QSPR study on solubility of some fullerenes derivatives using the genetic algorithms — Multiple linear regression

Fullerene Derivatives for Drug Delivery against COVID-19: A Molecular Dynamics Investigation of Dendro[60]fullerene as Nanocarrier of Molnupiravir

In this paper, a theoretical investigation is made regarding the possibility of using a water-soluble derivative of C₆₀ as a drug delivery agent for treating Coronavirus disease 2019 (COVID-19). Molnupiravir is chosen as the transporting pharmaceutical compound since it has already proved to be very helpful in saving lives in case of hospitalization. According to the proposed formulation, a carboxyfullerene known as dendro[60]fullerene is externally connected with two molnupiravir molecules. Two properly formed nitrogen single bonds (N-N) are used as linkers between the dendro[60]fullerene and the two molnupiravir molecules to create the final form of the C₆₀ derivate/molnupiravir conjugate. The energetics of the developed molecular system and its interaction with water and n-octanol are extensively studied via classical molecular dynamics (MD) using the COMPASS II force field. To study the interactions with water and n-octanol, an appropriate periodic amorphous unit cell is created that contains a single C₆₀ derivative/molnupiravir system surrounded by numerous solvent molecules and simulated via MD in room conditions. In addition, the corresponding solvation-free energies of the investigated drug delivery system are computed and set in contrast with the corresponding properties of the water-soluble dendro[60]fullerene, to test its solubility capabilities.

Nanomaterials: Quasi-SMILES as a flexible basis for regulation and environmental risk assessment

Basic principles and problems of the systematization of data on nanomaterials are discussed. The eclectic character of nanomaterials is defined as the key difference between nanomaterials and traditional substances. The quasi-SMILES technique is described and discussed. The possible role of the approach is bridging between experimentalists and developers of models for endpoints related to nanomaterials. The use of models on the possible impact of nanomaterials on the environment and human health has been collected and compared. The new criteria of the predictive potential for the above models are discussed. The advantage of the statistical criteria sensitive simultaneously to both the correlation coefficient and the root mean square error noted. The rejection of the border between the effect of the biochemical reality of substances at a molecular level and the effect of experiment conditions at the macro level gives the possibility to develop models that are epistemologically more reliable in the comparison with traditional models based exclusively on the molecular structure-biological activity interdependence (without taking into account experimental conditions). Models of the physicochemical and biochemical behaviour of nanomaterials are necessary in order to develop and apply new industrial achievements, everyday comfort species, medicine, cosmetics, and foods without negative effects on ecology and human health. The CORAL (abbreviation CORrelation And Logic) software provides the user with the possibility to build up nano-QSAR models as a mathematical function of so-called correlation weights of fragments of quasi-SMILES. These models are built up via the Monte Carlo method. Apparently, the quasi-SMILES is a universal representation of nano-reality since there is no limitation to choose the list of eclectic data able to have an impact on nano-phenomena. This paradigm is a convenient language to the conversation of experimentalists and developers of models for nano-phenomena.

Computer-aided molecular design of 2-anilino 4-amino substituted quinazolines derivatives as malarial inhibitors

Quantitative structure–activity relationship studies conducted on forty-five (45) derivatives of 2-anilino 4-amino substituted quinazolines as malaria inhibitors to determine the structures responsible for their antimalarial properties and design novel derivatives with improved activities. The molecular descriptors generated were selected to develop the theoretical model using the genetic approximation component of the material studio. The developed model found to be a function of ATSC8c, GATS8i, SpMin1_Bhi, JGI10, and TDB6u descriptors, shows excellent statistical parameters (R2 = 0.7913, R2adj = 0.7553, Q2cv = 0.7112, LOF = 0.2125, and R2pred = 0.7650). The mean effect (MF) analysis revealed the descriptor SpMin1_Bhi, as the most influential by its largest percentage contribution (54%) to the developed model. The descriptor decodes the information on the first ionization potentials and was found to have positive MF. Hence, activity increases with increases the descriptor value. Structural modifications of the template (compound 13; pEC50 = 7.387) using electron-withdrawing groups increases the descriptor value (first ionization potentials) of the template, which by extension increases the antimalarial activity lead to the design of ten (10) novel theoretical derivatives with improve antimalarial activities. Compound 3, N4-(3-bromo-5-fluorobenzyl)-N2-(4-fluorophenyl)-6,7-dimethoxyquinazoline-2,4-diamine was found to have the highest antimalarial activities among all the designed derivatives (pEC50 = 8.0515).

Practices and Trends of Machine Learning Application in Nanotoxicology

Machine Learning (ML) techniques have been applied in the field of nanotoxicology with very encouraging results. Adverse effects of nanoforms are affected by multiple features described by theoretical descriptors, nano-specific measured properties, and experimental conditions. ML has been proven very helpful in this field in order to gain an insight into features effecting toxicity, predicting possible adverse effects as part of proactive risk analysis, and informing safe design. At this juncture, it is important to document and categorize the work that has been carried out. This study investigates and bookmarks ML methodologies used to predict nano (eco)-toxicological outcomes in nanotoxicology during the last decade. It provides a review of the sequenced steps involved in implementing an ML model, from data pre-processing, to model implementation, model validation, and applicability domain. The review gathers and presents the step-wise information on techniques and procedures of existing models that can be used readily to assemble new nanotoxicological in silico studies and accelerates the regulation of in silico tools in nanotoxicology. ML applications in nanotoxicology comprise an active and diverse collection of ongoing efforts, although it is still in their early steps toward a scientific accord, subsequent guidelines, and regulation adoption. This study is an important bookend to a decade of ML applications to nanotoxicology and serves as a useful guide to further in silico applications.

Quantitative structure-property relationship modeling of the C60 fullerene derivatives as electron acceptors of polymer solar cells: Elucidating the functional groups critical for device performance

Using the GUSAR 2013 program, we have performed a quantitative analysis of the "structure-power conversion efficiency (PCE)" on the series of 100 methano[60]fullerenes previously tested as acceptor components of bulk-heterojunction polymer organic solar cells (PSCs) utilizing the same donor polymer, viz. poly(3-hexylthiophene). Based on the MNA and QNA descriptors and self-consistent regression implemented in the program, six statistically significant consensus models for predicting the PCE values of the methano[60]fullerene-based PSCs have been constructed. The structural fragments of the fullerene compounds leading to an increase in the device performances are determined. Based on these structural descriptors, we have designed the three methano[60]fullerenes included in the training sets and characterized by poor optoelectrical properties is performed. As a result, two new compounds with potentially moderate efficiency have been proposed. This result opens opportunities of using the GUSAR 2013 program for modeling of the "structure-PCE" relationship for diverse compounds (not only fullerene derivatives).

Considerations of nano-QSAR/QSPR models for nanopesticide risk assessment within the European legislative framework

The European market for pesticides is currently legislated through the well-developed Regulation (EC) No. 1107/2009. This regulation promotes the competitiveness of European agriculture, recognizing the necessity of safe pesticides for human and animal health and the environment to protect crops against pests, diseases and weeds. In this sense, nanotechnology can provide a tremendous opportunity to achieve a more rational use of pesticides. However, the lack of information regarding nanopesticides and their fate and behavior in the environment and their effects on human and animal health is inhibiting rapid nanopesticide incorporation into European Union agriculture. This review analyzes the recent state of knowledge on nanopesticide risk assessment, highlighting the challenges that need to be overcame to accelerate the arrival of these new tools for plant protection to European agricultural professionals. Novel nano-Quantitative Structure-Activity/Structure-Property Relationship (nano-QSAR/QSPR) tools for risk assessment are analyzed, including modeling methods and validation procedures towards the potential of these computational instruments to meet the current requirements for authorization of nanoformulations. Future trends on these issues, of pressing importance within the context of the current European pesticide legislative framework, are also discussed. Standard protocols to make high-quality and well-described datasets for the series of related but differently sized nanoparticles/nanopesticides are required.

Development of in Silico Models for Predicting P-Glycoprotein Inhibitors Based on a Two-Step Approach for Feature Selection and Its Application to Chinese Herbal Medicine Screening

P-glycoprotein (P-gp) is regarded as an important factor in determining the ADMET (absorption, distribution, metabolism, elimination, and toxicity) characteristics of drugs and drug candidates. Successful prediction of P-gp inhibitors can thus lead to an improved understanding of the underlying mechanisms of both changes in the pharmacokinetics of drugs and drug-drug interactions. Therefore, there has been considerable interest in the development of in silico modeling of P-gp inhibitors in recent years. Considering that a large number of molecular descriptors are used to characterize diverse structural moleculars, efficient feature selection methods are required to extract the most informative predictors. In this work, we constructed an extensive available data set of 2428 molecules that includes 1518 P-gp inhibitors and 910 P-gp noninhibitors from multiple resources. Importantly, a two-step feature selection approach based on a genetic algorithm and a greedy forward-searching algorithm was employed to select the minimum set of the most informative descriptors that contribute to the prediction of P-gp inhibitors. To determine the best machine learning algorithm, 18 classifiers coupled with the feature selection method were compared. The top three best-performing models (flexible discriminant analysis, support vector machine, and random forest) and their ensemble model using respectively only 3, 9, 7, and 14 descriptors achieve an overall accuracy of 83.2%-86.7% for the training set containing 1040 compounds, an overall accuracy of 82.3%-85.5% for the test set containing 1039 compounds, and a prediction accuracy of 77.4%-79.9% for the external validation set containing 349 compounds. The models were further extensively validated by DrugBank database (1890 compounds). The proposed models are competitive with and in some cases better than other published models in terms of prediction accuracy and minimum number of descriptors. Applicability domain then was addressed by developing an ensemble classification model to obtain more reliable predictions. Finally, we employed these models as a virtual screening tool for identifying potential P-gp inhibitors in Traditional Chinese Medicine Systems Pharmacology (TCMSP) database containing a total of 13 051 unique compounds from 498 herbs, resulting in 875 potential P-gp inhibitors and 15 inhibitor-rich herbs. These predictions were partly supported by a literature search and are valuable not only to develop novel P-gp inhibitors from TCM in the early stages of drug development, but also to optimize the use of herbal remedies.

Theoretical Study on the Solvation of C60 Fullerene by Ionic Liquids II: DFT Analysis of the Interaction Mechanism

As a continuation of our previous work (J. Phys. Chem. B, 2014, 118, 11330) on the solvation of C60 by ionic liquids (ILs) using Molecular Dynamic simulations, this paper reports a systematic density functional theory (DFT) analysis on the interaction mechanism between C60 and 24 different ionic liquids (belonging to the imidazolium, piperazinium, and cholinium groups). Properties such as binding energies, charge distributions, intermolecular interactions, or electronic structure were analyzed as a function of the selected ILs. The stronger IL-C60 interactions would be related with π-π stacking between the C60 surface and anions such as salycilate ([SA]). Likewise, the electronic structure analysis pointed to a well-defined relationship between the energetics of IL-C60 systems and IL features. Therefore, ILs with deep HOMO energies as well as weak interaction between both ions would be a priori good candidates for C60 solvation. Although only short-range interactions are studied in the framework of DFT, this work provides useful information for the rational design of ILs that could exhibit suitable features as C60 solvents.