Machine Learning to Predict Enzyme-Substrate Interactions in Elucidation of Synthesis Pathways: A Review.

Enzyme-substrate interactions play a fundamental role in elucidating synthesis pathways and synthetic biology, as they allow for the understanding of important aspects of a reaction. Establishing the interaction experimentally is a slow and costly process, which is why this problem has been addressed using computational methods such as molecular dynamics, molecular docking, and Monte Carlo simulations. Nevertheless, this type of method tends to be computationally slow when dealing with a large search space. Therefore, in recent years, methods based on artificial intelligence, such as support vector machines, neural networks, or decision trees, have been implemented, significantly reducing the computing time and covering vast search spaces. These methods significantly reduce the computation time and cover broad search spaces, rapidly reducing the number of interacting candidates, as they allow repetitive processes to be automated and patterns to be extracted, are adaptable, and have the capacity to handle large amounts of data. This article analyzes these artificial intelligence-based approaches, presenting their common structure, advantages, disadvantages, limitations, challenges, and future perspectives.

QSPR predicting the vapor pressure of pesticides into high/low volatility classes.

In this work, the vapor pressure of pesticides is employed as an indicator of their volatility potential. Quantitative Structure-Property Relationship models are established to predict the classification of compounds according to their volatility, into the high and low binary classes separated by the 1-mPa limit. A large dataset of 1005 structurally diverse pesticides with known experimental vapor pressure data at 20 °C is compiled from the publicly available Pesticide Properties DataBase (PPDB) and used for model development. The freely available PaDEL-Descriptor and ISIDA/Fragmentor molecular descriptor programs provide a large number of 19,947 non-conformational molecular descriptors that are analyzed through multivariable linear regressions and the Replacement Method technique. Through the selection of appropriate molecular descriptors of the substructure fragment type and the use of different standard classification metrics of model's quality, the classification of the structure-property relationship achieves acceptable results for discerning between the high and low volatility classes. Finally, an application of the obtained QSPR model is performed to predict the classes for 504 pesticides not having experimentally measured vapor pressures.

Effect of Storage Time on the Physical, Chemical, and Rheological Properties of Blueberry Jam: Experimental Measurements and Artificial Neural Network Simulation.

Reversible data hiding (RDH) is crucial in modern data security, ensuring confidentiality and tamper-proofness in various industries like copyright protection, medical imaging, and digital forensics. As technology advances, RDH techniques become essential, but the trade-off between embedding capacity and visual quality must be heeded. In this paper, the relative correlation between the pixel's local complexity and its directional prediction error is employed to enhance an efficient RDH without using a location map. An embedding process based on multiple cumulative peak region localization (MCPRL) is proposed to hide information in the 3D-directional prediction error histogram with a lower local complexity value and avoid the underflow/overflow problems. The carrier image is divided into three color channels, and then each channel is split into two non-overlapping sets: blank and shadow. Two half-directional prediction errors (the blank set and the shadow set) are constructed to generate a full-directional prediction error for each color channel belonging to the host image. The local complexity value and directional prediction error are critical metrics in the proposed embedding process to improve security and robustness. By utilizing these metrics to construct a 3D stego-Blank Set, the 3D stego-shadow Set will be subsequently constructed using the 3D blank set. The proposed technique outperforms other state-of-the-art techniques in terms of embedding capacity, image quality, and robustness against attacks without an extra location map. The experimental results illustrate the effectiveness of the proposed method for various 3D RDH techniques.

Discovery of New 2-Phenylamino-3-acyl-1,4-naphthoquinones as Inhibitors of Cancer Cells Proliferation: Searching for Intra-Cellular Targets Playing a Role in Cancer Cells Survival.

A series of 2-phenylamino-3-acyl-1,4-naphtoquinones were evaluated regarding their in vitro antiproliferative activities using DU-145, MCF-7 and T24 cancer cells. Such activities were discussed in terms of molecular descriptors such as half-wave potentials, hydrophobicity and molar refractivity. Compounds 4 and 11 displayed the highest antiproliferative activity against the three cancer cells and were therefore further investigated. The in silico prediction of drug likeness, using pkCSM and SwissADME explorer online, shows that compound 11 is a suitable lead molecule to be developed. Moreover, the expressions of key genes were studied in DU-145 cancer cells. They include genes involved in apoptosis (Bcl-2), tumor metabolism regulation (mTOR), redox homeostasis (GSR), cell cycle regulation (CDC25A), cell cycle progression (TP53), epigenetic (HDAC4), cell-cell communication (CCN2) and inflammatory pathways (TNF). Compound 11 displays an interesting profile because among these genes, mTOR was significantly less expressed as compared to control conditions. Molecular docking shows that compound 11 has good affinity with mTOR, unraveling a potential inhibitory effect on this protein. Due to the key role of mTOR on tumor metabolism, we suggest that impaired DU-145 cells proliferation by compound 11 is caused by a reduced mTOR expression (less mTOR protein) and inhibitory activity on mTOR protein.

Chemoinformatic modelling of the antioxidant activity of phenolic compounds.

BACKGROUND: Antioxidants are chemicals used to protect foods from deterioration by neutralizing free radicals and inhibiting the oxidative process. One approach to investigate the antioxidant activity is to develop quantitative structure-activity relationships (QSARs). RESULTS: A curated database of 165 structurally heterogeneous phenolic compounds with the Trolox equivalent antioxidant capacity (TEAC) was developed. Molecular geometries were optimized by means of the GFN2-xTB semiempirical method and diverse molecular descriptors were obtained afterwards. For model development, V-WSP unsupervised variable reduction was used before performing the genetic algorithms-variable subset selection (GAs-VSS) to construct the best five-descriptor multiple linear regression model. The coefficient of determination and the root mean square error were used to measure the performance in calibration (R2 = 0.789 and RMSEC = 0.381), and test set prediction (Q2 = 0.748 and RMSEP = 0.416), along several cross-validation criteria. To thoroughly understand the TEAC prediction, a fully explained mechanism of action of the descriptors is provided. In addition, the applicability domain of the model defined a theoretical chemical space for reliable predictions of new phenolic compounds. CONCLUSION: This in silico model conforms to the five principles stated by the Organisation for Economic Co-operation and Development. The model might be useful for virtual screening of the antioxidant chemical space and for identifying the most potent molecules related to an experimental measurement of TEAC activity. In addition, the model could assist chemists working on computer-aided drug design for the synthesis of new targets with improved activity and potential uses in food science. © 2023 Society of Chemical Industry.

Development and optimization of stability-indicating method of ethinylestradiol, levonorgestrel, and their main impurities using quality by design approach.

The association of Ethinylestradiol 0.03 mg and Levonorgestrel 0.15 mg is a hormonal contraceptive that combines estrogen and progestogen. According to a bibliographic survey, these combined drugs present at least 18 known degradation products, which are required to control the potential impurities harmful to human health. The high number of impurities and the low concentrations of the active pharmaceutical ingredients (APIs) and their respective degradation products increase the complexity of the stability-indicating method development for this medicine. Thus, this work aimed to develop and optimize the stability-indicating method using the quality by design (QbD) approach and in-silico tools for application in samples of oral contraceptives sold in Brazil. The analysis samples were initially subjected to a forced degradation study through 7 days of exposure under acid and alkali hydrolysis, oxidative condition, and oxidation by metal ions. In addition to the chemical exposure, the sample was subjected to physical stress through 10 days of exposure under dry heat, moisture, and photolytic degradation. These exposure samples were analyzed in the development and optimization of chromatographic conditions. As a result, the developed method was able to separate 20 known substances, including the two APIs and their respective 18 degradation products, as well as unknown degradation products obtained by the forced degradation study. Finally, this stability-indicating method was successfully applied for comparative analysis of contraceptive drugs marketed in Brazil, newly purchased and subjected to accelerated stability condition at 40 °C and 75% RH over the 6-month period.

Reconocimiento de rutas biosintéticas para semioquímicos mediante técnicas de aprendizaje de máquina

En este trabajo consideramos 148 semioquímicos reportados para la familia Scarabaeidae, cuya estructura química fue caracterizada empleando un conjunto de 200 descriptores moleculares de cinco clases distintas. La selección de los descriptores más discriminantes se realizó con tres técnicas: análisis de componentes principales, por cada clase de descriptores, bosques aleatorios y Boruta-Shap, aplicados al total de descriptores. A pesar de que las tres técnicas son conceptualmente diferentes, seleccionan un número de descriptores similar de cada clase. Propusimos una combinación de técnicas de aprendizaje de máquina para buscar un patrón estructural en el conjunto de semioquímicos y posteriormente realizar la clasificación de estos. El patrón se estableció a partir de la alta pertenencia de un subconjunto de estos metabolitos a los grupos que fueron obtenidos por un método de agrupamiento basado en lógica difusa, C-means; el patrón descubierto corresponde a las rutas biosintéticas por las cuales se obtienen biológicamente. Esta primera clasificación se corroboró con el empleo de mapas autoorganizados de Kohonen. Para clasificar aquellos semioquímicos cuya pertenencia a una ruta no quedaba claramente definida, construimos dos modelos de perceptrones multicapa, los cuales tuvieron un desempeño aceptable.

In this work we consider 148 semiochemicals reported for the family Scarabaeidae, whose chemical structure was characterized using a set of 200 molecular descriptors from five different classes. The selection of the most discriminating descriptors was carried out with three different techniques: Principal Component Analysis, for each class of descriptors, Random Forests and Boruta-Shap, applied to the total of descriptors. Although the three techniques are conceptually different, they select a similar number of descriptors from each class. We proposed a combination of machine learning techniques to search for a structural pattern in the set of semiochemicals and then perform their classification. The pattern was established from the high belonging of a subset of these metabolites to the groups that were obtained by a grouping method based on fuzzy C-means logic; the discovered pattern corresponds to the biosynthetic pathway by which they are obtained biologically. This first classification was corroborated with Kohonen's self-organizing maps. To classify those semiochemicals whose belonging to a biosynthetic pathway was not clearly defined, we built two models of Multilayer Perceptrons which had an acceptable performance.

Neste trabalho consideramos 148 semioquímicos reportados para a família Scarabaeidae, cuja estrutura química foi caracterizada usando um conjunto de 200 descritores moleculares de 5 classes diferentes. A seleção dos descritores mais discriminantes foi realizada com três técnicas diferentes: Análise de Componentes Principais, para cada classe de descritores, Florestas Aleatórias e Boruta-Shap, aplicadas a todos os descritores. Embora as três técnicas sejam conceitualmente diferentes, elas selecionaram um número semelhante de descritores de cada classe. Nós propusemos uma combinação de técnicas de aprendizado de máquina para buscar um padrão estrutural no conjunto de semioquímicos e então realizar sua classificação. O padrão foi estabelecido a partir da alta pertinência de um subconjunto desses metabólitos aos grupos que foram obtidos por um método de agrupamento baseado em lógica fuzzy, C-means; o padrão descoberto corresponde às rotas biossintéticas pelas quais eles são obtidos biologicamente. Essa primeira classificação foi corroborada com o uso dos mapas auto-organizados de Kohonen. Para classificar os semioquímicos cuja pertença a uma rota não foi claramente definida, construímos dois modelos de Perceptrons Multicamadas que tiveram um desempenho aceitável.

Ferramenta in silico para desenvolvimento de métodos cromatográficos - aplicação em misturas de filtros solares / In silico tool for development of chromatographic methods - application in mixtures of sunscreensPalavras-chave em inglês

Planejamento de Experimentos (DoE) permite obter e explorar conhecimentos sobre inúmeros sistemas, facilitando a coleta de informações com reduzido número de experimentos. No entanto, DoE é restrito ao delineamento do desenho experimental. Para superar essa limitação e permitir uma previsão precisa dos tempos de retenção para uma seleção de filtros UV orgânicos sob diversas condições, usamos a Relação Quantitativa entre Estrutura e Retenção combinada com o método de Monte Carlo para desenvolver uma plataforma in silico capaz de prever o perfil cromatográfico de filtros UV orgânicos. Sete analitos foram usados para estabelecer o modelo de predição: benzofenona-3, avobenzona, ethilhexil triazona, octil dimetil PABA, metoxicinamato de octila, tinosorb® S e octocrileno. Os valores residuais obtidos no modelo de análise de regressão múltipla mostraram distribuição normal, homocedasticidade e independência. Os coeficientes de determinação (R2) e predição (R2 pred) foram de 99,82% e 99,71%, respectivamente. A plataforma in silico apresentou grande potencial para predição do perfil cromatográfico de filtros UV orgânicos, da coeluição de analitos, de seus parâmetros cromatográficos, além de permitir, sem experimentação, uma visão geral do comportamento de retenção de compostos sob diversas condições cromatográficas

Design of Experiments (DoE) allows obtaining and explorer knowledge about innumerous systems, facilitating the information collection with reduced number of experiments. However, DoE is restricted to the limited range which experimental design was delineated. In order to overcome this limitation and enable accurate prediction of retention times for a selection of organic UV filters under various conditions, we used the Quantitative Structure-Retention Relationships tool combined with Monte Carlo method to develop an in silico platform capable of predicting chromatographic profile of organic UV filters. Seven analytes were used to established to prediction model: benzophenone-3, butyl methoxydibenzoilmethane, ethylhexyl triazone, ethylhexyl dimetyl PABA, ethylhexyl methoxycinnamate, bisethylhexyloxyphenol methoxyphenyl triazine and octocrylene. Residual values obtained from multiple regression analysis model showed normal distribution, homoscedasticity, and independence. Determination (R2) and prediction (R2 pred) coefficients were found to be 99,82% and 99,71%, respectively. In silico platform presented great potential for predicting chromatographic profile of organic UV filters, analytes coelution, chromatographic parameters and allowing, without experimentation, an overview of retention behavior of compounds under various chromatographic conditions

QSAR models for insecticidal properties of plant essential oils on the housefly (Musca domestica L.).

The fumigant and topical activities exhibited by 27 plant-derived essentials oils (EOs) on adult M. domestica housefly are predicted through the Quantitative Structure-Activity Relationship (QSAR) theory. These molecular structure based calculations are performed on 253 structurally diverse compounds from the EOs, where the number of constituents in each essential oil mixture varies between 2 to 24. A large number of 86,048 non-conformational mixture descriptors are derived as linear combinations of the molecular descriptors of the EO components. Two strategies are compared for the mixture descriptor formulation, which consider or avoid the use of the chemical composition. The multivariable linear regression QSAR models of the present work are useful for fumigant and topical applications, describing predictive parallelisms for the insecticidal activity of the analysed complex mixtures.

Synthesis of new α-Aryl-α-tetralones and α-Fluoro-α-aryl-α-tetralones, preliminary antiproliferative evaluation on drug resistant cell lines and in silico prediction of ADMETox properties.

α-aryl-α-tetralones and α-fluoro-α-aryl-α-tetralones derivatives were synthesized by palladium catalyzed α-arylation reaction of α-tetralones and α-fluoro-α-tetralones, with bromoarenes in moderate to good yields. These compounds were evaluated for their in vitro anti-proliferative effects against human breast cancer and leukemia cell lines with diverse profiles of drug resistance. The most promising compounds, 3b, 3c, 8a and 8c, were effective on both neoplastic models. 3b and 8a induced higher toxicity on multidrug resistant cells and were able to avoid efflux by ABCB1 and ABCC1 transporters. Theoretical calculations of the physicochemical descriptors to predict ADMETox properties were favorable concerning Lipinski's rule of five, results that reflected on the low effects on non-tumor cells. Therefore, these compounds showed great potential for development of pharmaceutical agents against therapy refractory cancers.

Mathematical Models Generated for the Prediction of Corrosion Inhibition Using Different Theoretical Chemistry Simulations.

The use of corrosion inhibitors is an important method to retard the process of metallic attack by corrosion. The construction of mathematical models from theoretical-computational and experimental data obtained for different molecules is one of the most attractive alternatives in the analysis of corrosion prevention, whose objective is to define those molecular characteristics that are common in high-performance corrosion inhibitors. This review includes data of corrosion inhibitors evaluated in different media, the most commonly studied molecular descriptors, and some examples of mathematical models generated by different researchers.

Smoothed Spherical Truncation based on Fuzzy Membership Functions: Application to the Molecular Encoding.

A novel spherical truncation method, based on fuzzy membership functions, is introduced to truncate interatomic (or interaminoacid) relations according to smoothing values computed from fuzzy membership degrees. In this method, the molecules are circumscribed into a sphere, so that the geometric centers of the molecules are the centers of the spheres. The fuzzy membership degree of each atom (or aminoacid) is computed from its distance with respect to the geometric center of the molecule, by using a fuzzy membership function. So, the smoothing value to be applied in the truncation of a relation (or interaction) is computed by averaging the fuzzy membership degrees of the atoms (or aminoacids) involved in the relation. This truncation method is rather different from the existing ones, at considering the geometric center for the whole molecule and not only for atom-groups, as well as for using fuzzy membership functions to compute the smoothing values. A variability study on a set comprised of 20,469 compounds (15,050 drug-like compounds, 2994 drugs approved, 880 natural products from African sources, and 1545 plant-derived natural compounds exhibiting anti-cancerous activity) demonstrated that the truncation method proposed allows to determine molecular encodings with better ability for discriminating among structurally different molecules than the encodings obtained without applying truncation or applying non-fuzzy truncation functions. Moreover, a principal component analysis revealed that orthogonal chemical information of the molecules is encoded by using the method proposed. Lastly, a modeling study proved that the truncation method improves the modeling ability of existing geometric molecular descriptors, at allowing to develop more robust models than the ones built only using non-truncated descriptors. In this sense, a comparison and statistical assessment were performed on eight chemical datasets. As a result, the models based on the truncated molecular encodings yielded statistically better results than 12 procedures considered from the literature. It can thus be stated that the proposed truncation method is a relevant strategy for obtaining better molecular encodings, which will be ultimately useful in enhancing the modeling ability of existing encodings both on small-to-medium size molecules and biomacromolecules. © 2019 Wiley Periodicals, Inc.

Modeling the Antileukemia Activity of Ellipticine-Related Compounds: QSAR and Molecular Docking Study.

The antileukemia cancer activity of organic compounds analogous to ellipticine representes a critical endpoint in the understanding of this dramatic disease. A molecular modeling simulation on a dataset of 23 compounds, all of which comply with Lipinski's rules and have a structure analogous to ellipticine, was performed using the quantitative structure activity relationship (QSAR) technique, followed by a detailed docking study on three different proteins significantly involved in this disease (PDB IDs: SYK, PI3K and BTK). As a result, a model with only four descriptors (HOMO, softness, AC1RABAMBID, and TS1KFABMID) was found to be robust enough for prediction of the antileukemia activity of the compounds studied in this work, with an R2 of 0.899 and Q2 of 0.730. A favorable interaction between the compounds and their target proteins was found in all cases; in particular, compounds 9 and 22 showed high activity and binding free energy values of around -10 kcal/mol. Theses compounds were evaluated in detail based on their molecular structure, and some modifications are suggested herein to enhance their biological activity. In particular, compounds 22_1, 22_2, 9_1, and 9_2 are indicated as possible new, potent ellipticine derivatives to be synthesized and biologically tested.

QSAR studies of the antioxidant activity of anthocyanins.

Through experimental information available from antioxidant assays of seventeen anthocyanins, and six common anthocyanidins, quantitative structure-activity relationships (QSAR) have been established in the present work. The antioxidant bioactivity has been predicted in three different lipid environments: emulsified and bulk oil (methyl linoleate) (in vitro tests) at concentrations of 50 and 250 µM, and 50 µM of the inhibitor, respectively, and in human LDL (low-density lipoprotein; "bad cholesterol") (ex vivo test) at concentrations of 2.5, 10, and 25 µM of the inhibitor. Radical scavenging activity was predicted in the assay with the 1,1-diphenyl-2-picrylhydrazyl radical (DPPH·). The QSAR models developed for each test and concentration used allowed to obtain prospective information on the constitutional and topological molecular characteristics for anthocyanin/anthocyanidin compounds. Therefore, the antioxidant activity was predicted for twenty-one compounds with unknown experimental values, leading for some of them to a favorable predicted bioactivity.

Quantitative structure-activity relationship models for compounds with anticonvulsant activity.

Introduction: Third-generation antiepileptic drugs have seemingly failed to improve the global figures of seizure control and can still be regarded as symptomatic treatments. Quantitative structure-activity relationships (QSAR) can be used to guide hit-to-lead and lead optimization projects and applied to the large-scale virtual screening of chemical libraries. Areas covered: In this review, the authors cover reports on QSAR models related to antiepileptic drugs and drug targets in epilepsy, analyzing whether they refer to classic or non-classic QSAR and if they apply QSAR as a descriptive or predictive approach, among other considerations. The article finally focuses on a more detailed discussion of those predictive studies which include some sort of experimental validation, i.e. papers in which the reported models have been used to identify novel active compounds which have been tested in vitro and/or in vivo. Expert opinion: There are significant opportunities to apply the QSAR methodology to assist the discovery of more efficacious antiepileptic drugs. Considering the intrinsic complexity of the disorder, such applications should focus on state-of-the-art approximations (e.g. systemic, multi-target and multi-scale QSAR as well as ensemble and deep learning) and modeling the effects on novel drug targets and modern screening tools.

Berberine as Source of Antiproliferative Hybrid Compounds: In Vitro Antiproliferative Activity and Quantitative Structure-activity Relationship.

BACKGROUND: Despite advances for cancer treatment, it still remains a major worldwide public health problem. Compounds derived from natural sources are important alternatives to combat this mortal disease. Berberine is an isoquinoline alkaloid with a wide variety of pharmacological properties, including antiproliferative activity. Previously, we have found that fatty acids also show antiproliferative activity against cancer cell lines.. OBJECTIVE: To combine berberine and fatty acids, or carboxylic acids, in order to improve their antiproliferative properties. METHODS: We synthetized six new hybrid derivatives through a simple methylenedioxy group-cleavage method followed by the reaction with fatty acids, or carboxylic acids. The structure of the compounds was elucidated by IR, NMR and HRMS. The in vitro antiproliferative activity against four human cancer cell lines (HeLa, A-549, PC-3 and LS-180) and one normal cell line (ARPE-19), was evaluated by the MTT method. Chemical structures were drawn using SPARTAN '08 software and the conformational analysis was carried out with a molecular mechanic level of theory and the SYBIL force field. All molecular structures were subjected to geometrical optimization at the semi-empirical method PM3. Molecular descriptors were calculated using DRAGON 5.4 and SPARTAN ´08 programs. RESULTS: The geranic acid and berberine hybrid compound (6) improved the antiproliferative activity shown by natural berberine, even more than the 16- to 18-carbon atoms fatty acids. Compound 6 showed IC50 values of 2.40 ± 0.60, 1.5 ± 0.24, 5.85 ± 1.07 and 5.44 ± 0.24 µM, against HeLa, A-549, PC-3 and LS-180 human cancer cell lines, respectively. Using this information, we performed a quantitative structure-activity relationship (QSAR) of the hybrid molecules and found that the molecular descriptors associated with the antiproliferative activity are: hydrophobic constant associated with substituents (π(A) = 6.5), molecular volume descriptor (CPKvolume≈ 700 Å3), EHOMO, number of rotatable bonds (RBN) and number of 6-membered rings (nR06). CONCLUSION: The methylendioxy and methoxyl groups in berberine are important for the antiproliferative activity shown by its derivatives. Better results in antiproliferative activity were obtained in compound 6 with the prenyl moiety. The QSAR indicates that the molecular descriptors which associated positively with the antiproliferative activity are: hydrophobic constant associated with substituents (π(A) = 6.5), molecular volume descriptor (CPKvolume≈ 700 Å3) and EHOMO. This research gave the basis for the design and preparation of new, easily afforded molecules derived from berberine and carboxylic acids, with improved antiproliferative activity.

QSAR-Based Virtual Screening: Advances and Applications in Drug Discovery.

Virtual screening (VS) has emerged in drug discovery as a powerful computational approach to screen large libraries of small molecules for new hits with desired properties that can then be tested experimentally. Similar to other computational approaches, VS intention is not to replace in vitro or in vivo assays, but to speed up the discovery process, to reduce the number of candidates to be tested experimentally, and to rationalize their choice. Moreover, VS has become very popular in pharmaceutical companies and academic organizations due to its time-, cost-, resources-, and labor-saving. Among the VS approaches, quantitative structure-activity relationship (QSAR) analysis is the most powerful method due to its high and fast throughput and good hit rate. As the first preliminary step of a QSAR model development, relevant chemogenomics data are collected from databases and the literature. Then, chemical descriptors are calculated on different levels of representation of molecular structure, ranging from 1D to nD, and then correlated with the biological property using machine learning techniques. Once developed and validated, QSAR models are applied to predict the biological property of novel compounds. Although the experimental testing of computational hits is not an inherent part of QSAR methodology, it is highly desired and should be performed as an ultimate validation of developed models. In this mini-review, we summarize and critically analyze the recent trends of QSAR-based VS in drug discovery and demonstrate successful applications in identifying perspective compounds with desired properties. Moreover, we provide some recommendations about the best practices for QSAR-based VS along with the future perspectives of this approach.

Classical QSAR and Docking Simulation of 4-Pyridone Derivatives for Their Antimalarial Activity.

In this work, the minimum energy structures of 22 4-pyridone derivatives have been optimized at Density Functional Theory level, and several quantum molecular, including electronic and thermodynamic descriptors, were computed for these substrates in order to obtain a statistical and meaningful QSAR equation. In this sense, by using multiple linear regressions, five mathematical models have been obtained. The best model with only four descriptors (r² = 0.86, Q² = 0.92, S.E.P = 0.38) was validated by the leave-one-out cross-validation method. The antimalarial activity can be explained by the combination of the four mentioned descriptors e.g., electronic potential, dipolar momentum, partition coefficient and molar refractivity. The statistical parameters of this model suggest that it is robust enough to predict the antimalarial activity of new possible compounds; consequently, three small chemical modifications into the structural core of these compounds were performed specifically on the most active compound of the series (compound 13). These three new suggested compounds were leveled as 13A, 13B and 13C, and the predicted biological antimalarial activity is 0.02 µM, 0.03 µM, and 0.07 µM, respectively. In order to complement these results focused on the possible action mechanism of the substrates, a docking simulation was included for these new structures as well as for the compound 13 and the docking scores (binding affinity) obtained for the interaction of these substrates with the cytochrome bc1, were -7.5, -7.2, -6.9 and -7.5 kcal/mol for 13A, 13B, 13C and compound 13, respectively, which suggests that these compounds are good candidates for its biological application in this illness.

An In Silico Study of the Antioxidant Ability for Two Caffeine Analogs Using Molecular Docking and Quantum Chemical Methods.

The antioxidant activity of molecules constitutes an important factor for the regulation of redox homeostasis and reduction of the oxidative stress. Cells affected by oxidative stress can undergo genetic alteration, causing structural changes and promoting the onset of chronic diseases, such as cancer. We have performed an in silico study to evaluate the antioxidant potential of two molecules of the zinc database: ZINC08706191 (Z91) and ZINC08992920 (Z20). Molecular docking, quantum chemical calculations (HF/6-31G**) and Pearson's correlation have been performed. Molecular docking results of Z91 and Z20 showed both the lower binding affinity (BA) and inhibition constant (Ki) values for the receptor-ligand interactions in the three tested enzymes (cytochrome P450-CP450, myeloperoxidase-MP and NADPH oxidase-NO) than the control molecules (5-fluorouracil-FLU, melatonin-MEL and dextromethorphan-DEX, for each receptor respectively). Molecular descriptors were correlated with Ki and strong correlations were observed for the CP450, MP and NO receptors. These and other results attest the significant antioxidant ability of Z91 and Z20, that may be indicated for further analyses in relation to the control of oxidative stress and as possible antioxidant agents to be used in the pharmaceutical industry.