (i) preparation of the photoanode through electrophoretic deposition of synthetic TiO2 nanoparticles on a BDD electrode; (ii) characterization of the modified photoanode using electrochemical, structural, and optical techniques; and (iii) degradation of HDPE MPs by electrochemical oxidation and photoelectrocatalysis on bare and modified BDD electrodes under dark and UV light conditions. The results indicate that the PEC technique degraded 89.91 ± 0.08% of HDPE MPs in a 10-h reaction and was more efficient at a lower current density (6.89 mA cm-1) with the BDD/TiO2 photoanode compared to electrochemical oxidation on bare BDD.

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Pharmaceuticals comprise a complex group of emerging pollutants. Despite the significant number of pharmaceuticals used in veterinary medicine, the input of these compounds into the environment due to livestock activities has been scarcely described. This work assays for the first time in Central America the occurrence of pharmaceuticals in farm wastewater in an area devoted to dairy production, and in the surrounding surface waters. Among 69 monitored pharmaceuticals, a total of eight compounds were detected in wastewater samples collected from seven dairy farms after three sampling campaigns. Six pharmaceuticals were considered either of high (albendazole, lovastatin and caffeine) or intermediate estimated hazard (ciprofloxacin, acetaminophen and ketoprofen) based on the HQ approach, while 26% of the samples were considered of high estimated hazard according to the cumulative ∑HQ approach. Similarly, when ecotoxicological tests were applied, all the samples showed some level of toxicity towards Daphnia magna, and most samples towards Vibrio fischeri and Lactuca sativa. Fourteen pharmaceuticals were detected in surface water samples collected in the surroundings of the dairy production farms, including rural and urban areas. Seven out of these compounds showed high estimated risk (risperidone, diphenhydramine, trimethoprim, fluoxetine, ofloxacin, caffeine and ibuprofen), while three (gemfibrozil, ciprofloxacin and cephalexin) exhibited intermediate estimated risk. In a similar worrisome way, 27% of these samples were estimated to pose high environmental risk according to the pharmaceutical content. Despite being nontoxic for D. magna or V. fischeri, frequent inhibition (>20%) of GI in L. sativa was determined in 34% of surface water samples; such findings raise concern on the apparent inceptive environmental pollution and risk within the area. According to the pharmaceutical content patterns in both kinds of studied matrices, no clear evidence of significant contamination in surface water due to livestock activities could be retrieved, suggesting a main role of urban influence.

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Notwithstanding the wide adoption of the OECD principles (or best practices) for QSAR modeling, disparities between in silico predictions and experimental results are frequent, suggesting that model predictions are often too optimistic. Of these OECD principles, the applicability domain (AD) estimation has been recognized in several reports in the literature to be one of the most challenging, implying that the actual reliability measures of model predictions are often unreliable. Applying tree-based error analysis workflows on 5 QSAR models reported in the literature and available in the QsarDB repository, i.e., androgen receptor bioactivity (agonists, antagonists, and binders, respectively) and membrane permeability (highest membrane permeability and the intrinsic permeability), we demonstrate that predictions erroneously tagged as reliable (AD prediction errors) overwhelmingly correspond to instances in subspaces (cohorts) with the highest prediction error rates, highlighting the inhomogeneity of the AD space. In this sense, we call for more stringent AD analysis guidelines which require the incorporation of model error analysis schemes, to provide critical insight on the reliability of underlying AD algorithms. Additionally, any selected AD method should be rigorously validated to demonstrate its suitability for the model space over which it is applied. These steps will ultimately contribute to more accurate estimations of the reliability of model predictions. Finally, error analysis may also be useful in "rational" model refinement in that data expansion efforts and model retraining are focused on cohorts with the highest error rates.

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Animal husbandry wastewaters represent an important source of pharmaceuticals into the environment. This work aimed to evaluate the occurrence of pharmaceuticals and their hazard in wastewater from a model dairy farm from Costa Rica. Among the seven pharmaceuticals detected (acetaminophen, caffeine, carbamazepine, ibuprofen, ketoprofen, risperidone, sulfamethazine), caffeine, ibuprofen and acetaminophen showed the highest concentrations, while caffeine, carbamazepine and risperidone were the most frequently detected compounds. High (HQ ≥ 1) or medium (0.1 ≤ HQ < 1) hazard were estimated for three (caffeine, ibuprofen, risperidone) and two (acetaminophen, ketoprofen) pharmaceuticals, respectively; similarly, high overall hazard (∑HQ) and significant ecotoxicity were determined in samples from all sampling points. According to our results, the release of these aqueous matrices is a matter of environmental concern, as the treated wastewater is used for farm irrigation or directly released into nearby water streams. This work contributes to the knowledge on the scarcely described occurrence and risk of pharmaceuticals in Latin American regions.

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The racemization of biomolecules in the active site can reduce the biological activity of drugs, and the mechanism involved in this process is still not fully comprehended. The present study investigates the impact of aromaticity on racemization using advanced theoretical techniques based on density functional theory. Calculations were performed at the ωb97xd/6-311++g(d,p) level of theory. A compelling explanation for the observed aromatic stabilization via resonance is put forward, involving a carbanion intermediate. The analysis, employing Hammett's parameters, convincingly supports the presence of a negative charge within the transition state of aromatic compounds. Moreover, the combined utilization of natural bond orbital (NBO) analysis and intrinsic reaction coordinate (IRC) calculations confirms the pronounced stabilization of electron distribution within the carbanion intermediate. To enhance our understanding of the racemization process, a thorough examination of the evolution of NBO charges and Wiberg bond indices (WBIs) at all points along the IRC profile is performed. This approach offers valuable insights into the synchronicity parameters governing the racemization reactions.

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This work aims to determine the occurrence, hazard and prioritization of pharmaceuticals from hospital wastewater in Costa Rica through the monitoring of 70 compounds and assessing their environmental risk through a hazard quotient approach (HQ). Moreover, the quantification of selected antibiotic resistance genes (ARGs) was conducted for the first time in this matrix in this geographical location. Thirty-four pharmaceuticals were detected, being caffeine, 1,7-dimethylxanthine, acetaminophen, ibuprofen, naproxen, ciprofloxacin and ketoprofen the most frequent (>50% of the samples). Eighteen pharmaceuticals exhibited high hazard (HQ ≥ 1), while five more showed medium hazard (1 > HQ ≥ 0.1). Prioritization, which also included frequency parameters, revealed caffeine, lovastatin, diphenhydramine, acetaminophen, ibuprofen, ciprofloxacin, and sildenafil as the compounds of major concern. Similarly, cumulative hazard per sample (ΣHQ) estimated high hazard towards aquatic organisms in every sample. All selected ARGs, except mcr-1 (polymyxin resistance), were detected. Among genes conferring resistance to beta-lactams, blaCTX-M and blaKPC were the most abundant, related to resistance to cephalosporins and carbapenems. Ecotoxicological evaluation showed mostly low toxicity towards Daphnia magna and Vibrio fischeri, contrary to the marked effect observed towards Lactuca sativa. These findings provide relevant and novel information on the risk posed by hospital wastewater and their pharmaceutical content in the Latin American environmental context.

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Herein, biochar from biomass residues is demonstrated as active materials for the catalytic cracking of waste motor oil into diesel-like fuels. Above all, alkali-treated rice husk biochar showed great activity with a 250% increase in the kinetic constant compared to the thermal cracking. It also showed better activity than synthetic materials, as previously reported. Moreover, much lower activation energy (185.77to293.48kJmol) for the cracking process was also obtained. According to materials characterization, the catalytic activity was more related to the nature of the biochar's surface than its specific surface area. Finally, liquid products complied with all the physical properties defined by international standards for diesel-like fuels, with the presence of hydrocarbons chains between C10-C27 similar to the ones obtained in commercial diesel.

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Despite their environmental implications, ecotoxicological information regarding pesticide mixtures is relatively scarce. This study aimed to determine the ecotoxicity of individual pesticide formulations and their mixtures (insecticides and fungicides), which are applied during the production cycle of potato, according to agricultural practices from a Latin American region in Costa Rica. Two benchmark organisms were employed: Daphnia magna and Lactuca sativa. First, the evaluation of individual formulations (chlorothalonil, propineb, deltamethrin+imidacloprid, ziram, thiocyclam and chlorpyrifos) revealed differences between available EC50 for active ingredients (a.i.) and their respective formulations toward D. magna; on the contrary, no information could be retrieved from scientific literature for comparison in the case of L. sativa. In general, acute toxicity was higher toward D. magna than L. sativa. Moreover, interactions could not be determined on L. sativa, as the chlorothalonil formulation was not toxic at high levels and the concentration-response to propineb could not be fitted to obtain an IC50 value. The commercial formulation composed of deltamethrin+imidacloprid followed the concentration addition model (when compared with parameters retrieved from individual a.i.) and the other three mixtures evaluated (I: chlorothalonil-propineb-deltamethrin+imidacloprid; II: chlorothalonil-propineb-ziram-thiocyclam; III: chlorothalonil-propineb-chlorpyrifos) produced an antagonistic effect on D. magna, thus suggesting less acute toxicity than their individual components. Subsequent chronic studies showed that one of the most toxic mixtures (II) negatively affected D. magna reproduction at sublethal concentrations indicating that this mixture poses a risk to this species if these pesticides co-exist in freshwater systems. These findings provide useful data to better estimate the impact of real agricultural practices related to the use of agrochemicals.

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This research reported a hydrogel loaded with the ethanolic and methanolic extracts of Eupatorium glutinosum Lam. The E. glutinosum extracts were characterized by phytochemical screening, Fourier-transform infrared spectroscopy (FTIR), thin-layer chromatography (TLC), and UV/Vis profile identification. This research also evaluated the pharmacological activity of the extracts using antimicrobial, antioxidant, and anti-inflammatory assays prior to polymeric encapsulation. Results indicate that extracts inhibit the Escherichia colii DH5-α (Gram negative) growth; excellent antioxidant activity was evaluated by the ferric reducing power and total antioxidant activity assays, and extracts showed an anti-hemolytic effect. Moreover, the cotton and microcrystalline cellulose hydrogels demonstrate successful encapsulation based on characterization and kinetics studies such as FTIR, extract release, and swelling degree. Moreover, effective antibacterial activity was registered by the loaded hydrogel. The overall results encourage and show that Eupatorium glutinosum-loaded hydrogel may find a wide range of bandage and wound healing applications in the biomedical area.

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Electrophilicity (E) is one of the most important parameters to understand the reactivity of an organic molecule. Although the theoretical electrophilicity index (ω) has been associated with E in a small homologous series, the use of w to predict E in a structurally heterogeneous set of compounds is not a trivial task. In this study, a robust ensemble model is created using Mayr's database of reactivity parameters. A combination of topological and quantum mechanical descriptors and different machine learning algorithms are employed for the model's development. The predictability of the model is assessed using different statistical parameters, and its validation is examined, including a training/test partition, an applicability domain, and a y-scrambling test. The global ensemble model presents a Q5-fold2 of 0.909 and a Qext2 of 0.912, demonstrating an excellent predictability performance of E values and showing that w is not a good descriptor for the prediction of E, especially for the case of neutral compounds. ElectroPredictor, a noncommercial Python application (https://github.com/mmoreno1/ElectroPredictor), is developed to predict E. QM9, a well-known large dataset containing 133885 neutral molecules, is used to perform a virtual screening (94.0% coverage). Finally, the 10 most electrophilic molecules are analyzed as possible new Mayr's electrophiles, which have not yet been experimentally tested. This study confirms the necessity to build an ensemble model using nonlinear machine learning algorithms, topographic descriptors, and separating molecules into charged and neutral compounds to predict E with precision.

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In this study, a series of novel quinolinone-based thiosemicarbazones were designed in silico and their activities tested in vitro against Mycobacterium tuberculosis (M. tuberculosis). Quantitative structure-activity relationship (QSAR) studies were performed using quinolinone and thiosemicarbazide as pharmacophoric nuclei; the best model showed statistical parameters of R2 = 0.83; F = 47.96; s = 0.31, and was validated by several different methods. The van der Waals volume, electron density, and electronegativity model results suggested a pivotal role in antituberculosis (anti-TB) activity. Subsequently, from this model a new series of quinolinone-thiosemicarbazone 11a-e was designed and docked against two tuberculosis protein targets: enoyl-acyl carrier protein reductase (InhA) and decaprenylphosphoryl-ß-D-ribose-2'-oxidase (DprE1). Molecular dynamics simulation over 200 ns showed a binding energy of -71.3 to -12.7 Kcal/mol, suggesting likely inhibition. In vitro antimycobacterial activity of quinolinone-thiosemicarbazone for 11a-e was evaluated against M. bovis, M. tuberculosis H37Rv, and six different strains of drug-resistant M. tuberculosis. All compounds exhibited good to excellent activity against all the families of M. tuberculosis. Several of the here synthesized compounds were more effective than the standard drugs (isoniazid, oxafloxacin), 11d and 11e being the most active products. The results suggest that these compounds may contribute as lead compounds in the research of new potential antimycobacterial agents.

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The nature of the Na⋯B bond, in the recently synthesized NaBH 3 - adduct, is analyzed on the light of the Na- propensity to polarize along the bond axis as a consequence of the electric field produced by the BH3 fragment. The observed induced polarization has two consequences: (i) the energetic stabilization of the Na- , and (ii) the split of its valence electrons into two opposite lobes along the bond axis. Additionally, an analysis of the electron localization is presented using the information content of the correlated conditional pair density that reveals a significant delocalization between one lobe of the polarized Na- anion and the BH3 fragment at the equilibrium distance. Our findings reported here complement previous works on this system.

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Dapsone (DDS) is an antibacterial drug with well-known antioxidant properties. However, the antioxidant behavior of its derivatives has not been well explored. In the present work, the antioxidant activity of 10 dapsone derivatives 4-substituted was determined by an evaluation in two in vitro models (DPPH radical scavenging assay and ferric reducing antioxidant power). These imine derivatives 1-10 were obtained through condensation between DDS and the corresponding aromatic aldehydes 4-substuited. Three derivatives presented better results than DDS in the determination of DPPH (2, 9, and 10). Likewise, we have three compounds with better reducing activity than dapsone (4, 9, and 10). In order to be more insight, the redox process, a conceptual DFT analysis was carried out. Molecular descriptors such as electronic distribution, the total charge accepting/donating capacity (I/A), and the partial charge accepting/donating capacity (ω+/ω-) were calculated to analyze the relative donor-acceptor capacity through employing a donor acceptor map (DAM). The DFT calculation allowed us to establish a relationship between GAPHOMO-LUMO and DAM with the observed antioxidant effects. According to the results, we concluded that compounds 2 and 3 have the lowest Ra values, representing a good antioxidant behavior observed experimentally in DPPH radical capturing. On the other hand, derivatives 4, 9, and 10 display the best reducing capacity activity with the highest ω- and Rd values. Consequently, we propose these compounds as the best antireductants in our DDS imine derivative series.

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In this study, the degradation mechanism of chloroacetanilide herbicides in the presence of four different nucleophiles, namely: Br-, I-, HS-, and S2O3-2, was theoretically evaluated using the dispersion-corrected hybrid functional wB97XD and the DGDZVP as a basis set. The comparison of computed activation energies with experimental data shows an excellent correlation (R2 = 0.98 for alachlor and 0.97 for propachlor). The results suggest that the best nucleophiles are those where a sulfur atom performs the nucleophilic attack, whereas the other species are less reactive. Furthermore, it was observed that the different R groups of chloroacetanilide herbicides have a negligible effect on the activation energy of the process. Further insights into the mechanism show that geometrical changes and electronic rearrangements contribute 60% and 40% of the activation energy, respectively. A deeper analysis of the reaction coordinate was conducted, employing the evolution chemical potential, hardness, and electrophilicity index, as well as the electronic flux. The charge analysis shows that the electron density of chlorine increases as the nucleophilic attack occurs. Finally, NBO analysis indicates that the nucleophilic substitution in chloroacetanilides is an asynchronous process with a late transition state for all models except for the case of the iodide attack, which occurs through an early transition state in the reaction.

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The occurrence, persistence, and accumulation of antibiotics and non-steroidal anti-inflammatory drugs (NSAIDs) represent a new environmental problem due to their harmful effects on human and aquatic life. A suitable absorbent for a particular type of pollutant does not necessarily absorb other types of compounds, so knowing the compatibility between a particular pollutant and a potential absorbent before experimentation seems to be fundamental. In this work, the molecular interactions between some pharmaceuticals (amoxicillin, ibuprofen, and tetracycline derivatives) with two potential absorbers, chitosan and graphene oxide models (pyrene, GO-1, and coronene, GO-2), were studied using the ωB97X-D/6-311G(2d,p) level of theory. The energetic interaction order found was amoxicillin/chitosan > amoxicillin/GO-1 > amoxicillin/GO-2 > ibuprofen/chitosan > ibuprofen/GO-2 > ibuprofen/GO-1, the negative sign for the interaction energy in all complex formations confirms good compatibility, while the size of Eint between 24-34 kcal/mol indicates physisorption processes. Moreover, the free energies of complex formation were negative, confirming the spontaneity of the processes. The larger interaction of amoxicillin Gos, compared to ibuprofen Gos, is consistent with previously reported experimental results, demonstrating the exceptional predictability of these methods. The second-order perturbation theory analysis shows that the amoxicillin complexes are mainly driven by hydrogen bonds, while van der Waals interactions with chitosan and hydrophobic interactions with graphene oxides are modelled for the ibuprofen complexes. Energy decomposition analysis (EDA) shows that electrostatic energy is a major contributor to the stabilization energy in all cases. The results obtained in this work promote the use of graphene oxides and chitosan as potential adsorbents for the removal of these emerging pollutants from water.