VolSurf: a new tool for the pharmacokinetic optimization of lead compounds

Enzymatic Synthesis and Structural Modeling of Bio-Based Oligoesters as an Approach for the Fast Screening of Marine Biodegradation and Ecotoxicity

Given the widespread use of esters and polyesters in products like cosmetics, fishing nets, lubricants and adhesives, whose specific application(s) may cause their dispersion in open environments, there is a critical need for stringent eco-design criteria based on biodegradability and ecotoxicity evidence. Our approach integrates experimental and computational methods based on short oligomers, offering a screening tool for the rapid identification of sustainable monomers and oligomers, with a special focus on bio-based alternates. We provide insights into the relationships between the chemical structure and properties of bio-based oligomers in terms of biodegradability in marine environments and toxicity in benchmark organisms. The experimental results reveal that the considered aromatic monomers (terephthalic acid and 2,5-furandicarboxylic acid) accumulate under the tested conditions (OECD 306), although some slight biodegradation is observable when the inoculum derives from sites affected by industrial and urban pollution, which suggests that ecosystems adapt to non-natural chemical pollutants. While clean seas are more susceptible to toxic chemical buildup, biotic catalytic activities offer promise for plastic pollution mitigation. Without prejudice to the fact that biodegradability inherently signifies a desirable trait in plastic products, nor that it automatically grants them a sustainable "license", this study is intended to facilitate the rational design of new polymers and materials on the basis of specific uses and applications.

Non-targeted analysis based on quantitative prediction and toxicity assessment for emerging contaminants in tire particle leachates

Non-targeted analysis (NTA) has great potential to screen emerging contaminants in the environment, and some studies have conducted in-depth investigation on environmental samples. Here, we used a NTA workflow to identify emerging contaminants in used tire particle (TP) leachates, followed by quantitative prediction and toxicity assessment based on hazard scores. Tire particles were obtained from four different types of automobiles, representing the most common tires during daily transportation. With the instrumental analysis of TP leachates, a total of 244 positive and 104 negative molecular features were extracted from the mass data. After filtering by a specialized emerging contaminants list and matching by spectral databases, a total of 51 molecular features were tentatively identified as contaminants, including benzothiazole, hexaethylene glycol, 2-hydroxybenzaldehyde, etc. Given that these contaminants have different mass spectral responses in the mass spectrometry, models for predicting the response of contaminants were constructed based on machine learning algorithms, in this case random forest and artificial neural networks. After five-fold cross-validation, the random forest algorithm model had better prediction performance (MAECV = 0.12, Q2 = 0.90), and thus it was chosen to predict the contaminant concentrations. The prediction results showed that the contaminant at the highest concentration was benzothiazole, with 4,875 μg/L in the winter tire sample. In addition, the joint toxicity assessment of four types of tires was conducted in this study. According to different hazard levels, hazard scores increasing by a factor 10 were developed, and hazard scores of all the contaminants identified in each TP leachate were summed to obtain the total hazard score. All four tires were calculated to have relatively high risks, with winter tires having the highest total hazard score of 40,751. This study extended the application of NTA research and led to the direction of subsequent targeting studies on highly concentrated and toxic contaminants.

Machine-Learning- and Structure-Based Virtual Screening for Selecting Cinnamic Acid Derivatives as Leishmania major DHFR-TS Inhibitors

The critical enzyme dihydrofolate reductase-thymidylate synthase in Leishmania major (LmDHFR-TS) serves a dual-purpose role and is essential for DNA synthesis, a cornerstone of the parasite's reproductive processes. Consequently, the development of inhibitors against LmDHFR-TS is crucial for the creation of novel anti-Leishmania chemotherapies. In this study, we employed an in-house database containing 314 secondary metabolites derived from cinnamic acid that occurred in the Asteraceae family. We conducted a combined ligand/structure-based virtual screening to identify potential inhibitors against LmDHFR-TS. Through consensus analysis of both approaches, we identified three compounds, i.e., lithospermic acid (237), diarctigenin (306), and isolappaol A (308), that exhibited a high probability of being inhibitors according to both approaches and were consequently classified as promising hits. Subsequently, we expanded the binding mode examination of these compounds within the active site of the test enzyme through molecular dynamics simulations, revealing a high degree of structural stability and minimal fluctuations in its tertiary structure. The in silico predictions were then validated through in vitro assays to examine the inhibitory capacity of the top-ranked naturally occurring compounds against LmDHFR-TS recombinant protein. The test compounds effectively inhibited the enzyme with IC50 values ranging from 6.1 to 10.1 μM. In contrast, other common cinnamic acid derivatives (i.e., flavonoid glycosides) from the Asteraceae family, such as hesperidin, isovitexin 4'-O-glucoside, and rutin, exhibited low activity against this target. The selective index (SI) for all tested compounds was determined using HsDHFR with moderate inhibitory effect. Among these hits, lignans 306 and 308 demonstrated the highest selectivity, displaying superior SI values compared to methotrexate, the reference inhibitor of DHFR-TS. Therefore, continued research into the anti-leishmanial potential of these C6C3-hybrid butyrolactone lignans may offer a brighter outlook for combating this neglected tropical disease.

Principal Component Analysis (PCA) of Molecular Descriptors for Improving Permeation through the Blood-Brain Barrier of Quercetin Analogues

Despite its beneficial pharmacological effects in the brain, partly by modulating inositol phosphate multikinase (IPMK) activity, the therapeutic use of quercetin is limited due to its poor solubility, low oral bioavailability, and low permeability through the blood-brain barrier (BBB). We aimed to identify quercetin analogues with improved BBB permeability and preserved binding affinities towards IPMK and to identify the molecular characteristics required for them to permeate the BBB. Binding affinities of quercetin analogues towards IPMK were determined by molecular docking. Principal component analysis (PCA) was applied to identify the molecular descriptors contributing to efficient permeation through the BBB. Among 34 quercetin analogues, 19 compounds were found to form more stable complexes with IPMK, and the vast majority were found to be more lipophilic than quercetin. Using two distinct in silico techniques, insufficient BBB permeation was determined for all quercetin analogues. However, using the PCA method, the descriptors related to intrinsic solubility and lipophilicity (logP) were identified as mainly responsible for clustering four quercetin analogues (trihydroxyflavones) with the highest BBB permeability. The application of PCA revealed that quercetin analogues could be classified with respect to their structural characteristics, which may be utilized in further analogue syntheses and lead optimization of BBB-penetrating IPMK modulators as neuroprotective agents.

Recent advances in structure-based enzyme engineering for functional reconstruction

Structural information can help engineer enzymes. Usually, specific amino acids in particular regions are targeted for functional reconstruction to enhance the catalytic performance, including activity, stereoselectivity, and thermostability. Appropriate selection of target sites is the key to structure-based design, which requires elucidation of the structure-function relationships. Here, we summarize the mutations of residues in different specific regions, including active center, access tunnels, and flexible loops, on fine-tuning the catalytic performance of enzymes, and discuss the effects of altering the local structural environment on the functions. In addition, we keep up with the recent progress of structure-based approaches for enzyme engineering, aiming to provide some guidance on how to take advantage of the structural information.

Collaborative SAR Modeling and Prospective In Vitro Validation of Oxidative Stress Activation in Human HepG2 Cells

Oxidative stress is the consequence of an abnormal increase of reactive oxygen species (ROS). ROS are generated mainly during the metabolism in both normal and pathological conditions as well as from exposure to xenobiotics. Xenobiotics can, on the one hand, disrupt molecular machinery involved in redox processes and, on the other hand, reduce the effectiveness of the antioxidant activity. Such dysregulation may lead to oxidative damage when combined with oxidative stress overpassing the cell capacity to detoxify ROS. In this work, a green fluorescent protein (GFP)-tagged nuclear factor erythroid 2-related factor 2 (NRF2)-regulated sulfiredoxin reporter (Srxn1-GFP) was used to measure the antioxidant response of HepG2 cells to a large series of drug and drug-like compounds (2230 compounds). These compounds were then classified as positive or negative depending on cellular response and distributed among different modeling groups to establish structure-activity relationship (SAR) models. A selection of models was used to prospectively predict oxidative stress induced by a new set of compounds subsequently experimentally tested to validate the model predictions. Altogether, this exercise exemplifies the different challenges of developing SAR models of a phenotypic cellular readout, model combination, chemical space selection, and results interpretation.

Evaluation of the Effects of Heteroaryl Ethylene Molecules in Combination with Antibiotics: A Preliminary Study on Control Strains

The discovery of compounds with antibacterial activity is crucial in the ongoing battle against antibiotic resistance. We developed two QSAR models to design six novel heteroaryl drug candidates and assessed their antibacterial properties against nine ATCC strains, including Enterococcus faecalis, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, and also Salmonella enterica and Escherichia coli, many of which belong to the ESKAPE group. We combined PB4, a previously tested compound from published studies, with GC-VI-70, a newly discovered compound, with the best cytotoxicity/MIC profile. By testing sub-MIC concentrations of PB4 with five antibiotics (linezolid, gentamycin, ampicillin, erythromycin, rifampin, and imipenem), we evaluated the combination's efficacy against the ATCC strains. To assess the compounds' cytotoxicity, we conducted a 24 h and 48 h 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay on colorectal adenocarcinoma (CaCo-2) cells. We tested the antibiotics alone and in combination with PB4. Encouragingly, PB4 reduced the MIC values for GC-VI-70 and for the various clinically used antibiotics. However, it is essential to note that all the compounds studied in this research exhibited cytotoxic activity against cells. These findings highlight the potential of using these compounds in combination with antibiotics to enhance their effectiveness at lower concentrations while minimizing cytotoxic effects.

Chromatographic Data in Statistical Analysis of BBB Permeability Indices

Blood-brain barrier (BBB) permeability is an essential phenomena when considering the treatment of neurological disorders as well as in the case of central nervous system (CNS) adverse effects caused by peripherally acting drugs. The presented work contains statistical analyses and the correlation assessment of the analyzed group of active pharmaceutical ingredients (APIs) with their BBB-permeability data collected from the literature (such as computational log BB; Kp,uu,brain, and CNS+/- groups). A number of regression models were constructed in order to observe the connections between the APIs' physicochemical properties in combination with their retention data from the chromatographic experiments (TLC and HPLC) and the indices of bioavailability in the CNS. Conducted analyses confirm that descriptors significant in BBB permeability modeling are hydrogen bond acceptors and donors, physiological charge, or energy of the lowest unoccupied molecular orbital. These molecular descriptors were the basis, along with the chromatographic data from the TLC in log BB regression analyses. Normal-phase TLC data showed a significant contribution to the creation of the log BB regression model using the multiple linear regression method. The model using them showed a good predictive value at the level of R² = 0.87. Models for Kp,uu,brain resulted in lower statistics: R² = 0.56 for the group of 23 APIs with the participation of k IAM.

SophosQM: Accurate Binding Affinity Prediction in Compound Optimization

The optimization of compounds' binding affinity for a biological target is a crucial aspect of the drug development process. Being able to accurately predict binding energies in advance of synthesizing compounds would have a massive impact on the speed of the drug discovery process. The ideal binding affinity prediction method should combine accuracy, reliability, and speed. In this paper, we present SophosQM, a quantum mechanics (QM)-based approach, which can accurately predict the binding affinities of compounds to proteins. The binding affinity predictive models generated by SophosQM are based on the fragment molecular orbital (FMO) method to estimate the enthalpic component of the binding free energy, and a macroscopic descriptor, clog P, is used as an approximation of the entropic component. The affinity prediction is performed using multilinear regression, fitting the experimental values against the FMO-computed enthalpic term and clog P. The quality of the prediction can be assessed in terms of the correlation coefficient between experimental and predicted values. In this work, the method's reliability and accuracy are exemplified by applying SophosQM to 70 compounds binding to six different targets of pharmaceutical relevance. Overall, the results show a very satisfactory performance with a global correlation coefficient in the order of 0.9. Our predictions also show a satisfactory performance compared to data based on free energy perturbation. Finally, SophosQM can also be applied in high-throughput mode by using semiempirical QM methods to evaluate large portions of chemical space, while retaining a good level of accuracy, but decreasing the computing time to just a few seconds per compound.

Studies of Cytotoxicity Effects, SARS-CoV-2 Main Protease Inhibition, and in Silico Interactions of Synthetic Chalcones

SARS-CoV-2 main protease (M^pro ) plays an essential role in proteolysis cleavage that promotes coronavirus replication. Thus, attenuating the activity of this enzyme represents a strategy to develop antiviral agents. We report inhibitory effects against M^pro of 40 synthetic chalcones, and cytotoxicity activities, hemolysis, and in silico interactions of active compounds. Seven of them bearing a (E)-3-(furan-2-yl)-1-arylprop-2-en-1-one skeleton (10, 28, and 35-39) showed enzyme inhibition with IC₅₀ ranging from 13.76 and 36.13 μM. Except for 35 and 36, other active compounds were not cytotoxic up to 150 μM against THP-1 and Vero cell lines. Compounds 10, and 35-39 showed no hemolysis while 28 was weakly hemotoxic at 150 μM. Moreover, molecular docking showed interactions between compound 10 and M^pro (PDBID 5RG2 and 5RG3) with proximity to cys145 and His41, suggesting a covalent binding. Products of the reaction between chalcones and cyclohexanethiol indicated that this binding could be a Michael addition type.

Inhibitors of ABCG2-mediated multidrug resistance: Lead generation through computer-aided drug design

Human breast cancer resistance protein (BCRP), known also as ABCG2, plays a major role in multiple drug resistance (MDR) in tumor cells. Through this ABC transporter, cancer cells acquire the ability of resistance to structurally and functionally unrelated anticancer drugs. Nowadays, the design of ABCG2 inhibitors as potential agents to enhance the chemotherapy efficacy is an interesting strategy. In this context, we have used computer-aided drug design (CADD) based on available data of a large series of potent inhibitors from our groups as an approach in guiding the design of effective ABCG2 inhibitors. We report therein the results on the use of the FLAPpharm method to elucidate the pharmacophoric features of one of the ABCG2 binding sites involved in the regulation of the basal ATPase activity of the transporter. The predictivity of the model was evaluated by testing three predicted compounds which were found to induce high inhibitory activity of BCRP, in the nanomolar range for the best of them.

An Integrated Machine Learning Model To Spot Peptide Binding Pockets in 3D Protein Screening

The prediction of peptide-protein binding sites is of utmost importance to tackle the onset of severe neurodegenerative diseases and cancer. In this work, we detail a novel machine learning model based on Linear Discriminant Analysis (LDA) demonstrating to be highly predictive in detecting the putative protein binding regions of small peptides. Starting from 439 high-quality pockets derived from peptide-protein crystallographic complexes, three sets of well-established peptide-binding regions were first selected through a Partitioning Around Medoids (PAM) clustering algorithm based on morphological and energetic 3D GRID-MIF molecular descriptors. Next, the best combination between all the putative interacting peptide pockets and related GRID-MIF scores was automatically explored by using the LDA-based protocol implemented in BioGPS. This approach proved successful to recognize the actual interacting peptide regions (that is, AUC = 0.86 and partial ROC enrichment at 5% of 0.48) from all the other pockets of the protein. Validated on two external collections sets, including 445 and 347 crystallographic peptide-protein complexes, our LDA-based model could be effective to further run peptide-protein virtual screening campaigns.

Blood-brain barrier penetration prediction enhanced by uncertainty estimation

Blood–brain barrier is a pivotal factor to be considered in the process of central nervous system (CNS) drug development, and it is of great significance to rapidly explore the blood–brain barrier permeability (BBBp) of compounds in silico in early drug discovery process. Here, we focus on whether and how uncertainty estimation methods improve in silico BBBp models. We briefly surveyed the current state of in silico BBBp prediction and uncertainty estimation methods of deep learning models, and curated an independent dataset to determine the reliability of the state-of-the-art algorithms. The results exhibit that, despite the comparable performance on BBBp prediction between graph neural networks-based deep learning models and conventional physicochemical-based machine learning models, the GROVER-BBBp model shows greatly improvement when using uncertainty estimations. In particular, the strategy combined Entropy and MC-dropout can increase the accuracy of distinguishing BBB + from BBB − to above 99% by extracting predictions with high confidence level (uncertainty score < 0.1). Case studies on preclinical/clinical drugs for Alzheimer’ s disease and marketed antitumor drugs that verified by literature proved the application value of uncertainty estimation enhanced BBBp prediction model, that may facilitate the drug discovery in the field of CNS diseases and metastatic brain tumors.

Development of Simple and Accurate in Silico Ligand-Based Models for Predicting ABCG2 Inhibition

The ATP binding cassette transporter ABCG2 is a physiologically important drug transporter that has a central role in determining the ADMET (absorption, distribution, metabolism, elimination, and toxicity) profile of therapeutics, and contributes to multidrug resistance. Thus, development of predictive in silico models for the identification of ABCG2 inhibitors is of great interest in the early stage of drug discovery. In this work, by exploiting a large public dataset, a number of ligand-based classification models were developed using partial least squares-discriminant analysis (PLS-DA) with molecular interaction field- and fingerprint-based structural description methods, regarding physicochemical and fragmental properties related to ABCG2 inhibition. An in-house dataset compiled from recently experimental studies was used to rigorously validated the model performance. The key molecular properties and fragments favored to inhibitor binding were discussed in detail, which was further explored by docking simulations. A highly informative chemical property was identified as the principal determinant of ABCG2 inhibition, which was utilized to derive a simple rule that had a strong capability for differentiating inhibitors from non-inhibitors. Furthermore, the incorporation of the rule into the best PLS-DA model significantly improved the classification performance, particularly achieving a high prediction accuracy on the independent in-house set. The integrative model is simple and accurate, which could be applied to the evaluation of drug-transporter interactions in drug development. Also, the dominant molecular features derived from the models may help medicinal chemists in the molecular design of novel inhibitors to circumvent ABCG2-mediated drug resistance.

In silico models to predict tubular secretion or reabsorption clearance pathway using physicochemical properties and structural characteristics

1. Renal clearance is one of the main pathways for a drug to be cleared from plasma. The aim of this study is to develop in-silico models to find out the relationship between the type of renal clearance, and structural parameters.2. Literature data were used to categorize the drugs into those that undergo tubular secretion and those that undergo reabsorption. Different structural descriptors (VolSurf descriptors, Abraham solvation parameters, data warrior descriptors, logarithm of distribution coefficient at pH =7.4 (logD7.4)) were applied to develop a mechanistic model for estimating renal clearance class whether its secretion or reabsorption.3. The results of this study show that logD7.4, and the number of hydrogen bond donors as well as available uncharged species (AUS7.4) are the most effective descriptors to establish mechanistic models for predicting renal clearance class. The classification models were established with a level of accuracy of more than 75%.4. Developed models of this study can be helpful to predict renal clearance class for new drug candidates with an acceptable error. Hydrophilicity and hydrogen bond formation ability of drugs are among the main descriptors.

Design, synthesis, biological evaluation and QSAR analysis of novel N-substituted benzimidazole derived carboxamides

As a result of our previous research focussed on benzimidazoles, herein we present design, synthesis, QSAR analysis and biological activity of novel N-substituted benzimidazole derived carboxamides. Carboxamides were designed to study the influence of the number of methoxy groups, the type of the substituent placed at the benzimidazole core on biological activity. Pronounced antioxidative activity displayed unsubstituted 28 (IC₅₀ ≈ 3.78 mM, 538.81 mmolFe²⁺/mmolC) and dimethoxy substituted derivative 34 (IC₅₀ ≈ 5.68 mM, 618.10 mmolFe²⁺/mmolC). Trimethoxy substituted 43 and unsubstituted compound 40 with isobutyl side chain at N atom showed strong activity against HCT116 (IC₅₀ ≈ 0.6 µM, both) and H 460 cells (IC₅₀ ≈ 2.5 µM; 0.4 µM), being less cytotoxic towards non-tumour cell. Antioxidative activity in cell generally confirmed relatively modest antioxidant capacity obtained in DPPH/FRAP assays of derivatives 34 and 40. The 3D-QSAR models were generated to explore molecular properties that have the highest influence on antioxidative activity.

Getting Insights into Structural and Energetic Properties of Reciprocal Peptide-Protein Interactions

Peptide-protein interactions play a key role for many cellular and metabolic processes involved in the onset of largely spread diseases such as cancer and neurodegenerative pathologies. Despite the progress in the structural characterization of peptide-protein interfaces, the in-depth knowledge of the molecular details behind their interactions is still a daunting task. Here, we present the first comprehensive in silico morphological and energetic study of peptide binding sites by focusing on both peptide and protein standpoints. Starting from the PixelDB database, a nonredundant benchmark collection of high-quality 3D crystallographic structures of peptide-protein complexes, a classification analysis of the most representative categories based on the nature of each cocrystallized peptide has been carried out. Several interpretable geometrical and energetic descriptors have been computed both from peptide and target protein sides in the attempt to unveil physicochemical and structural causative correlations. Finally, we investigated the most frequent peptide-protein residue pairs at the binding interface and made extensive energetic analyses, based on GRID MIFs, with the aim to study the peptide affinity-enhancing interactions to be further exploited in rational drug design strategies.

Computer-Assisted Discovery of Alkaloids with Schistosomicidal Activity

Schistosomiasis is a chronic parasitic disease caused by trematodes of the genus Schistosoma; it is commonly caused by Schistosoma mansoni, which is transmitted by Bioamphalaria snails. Studies show that more than 200 million people are infected and that more than 90% of them live in Africa. Treatment with praziquantel has the best cost–benefit result on the market. However, hypersensitivity, allergy, and drug resistance are frequently presented after administration. From this perspective, ligand-based and structure-based virtual screening (VS) techniques were combined to select potentially active alkaloids against S. mansoni from an internal dataset (SistematX). A set of molecules with known activity against S. mansoni was selected from the ChEMBL database to create two different models with accuracy greater than 84%, enabling ligand-based VS of the alkaloid bank. Subsequently, structure-based VS was performed through molecular docking using four targets of the parasite. Finally, five consensus hits (i.e., five alkaloids with schistosomicidal potential), were selected. In addition, in silico evaluations of the metabolism, toxicity, and drug-like profile of these five selected alkaloids were carried out. Two of them, namely, 11,12-methylethylenedioxypropoxy and methyl-3-oxo-12-methoxy-n(1)-decarbomethoxy-14,15-didehydrochanofruticosinate, had plausible toxicity, metabolomics, and toxicity profiles. These two alkaloids could serve as starting points for the development of new schistosomicidal compounds based on natural products.

Combined Pharmacophore and Grid-Independent Molecular Descriptors (GRIND) Analysis to Probe 3D Features of Inositol 1,4,5-Trisphosphate Receptor (IP3R) Inhibitors in Cancer

Inositol 1, 4, 5-trisphosphate receptor (IP₃R)-mediated Ca²⁺ signaling plays a pivotal role in different cellular processes, including cell proliferation and cell death. Remodeling Ca²⁺ signals by targeting the downstream effectors is considered an important hallmark in cancer progression. Despite recent structural analyses, no binding hypothesis for antagonists within the IP₃-binding core (IBC) has been proposed yet. Therefore, to elucidate the 3D structural features of IP₃R modulators, we used combined pharmacoinformatic approaches, including ligand-based pharmacophore models and grid-independent molecular descriptor (GRIND)-based models. Our pharmacophore model illuminates the existence of two hydrogen-bond acceptors (2.62 Å and 4.79 Å) and two hydrogen-bond donors (5.56 Å and 7.68 Å), respectively, from a hydrophobic group within the chemical scaffold, which may enhance the liability (IC₅₀) of a compound for IP₃R inhibition. Moreover, our GRIND model (PLS: Q² = 0.70 and R² = 0.72) further strengthens the identified pharmacophore features of IP₃R modulators by probing the presence of complementary hydrogen-bond donor and hydrogen-bond acceptor hotspots at a distance of 7.6-8.0 Å and 6.8-7.2 Å, respectively, from a hydrophobic hotspot at the virtual receptor site (VRS). The identified 3D structural features of IP₃R modulators were used to screen (virtual screening) 735,735 compounds from the ChemBridge database, 265,242 compounds from the National Cancer Institute (NCI) database, and 885 natural compounds from the ZINC database. After the application of filters, four compounds from ChemBridge, one compound from ZINC, and three compounds from NCI were shortlisted as potential hits (antagonists) against IP₃R. The identified hits could further assist in the design and optimization of lead structures for the targeting and remodeling of Ca²⁺ signals in cancer.

Essential oils of Uvaria boniana - chemical composition, in vitro bioactivity, docking, and in silico ADMET profiling of selective major compounds

Phytochemical investigation applying GC (gas chromatography)-MS (mass spectrometry)/GC-FID (flame ionization detection) on the hydro-distilled essential oils of the Vietnamese medicinal plant Uvaria boniana leaf and twig lead to the detection of 35 constituents (97.36%) in the leaf oil and 52 constituents (98.75%) in the twig oil. Monoterpenes, monoterpenoids, sesquiterpenes, and sesquiterpenoids were characteristic of U. boniana essential oils. The leaf oil was represented by major components (E)-caryophyllene (16.90%), bicyclogermacrene (15.95%), α-humulene (14.96%), and linalool (12.40%), whereas four compounds α-cadinol (16.16%), epi-α-muurolol (10.19%), α-pinene (11.01%), and β-pinene (8.08%) were the main ones in the twig oil. As compared with the leaf oil, the twig oil was better in antimicrobial activity. With the same MIC value of 40 mg/mL, the twig oil successfully controlled the growth of Gram (+) bacterium Bacillus subtilis, Gram (-) bacterium Escherichia coli, fungus Aspergillus niger, and yeast Saccharomyces cerevisiae. In addition, both two oil samples have induced antiinflammatory activity with the IC₅₀ values of 223.7-240.6 mg/mL in NO productive inhibition when BV2 cells had been stimulated by LPS. Docking simulations of four major compounds of U. boniana twig oil on eight relevant antibacterial targets revealed that epi-α-muurolol and α-cadinol are moderate inhibitors of E. coli DNA gyrase subunit B, penicillin binding protein 2X and penicillin binding protein 3 of Pseudomonas aeruginosa with similar free binding energies of -30.1, -29.3, and -29.3 kJ/mol, respectively. Furthermore, in silico ADMET studies indicated that all four docked compounds have acceptable oral absorption, low metabolism, and appropriated toxicological profile to be considered further as drug candidates.

Combining machine learning and quantum mechanics yields more chemically aware molecular descriptors for medicinal chemistry applications

Molecular interaction fields (MIFs), describing molecules in terms of their ability to interact with any chemical entity, are one of the most established and versatile concepts in drug discovery. Improvement of this molecular description is highly desirable for in silico drug discovery and medicinal chemistry applications. In this work, we revised a well‐established molecular mechanics' force field and applied a hybrid quantum mechanics and machine learning approach to parametrize the hydrogen‐bonding (HB) potentials of small molecules, improving this aspect of the molecular description. Approximately 66,000 molecules were chosen from available drug databases and subjected to density functional theory calculations (DFT). For each atom, the molecular electrostatic potential (EP) was extracted and used to derive new HB energy contributions; this was subsequently combined with a fingerprint‐based description of the structural environment via partial least squares modeling, enabling the new potentials to be used for molecules outside of the training set. We demonstrate that parameter prediction for molecules outside of the training set correlates with their DFT‐derived EP, and that there is correlation of the new potentials with hydrogen‐bond acidity and basicity scales. We show the newly derived MIFs vary in strength for various ring substitution in accordance with chemical intuition. Finally, we report that this derived parameter, when extended to non‐HB atoms, can also be used to estimate sites of reaction.

Heteroaryl-Ethylenes as New Lead Compounds in the Fight against High Priority Bacterial Strains

The widespread use of antibiotics has led to a gradual increase in drug-resistant bacterial infections, which severely weakens the clinical efficacy of antibacterial therapies. In recent decades, stilbenes aroused great interest because of their high bioavailability, as well as their manifold biological activity. Our research efforts are focused on synthetic heteroaromatic stilbene derivatives as they represent a potentially new type of antibiotic with a wide antibacterial spectrum. Herein, a preliminary molecular modeling study and a versatile synthetic scheme allowed us to define eight heteroaromatic stilbene derivatives with potential antimicrobial activity. In order to evaluate our compound’s activity spectrum and antibacterial ability, minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC) tests have been performed on Gram-positive and Gram-negative ATCC strains. Compounds PB4, PB5, PB7, and PB8 showed the best values in terms of MIC and were also evaluated for MBC, which was found to be greater than MIC, confirming a bacteriostatic activity. For all compounds, we evaluated toxicity on colon-rectal adenocarcinoma cells tumor cells (CaCo2), once it was established that the whole selected set was more active than 5-Fluorouracil in reducing CaCo-2 cells viability. To the best of our knowledge, the biological assays have shown for these derivatives an excellent bacteriostatic activity, compared to similar molecular structures previously reported, thus paving the way for a new class of antibiotic compounds.

Development of 2D, 3D-QSAR and Pharmacophore Modeling of Chalcones for the Inhibition of Monoamine Oxidase B

BACKGROUND

Selective and reversible types of MAO-B inhibitors have emerged as promising candidates for the management of neurodegenerative diseases. Several functionalized chalcone derivatives were shown to have potential reversible MAO-B inhibitory activity, which have recently been reported from our laboratory.

METHODS

With the experimental results of about 70 chalcone derivatives, we further developed a pharmacophore modelling, and 2D and 3D- QSAR analyses of these reported chalcones for MAO-B inhibition.

RESULTS

The 2D-QSAR model presented four variables (MATS7v, GATS 1i and 3i, and C-006) from 143 Dragon 7 molecular descriptors, with a r2 value of 0.76 and a Q2cv for cross-validation equal to 0.72. An external validation also was performed using 11 chalcones, obtaining a Q2ext value of 0.74. The second 3D-QSAR model using MLR (multiple linear regression) was built starting from 128 Volsurf+ molecular descriptors, being identified as 4 variables (Molecular descriptors): D3, CW1 and LgS11, and L2LGS. Adetermination coefficient (r2) value of 0.76 and a Q2cv for cross-validation equal to 0.72 were obtained for this model. An external validation also was performed using 11 chalcones and a Q2ext value of 0.74 was found.

CONCLUSION

This report exhibited a good correlation and satisfactory agreement between experiment and theory.

Machine learning models to select potential inhibitors of acetylcholinesterase activity from SistematX: a natural products database

Alzheimer's disease is the most common form of dementia, representing 60-70% of dementia cases. The enzyme acetylcholinesterase (AChE) cleaves the ester bonds in acetylcholine and plays an important role in the termination of acetylcholine activity at cholinergic synapses in various regions of the nervous system. The inhibition of acetylcholinesterase is frequently used to treat Alzheimer's disease. In this study, a merged BindingDB and ChEMBL dataset containing molecules with reported half-maximal inhibitory concentration (IC₅₀) values for AChE (7032 molecules) was used to build machine learning classification models for selecting potential AChE inhibitors from the SistematX dataset (8593 secondary metabolites). A total of seven fivefold models with accuracy above 80% after cross-validation were obtained using three types of molecular descriptors (VolSurf, DRAGON 5.0, and bit-based fingerprints). A total of 521 secondary metabolites (6.1%) were classified as active in this stage. Subsequently, virtual screening was performed, and 25 secondary metabolites were identified as potential inhibitors of AChE. Separately, the crystal structure of AChE in complex with (-)-galantamine was used to perform molecular docking calculations with the entire SistematX dataset. Consensus analysis of both methodologies was performed. Only eight structures achieved combined probability values above 0.5. Finally, two sesquiterpene lactones, structures 15 and 24, were predicted to be able to cross the blood-brain barrier, which was confirmed in the VolSurf+ quantitative model, revealing these two structures as the most promising secondary metabolites for AChE inhibition among the 8593 molecules tested. A consensus analysis of classification models and molecular docking calculations identified four potential inhibitors of acetylcholinesterase from the SistematX dataset (8593 structures).

Antimetastatic Potential of Quercetin Analogues with Improved Pharmacokinetic Profile: Pharmacoinformatic Preliminary Study

BACKGROUND

Urokinase-type plasminogen activator (uPA) system is a crucial pathway for tumor invasion and metastasis. Recently, multiple anticancer effects of quercetin have been described, including inhibitory activity against uPA. However, the clinical use of this flavonoid has been limited due to its low oral bioavailability.

OBJECTIVE

The objectives of the study were to assess the antimetastatic potential of quercetin analogues by analyzing their binding affinity for uPA and to select the compounds with improved pharmacological profiles.

METHODS

Binding affinities of structural analogues of quercetin to uPA receptor were determined by molecular docking analysis using Molegro Virtual Docker software, and molecular descriptors relevant for estimating pharmacological profile were calculated from ligand structures using computational models.

RESULTS

Among 44 quercetin analogues, only one quercetin analogue (3,6,2',4',5'-pentahydroxyflavone) was found to possess both higher aqueous solubility and membrane permeability, and a stronger affinity for uPA than quercetin, which makes it the potential lead compound for anticancer drug development. Like quercetin, this compound has five hydroxyl groups but is arranged differently, which contributes to the higher aqueous solubility and higher amphiphilic moment compared to quercetin. Since membrane permeability is not recognized as the limiting factor for quercetin absorption, analogues with higher aqueous solubility and retained or stronger uPA inhibitory activity should also be further experimentally validated for potential therapeutic use.

CONCLUSION

Identified quercetin analogues with better physicochemical and pharmacological properties have a high potential to succeed in later stages of research in biological systems as potential anticancer agents with antimetastatic activity.

Identification of Kaurane-Type Diterpenes as Inhibitors of Leishmania Pteridine Reductase I

The current treatments against Leishmania parasites present high toxicity and multiple side effects, which makes the control and elimination of leishmaniasis challenging. Natural products constitute an interesting and diverse chemical space for the identification of new antileishmanial drugs. To identify new drug options, an in-house database of 360 kauranes (tetracyclic diterpenes) was generated, and a combined ligand- and structure-based virtual screening (VS) approach was performed to select potential inhibitors of Leishmania major (Lm) pteridine reductase I (PTR1). The best-ranked kauranes were employed to verify the validity of the VS approach through LmPTR1 enzyme inhibition assay. The half-maximal inhibitory concentration (IC₅₀) values of selected bioactive compounds were examined using the random forest (RF) model (i.e., 2β-hydroxy-menth-6-en-5β-yl ent-kaurenoate (135) and 3α-cinnamoyloxy-ent-kaur-16-en-19-oic acid (302)) were below 10 μM. A compound similar to 302, 3α-p-coumaroyloxy-ent-kaur-16-en-19-oic acid (302a), was also synthesized and showed the highest activity against LmPTR1. Finally, molecular docking calculations and molecular dynamics simulations were performed for the VS-selected, most-active kauranes within the active sites of PTR1 hybrid models, generated from three Leishmania species that are known to cause cutaneous leishmaniasis in the new world (i.e., L. braziliensis, L. panamensis, and L. amazonensis) to explore the targeting potential of these kauranes to other species-dependent variants of this enzyme.

Application of Advanced Technologies in Natural Product Research: A Review with Special Emphasis on ADMET Profiling

The successful conversion of natural products (NPs) into lead compounds and novel pharmacophores has emboldened the researchers to harness the drug discovery process with a lot more enthusiasm. However, forfeit of bioactive NPs resulting from an overabundance of metabolites and their wide dynamic range have created the bottleneck in NP researches. Similarly, the existence of multidimensional challenges, including the evaluation of pharmacokinetics, pharmacodynamics, and safety parameters, has been a concerning issue. Advancement of technology has brought the evolution of traditional natural product researches into the computer-based assessment exhibiting pretentious remarks about their efficiency in drug discovery. The early attention to the quality of the NPs may reduce the attrition rate of drug candidates by parallel assessment of ADMET profiling. This article reviews the status, challenges, opportunities, and integration of advanced technologies in natural product research. Indeed, emphasis will be laid on the current and futuristic direction towards the application of newer technologies in early-stage ADMET profiling of bioactive moieties from the natural sources. It can be expected that combinatorial approaches in ADMET profiling will fortify the natural product-based drug discovery in the near future.

Computer-assisted discovery of compounds with insecticidal activity against Musca domestica and Mythimna separata

Pesticides are used to control and combat insects and pests in the agricultural sector, households, and public health programs. The frequent and disorderly use of these pesticides may lead to variety of undesired effects. Therefore, natural products have many advantages over to synthetic compounds to be used as insecticides. The goal of this study was to find natural products with insecticidal potential against Musca domestica and Mythimna separata. To achieve this goal, we developed predictive QSAR models using MuDRA, PLS, and RF approaches and performed virtual screening of 117 natural products. As a result of QSAR modeling, we formulated the recommendations regarding physico-chemical characteristics for promising compounds active against Musca domestica and Mythimna separata. Homology models were successfully built for both species and molecular docking of QSAR hits vs known insecticides allowed us to prioritize twenty-two compounds against Musca domestica and six against Mythimna separata. Our results suggest that pimarane diterpenes, abietanes diterpenes, dimeric diterpenes and scopadulane diterpenes obtained from aerial parts of species of the genus Calceolaria (Calceolariaceae: Scrophulariaceae) can be considered as potential insecticidal.

REDIAL-2020: A suite of machine learning models to estimate Anti-SARS-CoV-2 activities

Strategies for drug discovery and repositioning are an urgent need with respect to COVID-19. We developed "REDIAL-2020", a suite of machine learning models for estimating small molecule activity from molecular structure, for a range of SARS-CoV-2 related assays. Each classifier is based on three distinct types of descriptors (fingerprint, physicochemical, and pharmacophore) for parallel model development. These models were trained using high throughput screening data from the NCATS COVID19 portal (https://opendata.ncats.nih.gov/covid19/index.html), with multiple categorical machine learning algorithms. The "best models" are combined in an ensemble consensus predictor that outperforms single models where external validation is available. This suite of machine learning models is available through the DrugCentral web portal (http://drugcentral.org/Redial). Acceptable input formats are: drug name, PubChem CID, or SMILES; the output is an estimate of anti-SARS-CoV-2 activities. The web application reports estimated activity across three areas (viral entry, viral replication, and live virus infectivity) spanning six independent models, followed by a similarity search that displays the most similar molecules to the query among experimentally determined data. The ML models have 60% to 74% external predictivity, based on three separate datasets. Complementing the NCATS COVID19 portal, REDIAL-2020 can serve as a rapid online tool for identifying active molecules for COVID-19 treatment. The source code and specific models are available through Github (https://github.com/sirimullalab/redial-2020), or via Docker Hub (https://hub.docker.com/r/sirimullalab/redial-2020) for users preferring a containerized version.

Selection of antileishmanial sesquiterpene lactones from SistematX database using a combined ligand-/structure-based virtual screening approach

Leishmaniasis refers to a complex of diseases, caused by the intracellular parasitic protozoans belonging to the genus Leishmania. Among the three types of disease manifestations, the most severe type is visceral leishmaniasis, which is caused by Leishmania donovani, and is diagnosed in more than 20,000 cases annually, worldwide. Because the current therapeutic options for disease treatment are associated with several limitations, the identification of new potential leads/drugs remains necessary. In this study, a combined approach was used, based on two different virtual screening (VS) methods, which were designed to select promising antileishmanial agents from among the entire sesquiterpene lactone (SL) dataset registered in SistematX, a web interface for managing a secondary metabolite database that is accessible by multiple platforms on the Internet. Thus, a ChEMBL dataset, including 3159 and 1569 structures that were previously tested against L. donovani amastigotes and promastigotes in vitro, respectively, was used to develop two random forest models, which performed with greater than 74% accuracy in both the cross-validation and test sets. Subsequently, a ligand-based VS assay was performed against the 1306 SistematX-registered SLs. In parallel, the crystal structures of three L. donovani target proteins, N-myristoyltransferase, ornithine decarboxylase, and mitogen-activated protein kinase 3, and a homology model of pteridine reductase 1 were used to perform a structure-based VS, using molecular docking, of the entire SistematX SL dataset. The consensus analysis of these two VS approaches resulted in the normalization of probability scores and identified 13 promising, enzyme-targeting, antileishmanial SLs from SistematX that may act against L. donovani. A combined approach based on two different virtual screening methods (structure-based and ligand-based) was performed using an in-house dataset composed of 1306 sesquiterpene lactones to identify potential antileishmanial (Leishmania donovani) structures.

In vitro and in silico Activity of Iridoids Against Leishmania amazonensis

BACKGROUND

Leishmaniasis reaches millions of people around the world. The control of the disease is difficult due to the restricted access to the diagnosis and medication, and low adherence to the treatment. Thus, more efficient drugs are needed and natural products are good alternatives. Iridoids, natural products with reported leishmanicidal activity, can be exploited for the development of anti- Leishmania drugs. The aim of this study was to isolate and to investigate the in vitro activity of iridoids against Leishmania amazonensis and to compare the activity in silico of these compounds with those reported as active against this parasite.

METHODS

Iridoids were isolated by chromatographic methods. The in vitro activity of asperuloside (1) and geniposide (2) from Escalonia bifida, galiridoside (3) from Angelonia integerrima and theveridoside (4) and ipolamiide (5) from Amphilophium crucigerum was investigated against promastigote forms of Leishmania amazonensis. Molecular modeling studies of 1-5 and iridoids cited as active against Leishmania spp. were performed.

RESULTS

Compounds 1-5 (5-100 µM) did not inhibit the parasite survival. Physicochemical parameters predicted for 1-5 did not show differences compared to those described in literature. The SAR and the pharmacophoric model confirmed the importance of maintaining the cyclopentane[C]pyran ring of the iridoid, of oxygen-linked substituents at the C1 and C6 positions and of bulky substituents attached to the iridoid ring to present leishmanicidal activity.

CONCLUSION

The results obtained in this study indicate that iridoids are a promising group of secondary metabolites and should be further investigated in the search for new anti-Leishmania drugs.

Lipid aldehyde hydrophobicity affects apo-SOD1 modification and aggregation

Unsaturated lipids are oxidized by reactive oxygen species and enzymes, leading to the increased formation of lipid hydroperoxides and several electrophilic products. Lipid-derived electrophiles can modify macromolecules, such as proteins, resulting in the loss of function and/or aggregation. The accumulation of Cu,Zn-superoxide dismutase (SOD1) aggregates has been associated with familial cases of amyotrophic lateral sclerosis (ALS). The protein aggregation mechanisms in motor neurons remain unclear, although recent studies have shown that lipids and oxidized lipid derivatives may play roles in this process. Here, we aimed to compare the effects of different lipid aldehydes on the induction of SOD1 modifications and aggregation, in vitro. Human recombinant apo-SOD1 was incubated with 4-hydroxy-2-hexenal (HHE), 4-hydroxy-2-nonenal (HNE), 2-hexen-1-al (HEX), 2,4-nonadienal (NON), 2,4-decadienal (DEC), or secosterol aldehydes (SECO-A or SECO-B). High-molecular-weight apo-SOD1 aggregates dramatically increased in the presence of highly hydrophobic aldehydes (LogP_calc > 3). Notably, several Lys residues were modified by exposure to all aldehydes. The observed modifications were primarily observed on Lys residues located near the dimer interface (K3 and K9) and at the electrostatic loop (K122, K128, and K136). Moreover, HHE and HNE induced extensive apo-SOD1 modifications, by forming Schiff bases or Michael adducts with Lys, His, and Cys residues. However, these aldehydes were unable to induce large protein aggregates. Overall, our data shed light on the importance of lipid aldehyde hydrophobicity on the induction of apo-SOD1 aggregation and identified preferential sites of lipid aldehyde-induced modifications.

Modified Jian-pi-yang-zheng decoction inhibits gastric cancer progression via the macrophage immune checkpoint PI3Kγ

Jian-pi-yang-zheng Decoction (JPYZ) is a traditional Chinese medicine that is used for the treatment of advanced gastric cancer, and it shows good efficacy in patients. A previous study indicated that JPYZ inhibited the progression of gastric cancer via the regulation of tumor-associated macrophages (TAMs), but the underlying molecular target of JPYZ regulation of TAMs has not been determined. The present study used modified-JPYZ (mJPYZ) to extend our investigation of gastric cancer. Our results showed that mJPYZ inhibited gastric cancer progression in vivo and in vitro. We found that mJPYZ decreased the activity of PI3-kinase γ (PI3Kγ) in TAMs, reduced the anti-inflammatory factor IL-10 and increased the expression of pro-inflammatory cytokines, such as TNF-α and IL-1β, which ultimately promoted the conversion of TAMs from M2 to M1. Our findings also indicated that mJPYZ inhibited the growth and metastasis of gastric cancer by alleviating the unfavorable differentiation of TAMs via the PI3Kγ signaling cascades. In conclusion, the present findings indicated that mJPYZ inhibited gastric cancer cell EMT via PI3Kγ-dependent TAM reprogramming, which eventually suppressed gastric cancer growth and metastasis. Our study provides an underlying mechanism of a Chinese medicine in the treatment of gastric cancer via PI3Kγ in macrophages.

In silico Discovery of Resveratrol Analogues as Potential Agents in Treatment of Metabolic Disorders

BACKGROUND

Resveratrol was demonstrated to act as partial agonist of PPAR-γ receptor, which opens up the possibility for its use in the treatment of metabolic disorders. Considering the poor bioavailability of resveratrol, particularly due to its low aqueous solubility, we aimed to identify analogues of resveratrol with improved pharmacokinetic properties and higher binding affinities towards PPAR-γ.

METHODS

3D structures of resveratrol and its analogues were retrieved from ZINC database, while PPAR-γ structure was obtained from Protein Data Bank. Docking studies were performed using Molegro Virtual Docker software. Molecular descriptors relevant to pharmacokinetics were calculated from ligand structures using VolSurf+ software.

RESULTS

Using structural similarity search method, 56 analogues of resveratrol were identified and subjected to docking analyses. Binding energies were ranged from -136.69 to -90.89 kcal/mol, with 16 analogues having higher affinities towards PPAR-γ in comparison to resveratrol. From the calculated values of SOLY descriptor, 23 studied compounds were shown to be more soluble in water than resveratrol. However, only two tetrahydroxy stilbene derivatives, piceatannol and oxyresveratrol, had both better solubility and affinity towards PPAR-γ. These compounds also had more favorable ADME profile, since they were shown to be more metabolically stable and wider distributed in body than resveratrol.

CONCLUSION

Piceatannol and oxyresveratrol should be considered as potential lead compounds for further drug development. Although experimental validation of obtained in silico results is required, this work can be considered as a step toward the discovery of new natural and safe drugs in treatment of metabolic disorders.

In Silico Prediction of the Dissociation Rate Constants of Small Chemical Ligands by 3D-Grid-Based VolSurf Method

Accumulated evidence suggests that binding kinetic properties—especially dissociation rate constant or drug-target residence time—are crucial factors affecting drug potency. However, quantitative prediction of kinetic properties has always been a challenging task in drug discovery. In this study, the VolSurf method was successfully applied to quantitatively predict the k_off values of the small ligands of heat shock protein 90α (HSP90α), adenosine receptor (AR) and p38 mitogen-activated protein kinase (p38 MAPK). The results showed that few VolSurf descriptors can efficiently capture the key ligand surface properties related to dissociation rate; the resulting models demonstrated to be extremely simple, robust and predictive in comparison with available prediction methods. Therefore, it can be concluded that the VolSurf-based prediction method can be widely applied in the ligand-receptor binding kinetics and de novo drug design researches.

Quantitative Structure-Activity Relationship (QSAR) Studies for the Inhibition of MAOs

Monoamine oxidases are the crucial drug targets for the treatment of neurodegenerative disorders like depression, Parkinson's disease, and Alzheimer's disease. The enzymes catalyze the oxidative deamination of several monoamine containing neurotransmitters, i.e. serotonin (5-HT), melatonin, epinephrine, norepinephrine, phenylethylamine, benzylamine, dopamine, tyramine, etc. The oxidative reaction of monoamine oxidases results in the production of hydrogen peroxide that leads to the neurodegeneration process. Therefore, the inhibition of monoamine oxidases has shown a profound effect against neurodegenerative diseases. At present, the design and development of newer lead molecules for the inhibition of monoamine oxidases are under intensive research in the field of medicinal chemistry. Recently, the advancement in QSAR methodologies has shown considerable interest in the development of monoamine oxidase inhibitors. The present review describes the development of QSAR methodologies, and their role in the design of newer monoamine oxidase inhibitors. It will assist the medicinal chemist in the identification of selective and potent monoamine oxidase inhibitors from various chemical scaffolds.

Computer-aided Drug Design Investigations for Benzothiazinone Derivatives Against Tuberculosis

BACKGROUND

Tuberculosis (Mycobacterium tuberculosis) is an infectious bacterial disease with the highest levels of mortality worldwide, presenting numerous cases of resistance. In silico studies, which elaborate chemical and biological models in computational tools and make it possible to interpret molecular characteristics, are among the methods used in the search for new drugs.

OBJECTIVE

In this perspective, our aim was to use QSAR and molecular modeling to propose possible pharmacophores from benzothiazinone derivatives.

METHODS

In this study, a set of 69 benzothiazinone derivatives, together with computational tools such as molecular descriptor analysis in chemometrics, metabolic prediction, and molecular coupling to 4 proteins: DprE1, InhA, PS, and DHFR important for the bacillus were investigated.

RESULTS

The chemometric model computed in the Volsurf+ program presented good predictive values for both amphiphilicity and molecular volume. These are essential for biological activity. Metabolites from the cytochrome isoforms CYP3A4 and 2D6 interactions revealed coupling divergences which, noting that the metabolites did not present changes to the QSAR proposed pharmacophore structures, may be due to the reaction medium and existing differences in the benzothiazinone structures. Similarly, molecular docking with the four TB enzymes presented good interactions for the more active compounds. The fragments found using QSAR (being essential for biological activity) also presented as being essential for ligand-protein site interactions.

CONCLUSION

From the benzothiazinone derivative series evaluated, compound 11026134 presented the best profile in all study analyses, noting that the trifluoromethyl, nitro group, and piperazine fragment with aliphatic hydrocarbon groups are likely pharmacophores for the benzothiazinones studied.

Synthesis and biological activity of analogs of the antifungal antibiotic UK-2A. III. Impact of modifications to the macrocycle isobutyryl ester position

BACKGROUND

Fenpicoxamid (Inatreq™ active), a new fungicide under development by Corteva Agriscience™, Agriculture Division of DowDuPont, is an isobutyryl acetal derivative of the antifungal antibiotic UK-2A. SAR studies around the picolinamide ring and benzyl substituents attached at positions 3 and 8, respectively, of the UK-2A bislactone macrocycle have recently been documented. This study focuses on replacement of the isobutyryl ester group in the 7 position.

RESULTS

Thirty analogs, predominantly esters and ethers, were prepared and evaluated for inhibition of mitochondrial electron transport and in vitro growth of Zymoseptoria tritici, Leptosphaeria nodorum, Pyricularia oryzae and Ustilago maydis. Aliphatic substituents containing four to six carbon atoms deliver strong intrinsic activity, the pivaloate ester (IC₅₀ 1.44 nM) and the n-butyl, 1-Me-propyl, 3,3-diMe-propyl and 2-c-propyl propyl ethers (IC₅₀ values = 1.08, 1.14, 1.15 & 1.32 nM, respectively) being the most active derivatives. QSAR modelling identified solvation energy (E_solv ) and critical packing parameters (vsurf_CP) as highly significant molecular descriptors for explaining relative intrinsic activity of analogs. Activity translation to fungal growth inhibition and disease control testing was significantly influenced by intrinsic activity and physical properties, the cyclopropanecarboxylate ester (log D 3.67, IC₅₀ 3.36 nM, Z. tritici EC₅₀ 12 μg L^-1 ) showing the strongest Z. tritici activity in protectant tests.

CONCLUSIONS

Substitution of the isobutyryl ester group of UK-2A generates analogs that retain strong antifungal activity against Z. tritici and other fungi. © 2019 Society of Chemical Industry.

To Pass or Not To Pass: Predicting the Blood-Brain Barrier Permeability with the 3D-RISM-KH Molecular Solvation Theory

Predicting the ability of chemical species to cross the blood-brain barrier (BBB) is an active field of research for development and mechanistic understanding in the pharmaceutical industry. Here, we report the BBB permeability of a large data set of compounds by incorporating molecular solvation energy descriptors computed by the 3D-RISM-KH molecular solvation theory. We have been able to show, for the first time, that the computed excess chemical potential in different solvents can be successfully used to predict permeability of compounds in a binary manner (yes/no) via a minimum-descriptor-based model. Our findings successfully combine the molecular solvation theory with the machine learning approach to address one of the most daunting challenges in predictive structure-activity relationship modeling. The workflow presented in this work is simple enough to be used by nonexperts with ease.

Discovery of Inhibitors for Proliferating Cell Nuclear Antigen Using a Computational-Based Linked-Multiple-Fragment Screen

Proliferating cell nuclear antigen (PCNA) is a central factor in DNA replication and repair pathways that plays an essential role in genome stability. The functional roles of PCNA are mediated through an extensive list of protein-protein interactions, each of which transmits specific information in protein assemblies. The flexibility at the PCNA-protein interaction interfaces offers opportunities for the discovery of functionally selective inhibitors of DNA repair pathways. Current fragment-based drug design methodologies can be limited by the flexibility of protein interfaces. These factors motivated an approach to defining compounds that could leverage previously identified subpockets on PCNA that are suitable for fragment-binding sites. Methodologies for screening multiple connected fragment-binding events in distinct subpockets are deployed to improve the selection of fragment combinations. A flexible backbone based on N-alkyl-glycine amides offers a scaffold to combinatorically link multiple fragments for in silico screening libraries that explore the diversity of subpockets at protein interfaces. This approach was applied to discover new potential inhibitors of DNA replication and repair that target PCNA in a multiprotein recognition site. The screens of the libraries were designed to computationally filter ligands based upon the fragments and positions to <1%, which were synthesized and tested for direct binding to PCNA. Molecular dynamics simulations also revealed distinct features of these novel molecules that block key PCNA-protein interactions. Furthermore, a Bayesian classifier predicted 15 of the 16 new inhibitors to be modulators of protein-protein interactions, demonstrating the method's utility as an effective screening filter. The cellular activities of example ligands with similar affinity for PCNA demonstrate unique properties for novel selective synergy with therapeutic DNA-damaging agents in drug-resistant contexts.

Biological Potential of Novel Methoxy and Hydroxy Substituted Heteroaromatic Amides Designed as Promising Antioxidative Agents: Synthesis, 3D-QSAR Analysis, and Biological Activity

This paper discusses antioxidative and biological activities of 25 novel amidino substituted benzamides with a variety of heteroaromatic nuclei attached to the benzamide moiety and with a variable number of methoxy or hydroxy substituents. Targeted compounds, bearing either amidino or 2-imidazolinyl substituent, were obtained in the Pinner reaction from cyano precursors. 3D-QSAR models were generated to predict antioxidative activity of the 25 novel aromatic and heteroaromatic benzamide derivatives. The compounds were tested for antioxidative activity using in vitro spectrophotometric assays. Direct validation of 3D-QSAR approach for predicting activities of novel benzamide derivatives was carried out by comparing experimental and computationally predicted antioxidative activity. Experimentally determined activities for all novel compounds were found to be within a standard deviation of error of the models. Following this, structure-activity relationships among the synthesized compounds are discussed. Furthermore, antiproliferative activity in vitro against HeLa cells as well as antibacterial and antifungal activity was tested to confirm the other biological activities of the prepared compounds.

Three-Dimensional Classification Structure-Activity Relationship Analysis Using Convolutional Neural Network

Quantitative structure-activity relationship (QSAR) techniques, especially those that possess three-dimensional attributes, such as the comparative molecular field analysis (CoMFA), are frequently used in modern-day drug design and other related research domains. However, the requirement for accurate alignment of compounds in CoMFA increases the difficulties encountered in its use. This has led to the development of several techniques-such as VolSurf, Grid-independent descriptors (GRIND), and Anchor-GRIND-which do not require such an alignment. We propose a technique to construct the prediction model that uses molecular interaction field grid potentials as inputs to convolutional neural network. The proposed model has been found to demonstrate higher accuracy compared to the conventional descriptor-based QSAR models as well as Anchor-GRIND techniques. In addition, the method is target independent, and is capable of providing useful information regarding the importance of individual atoms constituting the compounds contained in the chemical dataset used in the proposed analysis. In view of these advantages, the proposed technique is expected to find wide applications in future drug-design operations.