A novel strategy for designing the magic shotguns for distantly related target pairs.

Due to its promising capacity in improving drug efficacy, polypharmacology has emerged to be a new theme in the drug discovery of complex disease. In the process of novel multi-target drugs (MTDs) discovery, in silico strategies come to be quite essential for the advantage of high throughput and low cost. However, current researchers mostly aim at typical closely related target pairs. Because of the intricate pathogenesis networks of complex diseases, many distantly related targets are found to play crucial role in synergistic treatment. Therefore, an innovational method to develop drugs which could simultaneously target distantly related target pairs is of utmost importance. At the same time, reducing the false discovery rate in the design of MTDs remains to be the daunting technological difficulty. In this research, effective small molecule clustering in the positive dataset, together with a putative negative dataset generation strategy, was adopted in the process of model constructions. Through comprehensive assessment on 10 target pairs with hierarchical similarity-levels, the proposed strategy turned out to reduce the false discovery rate successfully. Constructed model types with much smaller numbers of inhibitor molecules gained considerable yields and showed better false-hit controllability than before. To further evaluate the generalization ability, an in-depth assessment of high-throughput virtual screening on ChEMBL database was conducted. As a result, this novel strategy could hierarchically improve the enrichment factors for each target pair (especially for those distantly related/unrelated target pairs), corresponding to target pair similarity-levels.

A Review of Machine Learning and QSAR/QSPR Predictions for Complexes of Organic Molecules with Cyclodextrins.

Cyclodextrins are macrocyclic rings composed of glucose residues. Due to their remarkable structural properties, they can form host-guest inclusion complexes, which is why they are frequently used in the pharmaceutical, cosmetic, and food industries, as well as in environmental and analytical chemistry. This review presents the reports from 2011 to 2023 on the quantitative structure-activity/property relationship (QSAR/QSPR) approach, which is primarily employed to predict the thermodynamic stability of inclusion complexes. This article extensively discusses the significant developments related to the size of available experimental data, the available sets of descriptors, and the machine learning (ML) algorithms used, such as support vector machines, random forests, artificial neural networks, and gradient boosting. As QSAR/QPR analysis only requires molecular structures of guests and experimental values of stability constants, this approach may be particularly useful for predicting these values for complexes with randomly substituted cyclodextrins, as well as for estimating their dependence on pH. This work proposes solutions on how to effectively use this knowledge, which is especially important for researchers who will deal with this topic in the future. This review also presents other applications of ML in relation to CD complexes, including the prediction of physicochemical properties of CD complexes, the development of analytical methods based on complexation with CDs, and the optimisation of experimental conditions for the preparation of the complexes.

Amino Acid Transporter as a Potential Carrier Protein for the Root-to-Shoot Translocation of Polybrominated Diphenyl Ethers in Rice.

As typical persistent organic pollutants, polybrominated diphenyl ethers (PBDEs) tend to accumulate in edible parts of rice, posing great ecological and health risks. The translocation of PBDEs from underground to aboveground parts of rice is a crucial procedure to determine the final bioaccumulation level. Herein, this study aimed to identify the transporter proteins for PBDEs in rice plants in order to strengthen our understanding of the bioaccumulation mechanism and the potential prevention strategy of the PBDE risk. Similar time-dependent patterns were observed among the root-to-shoot translocation factors (TFs) of PBDEs, the expression of lysine histidine transporter (LHT) protein, and the relative levels of LHT substrates (phenylalanine or tyrosine), implying the potential co-transport of PBDEs, phenylalanine, and tyrosine by the carrier LHT. Fluorescence spectra and circular dichroism showed that PBDE congeners interfered with LHT via static fluorescence quenching and changes in the protein's secondary structure. The in vitro sorption fraction of LHT to PBDEs, as revealed by sorption equilibrium analysis, was comparable to the in vivo TF values. Knockout of OsLHT1 in rice using CRISPR/Cas9 technology caused a 48.2-78.4% decrease in PBDE translocation. Molecular docking simulation suggested that PBDEs, phenylalanine, and tyrosine were inserted into the same ligand-binding cavity of LHT, substantiating the potential carrier role of LHT for PBDEs from a conformational perspective. Quantitative structure activity relationship analysis demonstrated that the ether-bond oxygen and the carbons at the site 4 and 4' of PBDE molecules are significant determinants of the binding affinity with the LHT protein and in vivo translocation of PBDEs. In summary, this study discovered that LHT acts as the cellular carrier for PBDEs and offered a comprehensive molecular explanation for the bioaccumulation and translocation of PBDEs in rice plants, covering both biological and chemical perspectives. These findings fill in a knowledge gap on the endogenous transporter proteins for exogenous organic pollutants.

Physiological variables in machine learning QSARs allow for both cross-chemical and cross-species predictions.

A major challenge in ecological risk assessment is estimating chemical-induced effects across taxa without species-specific testing. Where ecotoxicological data may be more challenging to gather, information on species physiology is more available for a broad range of taxa. Physiology is known to drive species sensitivity but understanding about the relative contribution of specific underlying processes is still elusive. Consequently, there remains a need to understand which physiological processes lead to differences in species sensitivity. The objective of our study was to utilize existing knowledge about organismal physiology to both understand and predict differences in species sensitivity. Machine learning models were trained to predict chemical- and species-specific endpoints as a function of both chemical fingerprints/descriptors and physiological properties represented by dynamic energy budget (DEB) parameters. We found that random forest models were able to predict chemical- and species-specific endpoints, and that DEB parameters were relatively important in the models, particularly for invertebrates. Our approach illuminates how physiological properties may drive species sensitivity, which will allow more realistic predictions of effects across species without the need for additional animal testing.

Screening and Analysis of Possible Drugs Binding to PDGFRα: A Molecular Modeling Study.

The platelet-derived growth factor receptor (PDGFR) is a membrane tyrosine kinase receptor involved in several metabolic pathways, not only physiological but also pathological, as in tumor progression, immune-mediated diseases, and viral diseases. Considering this macromolecule as a druggable target for modulation/inhibition of these conditions, the aim of this work was to find new ligands or new information to design novel effective drugs. We performed an initial interaction screening with the human intracellular PDGFRα of about 7200 drugs and natural compounds contained in 5 independent databases/libraries implemented in the MTiOpenScreen web server. After the selection of 27 compounds, a structural analysis of the obtained complexes was performed. Three-dimensional quantitative structure-activity relationship (3D-QSAR) and absorption, distribution, metabolism, excretion, and toxicity (ADMET) analyses were also performed to understand the physicochemical properties of identified compounds to increase affinity and selectivity for PDGFRα. Among these 27 compounds, the drugs Bafetinib, Radotinib, Flumatinib, and Imatinib showed higher affinity for this tyrosine kinase receptor, lying in the nanomolar order, while the natural products included in this group, such as curcumin, luteolin, and epigallocatechin gallate (EGCG), showed sub-micromolar affinities. Although experimental studies are mandatory to fully understand the mechanisms behind PDGFRα inhibitors, the structural information obtained through this study could provide useful insight into the future development of more effective and targeted treatments for PDGFRα-related diseases, such as cancer and fibrosis.

Construction of a prediction model for drug removal rate in hemodialysis based on chemical structures.

In designing drug dosing for hemodialysis patients, the removal rate (RR) of the drug by hemodialysis is important. However, acquiring the RR is difficult, and there is a need for an estimation method that can be used in clinical settings. In this study, the RR predictive model was constructed using the RR of known drugs by quantitative structure-activity relationship (QSAR) analysis. Drugs were divided into a model construction drug set (75%) and a model validation drug set (25%). The RR was collected from 143 medicines. The objective variable (RR) and chemical structural characteristics (descriptors) of the drug (explanatory variable) were used to construct a prediction model using partial least squares (PLS) regression and artificial neural network (ANN) analyses. The determination coefficients in the PLS and ANN methods were 0.586 and 0.721 for the model validation drug set, respectively. QSAR analysis successfully constructed dialysis RR prediction models that were comparable or superior to those using pharmacokinetic parameters. Considering that the RR dataset contains potential errors, we believe that this study has achieved the most reliable RR prediction accuracy currently available. These predictive RR models can be achieved using only the chemical structure of the drug. This model is expected to be applied at the time of hemodialysis.

Building 2D classification models and 3D CoMSIA models on small-molecule inhibitors of both wild-type and T790M/L858R double-mutant EGFR.

Epidermal growth factor receptor (EGFR) has received widespread attention because it is an important target for anticancer drug design. Mutations in the EGFR, especially the T790M/L858R double mutation, have made cancer treatment more difficult. We herein built the structure-activity relationship models of small-molecule inhibitors on wild-type and T790M/L858R double-mutant EGFR with a whole dataset of 379 compounds. For 2D classification models, we used ECFP4 fingerprints to build support vector machine and random forest models and used SMILES to build self-attention recurrent neural network models. Each of all six models resulted in an accuracy of above 0.87 and the Matthews correlation coefficient value of above 0.76 on the test set, respectively. We concluded that inhibitors containing anilinoquinoline and methoxy or fluoro phenyl are highly active against wild EGFR. Substructures such as anilinopyrimidine, acrylamide, amino phenyl, methoxy phenyl, and thienopyrimidinyl amide appeared more in highly active inhibitors against double-mutant EGFR. We also used self-organizing map to cluster the inhibitors into six subsets based on ECFP4 fingerprints and analyzed the activity characteristics of different scaffolds in each subset. Among them, three datasets, which are based on pteridin, anilinopyrimidine, and anilinoquinoline scaffold, were selected to build 3D comparative molecular similarity analysis models individually. Models with the leave-one-out coefficient of determination (q2) above 0.65 were selected, and five descriptor types (steric, electrostatic, hydrophobic, donor, and acceptor) were used to study the effects of side chains of inhibitors on the activity against wild-type and mutant-type EGFR.

Predicting Antifouling Activity and Acetylcholinesterase Inhibition of Marine-Derived Compounds Using a Computer-Aided Drug Design Approach.

Biofouling is the undesirable growth of micro- and macro-organisms on artificial water-immersed surfaces, which results in high costs for the prevention and maintenance of this process (billion €/year) for aquaculture, shipping and other industries that rely on coastal and off-shore infrastructure. To date, there are still no sustainable, economical and environmentally safe solutions to overcome this challenging phenomenon. A computer-aided drug design (CADD) approach comprising ligand- and structure-based methods was explored for predicting the antifouling activities of marine natural products (MNPs). In the CADD ligand-based method, 141 organic molecules extracted from the ChEMBL database and literature with antifouling screening data were used to build the quantitative structure-activity relationship (QSAR) classification model. An overall predictive accuracy score of up to 71% was achieved with the best QSAR model for external and internal validation using test and training sets. A virtual screening campaign of 14,492 MNPs from Encinar's website and 14 MNPs that are currently in the clinical pipeline was also carried out using the best QSAR model developed. In the CADD structure-based approach, the 125 MNPs that were selected by the QSAR approach were used in molecular docking experiments against the acetylcholinesterase enzyme. Overall, 16 MNPs were proposed as the most promising marine drug-like leads as antifouling agents, e.g., macrocyclic lactam, macrocyclic alkaloids, indole and pyridine derivatives.

Quantitative structure-activity relationship for the oxidation of organic contaminants by peracetic acid using GA-MLR method.

Peracetic acid (PAA) is considered as an effective and powerful oxidant for eliminating organic contaminants in wastewater treatment. The second-order rate constant (kapp) for the reaction of PAA with organic contaminants is practically important for evaluating their removal efficiency in wastewater treatment, but only limited numbers of kapp values are available. In this study, 70 organic compounds with various structures were selected, and the kapp of PAA with each organic compound was used to develop two quantitative structure-activity relationship (QSAR) models based on three kinds of descriptors including constitutional, quantum chemical, and the PaDEL descriptors. The genetic algorithm (GA) was applied to select the molecular descriptors, then the models developed by multiple linear regression (MLR). The most important descriptors that explain the reactivity of organic compounds with PAA are the EHOMO for the model with the constitutional and quantum chemical descriptors. The maxHdsCH and minHdCH2 are two most important descriptors for the model with only PaDEL descriptors. The developed models can be used to predict kapp for a wide range of organic contaminants. The accuracy of the developed models was proved by the internal, external validation and the Y-scrambling technique. The developed QSAR models using the GA-MLR method can be used as a screening tool for predicting the elimination of organic contaminants by PAA and increasing the understanding of chemical pollutant fate.

Assisting Multitargeted Ligand Affinity Prediction of Receptor Tyrosine Kinases Associated Nonsmall Cell Lung Cancer Treatment with Multitasking Principal Neighborhood Aggregation.

A multitargeted therapeutic approach with hybrid drugs is a promising strategy to enhance anticancer efficiency and overcome drug resistance in nonsmall cell lung cancer (NSCLC) treatment. Estimating affinities of small molecules against targets of interest typically proceeds as a preliminary action for recent drug discovery in the pharmaceutical industry. In this investigation, we employed machine learning models to provide a computationally affordable means for computer-aided screening to accelerate the discovery of potential drug compounds. In particular, we introduced a quantitative structure-activity-relationship (QSAR)-based multitask learning model to facilitate an in silico screening system of multitargeted drug development. Our method combines a recently developed graph-based neural network architecture, principal neighborhood aggregation (PNA), with a descriptor-based deep neural network supporting synergistic utilization of molecular graph and fingerprint features. The model was generated by more than ten-thousands affinity-reported ligands of seven crucial receptor tyrosine kinases in NSCLC from two public data sources. As a result, our multitask model demonstrated better performance than all other benchmark models, as well as achieving satisfying predictive ability regarding applicable QSAR criteria for most tasks within the model's applicability. Since our model could potentially be a screening tool for practical use, we have provided a model implementation platform with a tutorial that is freely accessible hence, advising the first move in a long journey of cancer drug development.

Synthesis and biological evaluation of 3-styrylchromone derivatives as selective monoamine oxidase B inhibitors.

A series of 3-styrylchromone derivatives was synthesized and evaluated for monoamine oxidase (MAO) A and B inhibitory activities. Most of all derivatives inhibited MAO-B selectively, except compound 21. Compound 19, which had a methoxy group at R2 on the chromone ring and chlorine at R4 on phenyl ring, potently inhibited MAO-B, with an IC50 value of 2.2 nM. Compound 1 showed the highest MAO-B selectivity, with a selectivity index of >3700. Further analysis of these compounds indicated that compounds 1 and 19 were reversible and mixed-type MAO-B inhibitors, suggesting that their mode of action may be through tight-binding inhibition to MAO-B. Quantitative structure-activity relationship (QSAR) analyses of the 3-styrylchromone derivatives were conducted using their pIC50 values, through Molecular Operating Environment (MOE) and Dragon. There were 1796 descriptors of MAO-B inhibitory activity, which showed significant correlations (P < 0.05). Further investigation of the 3-styrylchromone structures as useful scaffolds was performed through three-dimensional-QSAR studies using AutoGPA, which is based on the molecular field analysis algorithm using MOE. The MAO-B inhibitory activity model constructed using pIC50 value index exhibited a determination coefficients (R2) of 0.972 and a Leave-One-Out cross-validated determination coefficients (Q2) of 0.914. These data suggest that the 3-styrylchromone derivatives assessed herein may be suitable for the design and development of novel MAO inhibitors.

A novel artificial intelligence protocol to investigate potential leads for diabetes mellitus.

Dipeptidyl peptidase-4 (DPP4) is highly participated in regulating diabetes mellitus (DM), and inhibitors of DPP4 may act as potential DM drugs. Therefore, we performed a novel artificial intelligence (AI) protocol to screen and validate the potential inhibitors from Traditional Chinese Medicine Database. The potent top 10 compounds were selected as candidates by Dock Score. In order to further screen the candidates, we used numbers of machine learning regression models containing support vector machines, bagging, random forest and other regression algorithms, as well as deep neural network models to predict the activity of the candidates. In addition, as a traditional method, 2D QSAR (multiple linear regression) and 3D QSAR methods are also applied. The AI methods got a better performance than the traditional 2D QSAR method. Moreover, we also built a framework composed of deep neural networks and transformer to predict the binding affinity of candidates and DPP4. Artificial intelligence methods and QSAR models illustrated the compound, 2007_4105, was a potent inhibitor. The 2007_4105 compound was finally validated by molecular dynamics simulations. Combining all the models and algorithms constructed and the results, Hypecoum leptocarpum might be a potential and effective medicine herb for the treatment of DM.

Polarizability: a promising descriptor to study chemical-biological interactions.

Recently, we have defined atomic polarizability, a Conceptual Density Functional Theory (CDFT)-based reactivity descriptor, through an empirical method. Though the method is empirical, it is competent enough to meet the criteria of periodic descriptors and exhibit relativistic effect. Since the atomic data are very accurate, we have applied them to determine molecular polarizability. Molecular polarizability is an electronic parameter and has an impact on chemical-biological interactions. Thus, it plays a pivotal role in explaining such interactions through Structure Activity Relationships (SAR). In the present work, we have explored the application of polarizability in the real field through investigation of chemical-biological interactions in terms of molecular polarizability. A Quantitative Structure-Activity Relationship (QSAR) model is constructed to account for electronic effects owing to polarizability in ligand-substrate interactions. The study involves the prediction of various biological activities in terms of minimum block concentration, relative biological response, inhibitory growth concentration or binding affinity. Superior results are presented for the predicted and observed activities which support the accuracy of the proposed polarizability-QSAR model. Further, the results are considered from a biological viewpoint in order to understand the mechanism of interactions. The study is performed to explore the efficacy of the computational model based on newly proposed polarizability and not to establish the finest QSAR. For future studies, it is suggested that the descriptor polarizability should be contrasted with the use of other drug-like descriptors.

QSAR study of unsymmetrical aromatic disulfides as potent avian SARS-CoV main protease inhibitors using quantum chemical descriptors and statistical methods.

In silico research was executed on forty unsymmetrical aromatic disulfide derivatives as inhibitors of the SARS Coronavirus (SARS-CoV-1). Density functional theory (DFT) calculation with B3LYP functional employing 6-311 â€‹+ â€‹G(d,p) basis set was used to calculate quantum chemical descriptors. Topological, physicochemical and thermodynamic parameters were calculated using ChemOffice software. The dataset was divided randomly into training and test sets consisting of 32 and 8 compounds, respectively. In attempt to explore the structural requirements for bioactives molecules with significant anti-SARS-CoV activity, we have built valid and robust statistics models using QSAR approach. Hundred linear pentavariate and quadrivariate models were established by changing training set compounds and further applied in test set to calculate predicted IC50 values of compounds. Both built models were individually validated internally as well as externally along with Y-Randomization according to the OECD principles for the validation of QSAR model and the model acceptance criteria of Golbraikh and Tropsha's. Model 34 is chosen with higher values of R2, R2 test and Q2cv (R2 â€‹= â€‹0.838, R2 test â€‹= â€‹0.735, Q2 cv â€‹= â€‹0.757). It is very important to notice that anti-SARS-CoV main protease of these compounds appear to be mainly governed by five descriptors, i.e. highest occupied molecular orbital energy (EHOMO), energy of molecular orbital below HOMO energy (EHOMO-1), Balaban index (BI), bond length between the two sulfur atoms (S1S2) and bond length between sulfur atom and benzene ring (S2Bnz). Here the possible action mechanism of these compounds was analyzed and discussed, in particular, important structural requirements for great SARS-CoV main protease inhibitor will be by substituting disulfides with smaller size electron withdrawing groups. Based on the best proposed QSAR model, some new compounds with higher SARS-CoV inhibitors activities have been designed. Further, in silico prediction studies on ADMET pharmacokinetics properties were conducted.

Combined Toxicity of Metal Nanoparticles: Comparison of Individual and Mixture Particles Effect.

Toxicity of metal nanoparticles (NPs) are closely associated with increasing intracellular reactive oxygen species (ROS) and the levels of pro-inflammatory mediators. However, NP interactions and surface complexation reactions alter the original toxicity of individual NPs. To date, toxicity studies on NPs have mostly been focused on individual NPs instead of the combination of several species. It is expected that the amount of industrial and highway-acquired NPs released into the environment will further increase in the near future. This raises the possibility that various types of NPs could be found in the same medium, thereby, the adverse effects of each NP either could be potentiated, inhibited or remain unaffected by the presence of the other NPs. After uptake of NPs into the human body from various routes, protein kinases pathways mediate their toxicities. In this context, family of mitogen-activated protein kinases (MAPKs) is mostly efficient. Despite each NP activates almost the same metabolic pathways, the toxicity induced by a single type of NP is different than the case of co-exposure to the combined NPs. The scantiness of toxicological data on NPs combinations displays difficulties to determine, if there is any risk associated with exposure to combined nanomaterials. Currently, in addition to mathematical analysis (Response surface methodology; RSM), the quantitative-structure-activity relationship (QSAR) is used to estimate the toxicity of various metal oxide NPs based on their physicochemical properties and levels applied. In this chapter, it is discussed whether the coexistence of multiple metal NPs alter the original toxicity of individual NP. Additionally, in the part of "Toxicity of diesel emission/exhaust particles (DEP)", the known individual toxicity of metal NPs within the DEP is compared with the data regarding toxicity of total DEP mixture.

Biomimetic Chromatographic Studies Combined with the Computational Approach to Investigate the Ability of Triterpenoid Saponins of Plant Origin to Cross the Blood-Brain Barrier.

Biomimetic (non-cell based in vitro) and computational (in silico) studies are commonly used as screening tests in laboratory practice in the first stages of an experiment on biologically active compounds (potential drugs) and constitute an important step in the research on the drug design process. The main aim of this study was to evaluate the ability of triterpenoid saponins of plant origin to cross the blood-brain barrier (BBB) using both computational methods, including QSAR methodology, and biomimetic chromatographic methods, i.e., High Performance Liquid Chromatography (HPLC) with Immobilized Artificial Membrane (IAM) and cholesterol (CHOL) stationary phases, as well as Bio-partitioning Micellar Chromatography (BMC). The tested compounds were as follows: arjunic acid (Terminalia arjuna), akebia saponin D (Akebia quinata), bacoside A (Bacopa monnieri) and platycodin D (Platycodon grandiflorum). The pharmacokinetic BBB parameters calculated in silico show that three of the four substances, i.e., arjunic acid, akebia saponin D, and bacoside A exhibit similar values of brain/plasma equilibration rate expressed as logPSFubrain (the average logPSFubrain: -5.03), whereas the logPSFubrain value for platycodin D is -9.0. Platycodin D also shows the highest value of the unbound fraction in the brain obtained using the examined compounds (0.98). In these studies, it was found out for the first time that the logarithm of the analyte-micelle association constant (logKMA) calculated based on Foley's equation can describe the passage of substances through the BBB. The most similar logBB values were obtained for hydrophilic platycodin D, applying both biomimetic and computational methods. All of the obtained logBB values and physicochemical parameters of the molecule indicate that platycodin D does not cross the BBB (the average logBB: -1.681), even though the in silico estimated value of the fraction unbound in plasma is relatively high (0.52). As far as it is known, this is the first paper that shows the applicability of biomimetic chromatographic methods in predicting the penetration of triterpenoid saponins through the BBB.

Synthesis, Biological Evaluation, and Molecular Modeling of Aza-Crown Ethers.

Synthetic and natural ionophores have been developed to catalyze ion transport and have been shown to exhibit a variety of biological effects. We synthesized 24 aza- and diaza-crown ethers containing adamantyl, adamantylalkyl, aminomethylbenzoyl, and ε-aminocaproyl substituents and analyzed their biological effects in vitro. Ten of the compounds (8, 10-17, and 21) increased intracellular calcium ([Ca2+]i) in human neutrophils, with the most potent being compound 15 (N,N'-bis[2-(1-adamantyl)acetyl]-4,10-diaza-15-crown-5), suggesting that these compounds could alter normal neutrophil [Ca2+]i flux. Indeed, a number of these compounds (i.e., 8, 10-17, and 21) inhibited [Ca2+]i flux in human neutrophils activated by N-formyl peptide (fMLF). Some of these compounds also inhibited chemotactic peptide-induced [Ca2+]i flux in HL60 cells transfected with N-formyl peptide receptor 1 or 2 (FPR1 or FPR2). In addition, several of the active compounds inhibited neutrophil reactive oxygen species production induced by phorbol 12-myristate 13-acetate (PMA) and neutrophil chemotaxis toward fMLF, as both of these processes are highly dependent on regulated [Ca2+]i flux. Quantum chemical calculations were performed on five structure-related diaza-crown ethers and their complexes with Ca2+, Na+, and K+ to obtain a set of molecular electronic properties and to correlate these properties with biological activity. According to density-functional theory (DFT) modeling, Ca2+ ions were more effectively bound by these compounds versus Na+ and K+. The DFT-optimized structures of the ligand-Ca2+ complexes and quantitative structure-activity relationship (QSAR) analysis showed that the carbonyl oxygen atoms of the N,N'-diacylated diaza-crown ethers participated in cation binding and could play an important role in Ca2+ transfer. Thus, our modeling experiments provide a molecular basis to explain at least part of the ionophore mechanism of biological action of aza-crown ethers.

Binding Free Energy (BFE) Calculations and Quantitative Structure-Activity Relationship (QSAR) Analysis of Schistosoma mansoni Histone Deacetylase 8 (smHDAC8) Inhibitors.

Histone-modifying proteins have been identified as promising targets to treat several diseases including cancer and parasitic ailments. In silico methods have been incorporated within a variety of drug discovery programs to facilitate the identification and development of novel lead compounds. In this study, we explore the binding modes of a series of benzhydroxamates derivatives developed as histone deacetylase inhibitors of Schistosoma mansoni histone deacetylase (smHDAC) using molecular docking and binding free energy (BFE) calculations. The developed docking protocol was able to correctly reproduce the experimentally established binding modes of resolved smHDAC8-inhibitor complexes. However, as has been reported in former studies, the obtained docking scores weakly correlate with the experimentally determined activity of the studied inhibitors. Thus, the obtained docking poses were refined and rescored using the Amber software. From the computed protein-inhibitor BFE, different quantitative structure-activity relationship (QSAR) models could be developed and validated using several cross-validation techniques. Some of the generated QSAR models with good correlation could explain up to ~73% variance in activity within the studied training set molecules. The best performing models were subsequently tested on an external test set of newly designed and synthesized analogs. In vitro testing showed a good correlation between the predicted and experimentally observed IC50 values. Thus, the generated models can be considered as interesting tools for the identification of novel smHDAC8 inhibitors.

New Models to Predict the Acute and Chronic Toxicities of Representative Species of the Main Trophic Levels of Aquatic Environments.

To assess the impact of chemicals on an aquatic environment, toxicological data for three trophic levels are needed to address the chronic and acute toxicities. The use of non-testing methods, such as predictive computational models, was proposed to avoid or reduce the need for animal models and speed up the process when there are many substances to be tested. We developed predictive models for Raphidocelis subcapitata, Daphnia magna, and fish for acute and chronic toxicities. The random forest machine learning approach gave the best results. The models gave good statistical quality for all endpoints. These models are freely available for use as individual models in the VEGA platform and for prioritization in JANUS software.

Artificial Neural Networks in Computer-Aided Drug Design: An Overview of Recent Advances.

Computer-aided drug design (CADD) is the framework in which the huge amount of data accumulated by high-throughput experimental methods used in drug design is quantitatively studied. Its objectives include pattern recognition, biomarker identification and/or classification, etc. In order to achieve these objectives, machine learning algorithms and especially artificial neural networks (ANNs) have been used over ADMET factor testing and QSAR modeling evaluation. This paper provides an overview of the current trends in CADD-applied ANNs, since their use was re-boosted over a decade ago.