Nanomaterials have revolutionized modern science and technology due to their multiple applications in engineering, physics, chemistry, and biomedicine. Nevertheless, the use and manipulation of ...nanoparticles (NPs) can bring serious damages to living organisms and their ecosystems. For this reason, ecotoxicity and cytotoxicity assays are of special interest in order to determine the potential harmful effects of NPs. Processes based on ecotoxicity and cytotoxicity tests can significantly consume time and financial resources. In this sense, alternative approaches such as quantitative structure–activity/toxicity relationships (QSAR/QSTR) modeling have provided important insights for the better understanding of the biological behavior of NPs that may be responsible for causing toxicity. Until now, QSAR/QSTR models have predicted ecotoxicity or cytotoxicity separately against only one organism (bioindicator species or cell line) and have not reported information regarding the quantitative influence of characteristics other than composition or size. In this work, we developed a unified QSTR-perturbation model to simultaneously probe ecotoxicity and cytotoxicity of NPs under different experimental conditions, including diverse measures of toxicities, multiple biological targets, compositions, sizes and conditions to measure those sizes, shapes, times during which the biological targets were exposed to NPs, and coating agents. The model was created from 36488 cases (NP–NP pairs) and exhibited accuracies higher than 98% in both training and prediction sets. The model was used to predict toxicities of several NPs that were not included in the original data set. The results of the predictions suggest that the present QSTR-perturbation model can be employed as a highly promising tool for the fast and efficient assessment of ecotoxicity and cytotoxicity of NPs.
Quantitative structure–activity relationships (QSAR) modeling is a well-known computational technique with wide applications in fields such as drug design, toxicity predictions, nanomaterials, etc. ...However, QSAR researchers still face certain problems to develop robust classification-based QSAR models, especially while handling response data pertaining to diverse experimental and/or theoretical conditions. In the present work, we have developed an open source standalone software “QSAR-Co” (available to download at https://sites.google.com/view/qsar-co) to setup classification-based QSAR models that allow mining the response data coming from multiple conditions. The software comprises two modules: (1) the Model development module and (2) the Screen/Predict module. This user-friendly software provides several functionalities required for developing a robust multitasking or multitarget classification-based QSAR model using linear discriminant analysis or random forest techniques, with appropriate validation, following the principles set by the Organisation for Economic Co-operation and Development (OECD) for applying QSAR models in regulatory assessments.
To streamline the interpretation of vibrational spectra, this work introduces the use of Bayesian linear regression with automatic relevance determination as a viable approach to decompose the atomic ...motions along any vibrational mode as a weighted combination of displacements along chemically meaningful internal coordinates. This novel approach denominated vibrational mode automatic relevance determination (VMARD) is presented and compared with the well-established potential energy decomposition (PED) scheme. Good agreement is generally attained between the two methods. VMARD returns a decomposition of the atomic displacement using only a small number of internal coordinates, thus aiding the interpretation of the vibrational spectra. Moreover, the results show that the VMARD descriptions are resilient toward the addition of additional internal coordinates, achieving a concise description of the vibrational modes despite the use of redundant internal coordinates. Potential applications of VMARD involving the gathering of physical insights on the atomic motions along the reaction coordinate at transition state structures, as well as the improvement of theoretically predicted vibrational frequencies, are also presented under a proof-of-concept perspective.
Organic compounds are often exposed to the environment, and have an adverse effect on the environment and human health in the form of mixtures, rather than as single chemicals. In this paper, we try ...to establish reliable and developed classical quantitative structure⁻activity relationship (QSAR) models to evaluate the toxicity of 99 binary mixtures. The derived QSAR models were built by forward stepwise multiple linear regression (MLR) and nonlinear radial basis function neural networks (RBFNNs) using the hypothetical descriptors, respectively. The statistical parameters of the MLR model provided were N (number of compounds in training set) = 79, R² (the correlation coefficient between the predicted and observed activities)= 0.869, LOOq² (leave-one-out correlation coefficient) = 0.864, F (Fisher's test) = 165.494, and RMS (root mean square) = 0.599 for the training set, and N
(number of compounds in external test set) = 20, R² = 0.853, qext2 (leave-one-out correlation coefficient for test set)= 0.825, F = 30.861, and RMS = 0.691 for the external test set. The RBFNN model gave the statistical results, namely N = 79, R² = 0.925, LOOq² = 0.924, F = 950.686, RMS = 0.447 for the training set, and N
= 20, R² = 0.896, qext2 = 0.890, F = 155.424, RMS = 0.547 for the external test set. Both of the MLR and RBFNN models were evaluated by some statistical parameters and methods. The results confirm that the built models are acceptable, and can be used to predict the toxicity of the binary mixtures.
Nanotechnology has brought great advances to many fields of modern science. A manifold of applications of nanoparticles have been found due to their interesting optical, electrical, and ...biological/chemical properties. However, the potential toxic effects of nanoparticles to different ecosystems are of special concern nowadays. Despite the efforts of the scientific community, the mechanisms of toxicity of nanoparticles are still poorly understood. Quantitative-structure activity/toxicity relationships (QSAR/QSTR) models have just started being useful computational tools for the assessment of toxic effects of nanomaterials. But most QSAR/QSTR models have been applied so far to predict ecotoxicity against only one organism/bio-indicator such as Daphnia magna. This prevents having a deeper knowledge about the real ecotoxic effects of nanoparticles, and consequently, there is no possibility to establish an efficient risk assessment of nanomaterials in the environment. In this work, a perturbation model for nano-QSAR problems is introduced with the aim of simultaneously predicting the ecotoxicity of different nanoparticles against several assay organisms (bio-indicators), by considering also multiple measures of ecotoxicity, as well as the chemical compositions, sizes, conditions under which the sizes were measured, shapes, and the time during which the diverse assay organisms were exposed to nanoparticles. The QSAR-perturbation model was derived from a database containing 5520 cases (nanoparticle–nanoparticle pairs), and it was shown to exhibit accuracies of ca. 99% in both training and prediction sets. In order to demonstrate the practical applicability of our model, three different nickel-based nanoparticles (Ni) with experimental values reported in the literature were predicted. The predictions were found to be in very good agreement with the experimental evidences, confirming that Ni-nanoparticles are not ecotoxic when compared with other nanoparticles. The results of this study thus provide a single valuable tool toward an efficient prediction of the ecotoxicity of nanoparticles under multiple experimental conditions.
•A QSAR-perturbation model was created to predict ecotoxicity of nanoparticles.•Ecotoxicities were predicted under multiple sets of experimental conditions.•Physicochemical interpretations of the descriptors were provided.•The QSAR-perturbation was used to predict new nickel-based nanoparticles.•Strong agreement existed between the theoretical predictions and the experiments.
Many challenges persist in developing accurate computational models for predicting solvation free energy (ΔG sol). Despite recent developments in Machine Learning (ML) methodologies that outperformed ...traditional quantum mechanical models, several issues remain concerning explanatory insights for broad chemical predictions with an acceptable speed–accuracy trade-off. To overcome this, we present a novel supervised ML model to predict the ΔG sol for an array of solvent–solute pairs. Using two different ensemble regressor algorithms, we made fast and accurate property predictions using open-source chemical features, encoding complex electronic, structural, and surface area descriptors for every solvent and solute. By integrating molecular properties and chemical interaction features, we have analyzed individual descriptor importance and optimized our model though explanatory information form feature groups. On aqueous and organic solvent databases, ML models revealed the predictive relevance of solutes with increasing polar surface area and decreasing polarizability, yielding better results than state-of-the-art benchmark Neural Network methods (without complex quantum mechanical or molecular dynamic simulations). Both algorithms successfully outperformed previous ΔG sol predictions methods, with a maximum absolute error of 0.22 ± 0.02 kcal mol–1, further validated in an external benchmark database and with solvent hold-out tests. With these explanatory and statistical insights, they allow a thoughtful application of this method for predicting other thermodynamic properties, stressing the relevance of ML modeling for further complex computational chemistry problems.
We report a comparative periodic density functional theory study of the reaction of water dissociation on five platinum surfaces, e.g., Pt(111) Pt(100), Pt(110), Pt(211), and Pt(321). These surfaces ...were chosen to study the surface structural effects in the reaction of water dissociation. It was found that water molecules adsorb stronger on surfaces presenting low coordinated atoms in the surface. In the cases of the stepped Pt(110) and kinked Pt(321) surfaces, the activation energy barriers are smaller than the adsorption energies for the water molecule on the corresponding surfaces. Therefore, the calculations suggest that the dissociation reaction will take place preferentially at corner or edge sites on platinum particles with the (110) orientation. The inclusion of the results obtained in this work in previous derived BEP relationships confirms that the adsorption energy of the reaction products arises as the most appropriate descriptor for water dissociation on transition metal surfaces.
Gastric cancer is the third leading cause of cancer-related mortality worldwide and despite advances in prevention, diagnosis and therapy, it is still regarded as a global health concern. The ...efficacy of the therapies for gastric cancer is limited by a poor response to currently available therapeutic regimens. One of the reasons that may explain these poor clinical outcomes is the highly heterogeneous nature of this disease. In this sense, it is essential to discover new molecular agents capable of targeting various gastric cancer subtypes simultaneously. Here, we present a multi-objective approach for the ligand-based virtual screening discovery of chemical compounds simultaneously active against the gastric cancer cell lines AGS, NCI-N87 and SNU-1. The proposed approach relays in a novel methodology based on the development of ensemble models for the bioactivity prediction against each individual gastric cancer cell line. The methodology includes the aggregation of one ensemble per cell line using a desirability-based algorithm into virtual screening protocols. Our research leads to the proposal of a multi-targeted virtual screening protocol able to achieve high enrichment of known chemicals with anti-gastric cancer activity. Specifically, our results indicate that, using the proposed protocol, it is possible to retrieve almost 20 more times multi-targeted compounds in the first 1% of the ranked list than what is expected from a uniform distribution of the active ones in the virtual screening database. More importantly, the proposed protocol attains an outstanding initial enrichment of known multi-targeted anti-gastric cancer agents.
Efflux pumps of the ATP-binding cassette transporters superfamily (ABC transporters) are frequently involved in the multidrug-resistance (MDR) phenomenon in cancer cells. Herein, we describe a new ...atomistic model for the MDR-related ABCG2 efflux pump, also named breast cancer resistance protein (BCRP), based on the recently published crystallographic structure of the ABCG5/G8 heterodimer sterol transporter, a member of the ABCG family involved in cholesterol homeostasis. By means of molecular dynamics simulations and molecular docking, a far-reaching characterization of the ABCG2 homodimer was obtained. The role of important residues and motifs in the structural stability of the transporter was comprehensively studied and was found to be in good agreement with the available experimental data published in literature. Moreover, structural motifs potentially involved in signal transmission were identified, along with two symmetrical drug-binding sites that are herein described for the first time, in a rational attempt to better understand how drug binding and recognition occurs in ABCG2 homodimeric transporters.
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