Number Of Hydrogen Bond Donors Research Articles

Examining functional group-dependent effects on the ionization of lignin monomers using supercritical fluid chromatography/electrospray ionization mass spectrometry.

The chemical and biological conversion of biomass-derived lignin is a promising pathway for producing valuable low molecular weight aromatic chemicals, such as vanillin or guaiacol, known as lignin monomers (LMs). Various methods employing chromatography and electrospray ionization-mass spectrometry (ESI-MS) have been developed for LM analysis, but the impact of LM chemical properties on analytical performance remains unclear. This study systematically optimized ESI efficiency for 24 selected LMs, categorized by functionality. Fractional factorial designs were employed for each LM to assess ESI parameter effects on ionization efficiency using ultra-high-performance supercritical fluid chromatography/ESI-MS (UHPSFC/ESI-MS). Molecular descriptors were also investigated to explain variations in ESI parameter responses and chromatographic retention among the LMs. Structural differences among LMs led to complex optimal ESI settings. Notably, LMs with two methoxy groups benefited from higher gas and sheath gas temperatures, likely due to their lower log P and higher desolvation energy requirements. Similarly, vinyl acids and ketones showed advantages at elevated gas temperatures. The retention in UHPSFC using a diol stationary phase was correlated with the number of hydrogen bond donors. In summary, this study elucidates structural features influencing chromatographic retention and ESI efficiency in LMs. The findings can aid in developing analytical methods for specific technical lignins. However, the absence of an adequate number of LM standards limits the prediction of LM structures solely based on ESI performance data.

Unveiling the Potential of Ent-Kaurane Diterpenoids: Multifaceted Natural Products for Drug Discovery.

Natural products hold immense potential for drug discovery, yet many remain unexplored in vast libraries and databases. In an attempt to fill this gap and meet the growing demand for effective drugs, this study delves into the promising world of ent-kaurane diterpenoids, a class of natural products with huge therapeutic potential. With a dataset of 570 ent-kaurane diterpenoids obtained from the literature, we conducted an in silico analysis, evaluating their physicochemical, pharmacokinetic, and toxicological properties with a focus on their therapeutic implications. Notably, these natural compounds exhibit drug-like properties, aligning closely with those of FDA-approved drugs, indicating a high potential for drug development. The ranges of the physicochemical parameters were as follows: molecular weights-288.47 to 626.82 g/mol; number of heavy atoms-21 to 44; the number of hydrogen bond donors and acceptors-0 to 8 and 1 to 11, respectively; the number of rotatable bonds-0 to 11; fraction Csp3-0.65 to 1; and TPSA-20.23 to 189.53 Ų. Additionally, the majority of these molecules display favorable safety profiles, with only 0.70%, 1.40%, 0.70%, and 46.49% exhibiting mutagenic, tumorigenic, reproduction-enhancing, and irritant properties, respectively. Importantly, ent-kaurane diterpenoids exhibit promising biopharmaceutical properties. Their average lipophilicity is optimal for drug absorption, while over 99% are water-soluble, facilitating delivery. Further, 96.5% and 28.20% of these molecules exhibited intestinal and brain bioavailability, expanding their therapeutic reach. The predicted pharmacological activities of these compounds encompass a diverse range, including anticancer, immunosuppressant, chemoprotective, anti-hepatic, hepatoprotectant, anti-inflammation, antihyperthyroidism, and anti-hepatitis activities. This multi-targeted profile highlights ent-kaurane diterpenoids as highly promising candidates for further drug discovery endeavors.

Machine Learning Exploration of the Relationship Between Drugs and the Blood-Brain Barrier: Guiding Molecular Modification.

This study aimed to improve the efficiency of pharmacotherapy for CNS diseases by optimizing the ability of drug molecules to penetrate the Blood-Brain Barrier (BBB). We established qualitative and quantitative databases of the ADME properties of drugs and derived characteristic features of compounds with efficient BBB penetration. Using these insights, we developed four machine learning models to predict a drug's BBB permeability by assessing ADME properties and molecular topology. We then validated the models using the B3DB database. For acyclovir and ceftriaxone, we modified the Hydrogen Bond Donors and Acceptors, and evaluated the BBB permeability using the predictive model. The machine learning models performed well in predicting BBB permeability on both internal and external validation sets. Reducing the number of Hydrogen Bond Donors and Acceptors generally improves BBB permeability. Modification only enhanced BBB penetration in the case of acyclovir and not ceftriaxone. The machine learning models developed can accurately predict BBB permeability, and many drug molecules are likely to have increased BBB penetration if the number of Hydrogen Bond Donors and Acceptors are reduced. These findings suggest that molecular modifications can enhance the efficacy of CNS drugs and provide practical strategies for drug design and development. This is particularly relevant for improving drug penetration of the BBB.

Modeling the Blood-Brain Barrier Permeability of Potential Heterocyclic Drugs via Biomimetic IAM Chromatography Technique Combined with QSAR Methodology.

Penetration through the blood-brain barrier (BBB) is desirable in the case of potential pharmaceuticals acting on the central nervous system (CNS), but is undesirable in the case of drug candidates acting on the peripheral nervous system because it may cause CNS side effects. Therefore, modeling of the permeability across the blood-brain barrier (i.e., the logarithm of the brain to blood concentration ratio, log BB) of potential pharmaceuticals should be performed as early as possible in the preclinical phase of drug development. Biomimetic chromatography with immobilized artificial membrane (IAM) and the quantitative structure-activity relationship (QSAR) methodology were successful in modeling the blood-brain barrier permeability of 126 drug candidates, whose experimentally-derived lipophilicity indices and computationally-derived molecular descriptors (such as molecular weight (MW), number of rotatable bonds (NRB), number of hydrogen bond donors (HBD), number of hydrogen bond acceptors (HBA), topological polar surface area (TPSA), and polarizability (α)) varied by class. The QSARs model established by multiple linear regression showed a positive effect of the lipophilicity (log kw, IAM) and molecular weight of the compound, and a negative effect of the number of hydrogen bond donors and acceptors, on the log BB values. The model has been cross-validated, and all statistics indicate that it is very good and has high predictive ability. The simplicity of the developed model, and its usefulness in screening studies of novel drug candidates that are able to cross the BBB by passive diffusion, are emphasized.

Topical drug delivery of licorice flavonoids and their structure-affinity relationship with the porcine skin

Traditional Chinese medicine (TCM) possesses a long history of clinical application in the transdermal prescriptions, however, their transdermal mechanisms had not been clearly elucidated ascribing to their complex ingredients and different content. In this work, we firstly provide insights reports on the transdermal mechanisms of licorice flavonoids (LFs) extracts and the structure-affinity relationship between LFs compounds and the skin. The permeation properties of LFs extracts were conducted on the porcine skin and rat skin. It was highlighted that chalones and flavanone molecules were easier to permeate through the skin from LFs extracts than isoflavone. Differ from other LFs compounds, insoluble licochalcone A and licoflavone A obtained a higher permeation amount from LFs extracts than its corresponding monomer ingredients affirmed through molecular dynamics simulation study. In the stratum corneum (SC), among 33 kinds of flavonoids, glabridin possessed the highest compatibility with the skin lipids. Furthermore, interaction parameters (χ) values between the drugs and ceramide were negatively correlated with the number of hydrogen bond donor (HBD), polarizability and polar surface area of LFs compounds. The HBD of LFs molecules could help drugs bind to the skin lipids and then formed appropriate hydrogen bond interactions with the skin lipids, further improving the affinity between the drugs and the skin. Moreover, LFs components permeated through the porcine skin by disturbing the lipids arrangement in the SC. In the epidermis and dermis, the positive number and absorption rate of LFs extracts by HaCaT cells was significantly higher than melanocytes and HSF cells after 6 h. This study provided a new approach to unveil the molecular mechanisms of LFs permeation, which gave a reference for permeation of other TCM extracts.

Multi-cavity molecular descriptor interconnections: Enhanced protocol for prediction of serum albumin drug binding

The role of human serum albumin (HSA) in the transport of molecules predicates its involvement in the determination of drug distribution and metabolism. Optimization of ADME properties are analogous to HSA binding thus this is imperative to the drug discovery process. Currently, various in silico predictive tools exist to complement the drug discovery process, however, the prediction of possible ligand-binding sites on HSA has posed several challenges. Herein, we present a strong and deeper-than-surface case for the prediction of HSA-ligand binding sites using multi-cavity molecular descriptors by exploiting all experimentally available and crystallized HSA-bound drugs. Unlike previously proposed models found in literature, we established an in-depth correlation between the physicochemical properties of available crystallized HSA-bound drugs and different HSA binding site characteristics to precisely predict the binding sites of investigational molecules. Molecular descriptors such as the number of hydrogen bond donors (nHD), number of heteroatoms (nHet), topological polar surface area (TPSA), molecular weight (MW), and distribution coefficient (LogD) were correlated against HSA binding site characteristics, including hydrophobicity, hydrophilicity, enclosure, exposure, contact, site volume, and donor/acceptor ratio. Molecular descriptors nHD, TPSA, LogD, nHet, and MW were found to possess the most inherent capacities providing baseline information for the prediction of serum albumin binding site. We believe that these associations may form the bedrock for establishing a solid correlation between the physicochemical properties and Albumin binding site architecture. Information presented in this report would serve as critical in provisions of rational drug designing as well as drug delivery, bioavailability, and pharmacokinetics.

DDDR-31. TARGETING CANCER STEM CELLS WITH A CDK2 INHIBITOR IN GLIOBLASTOMA

Abstract Glioblastoma (GB) is the most common and aggressive adult brain tumor with no cure. Brain tumor stem cells (BTSCs) are a rare population of self-renewing multipotent stem cells in GB that contribute to tumorigenesis, therapeutic resistance and tumour recurrence. Here, we report a CDK2 inhibitor that suppresses BTSCs via OSM/OSMR/STAT3 signalling pathway. To begin with we performed high throughput screening (HTS) of ~8400 compounds including FDA-approved drugs in patient derived human BTSCs that naturally harbour EGFRvIII mutation and elevated STAT3 phosphorylation, in search of compounds that can suppress EGFRvIII/OSMR/STAT3 oncogenic pathway. The screen led to the identification of a panel of CDK inhibitors that possessed important characteristics including a) ability to cross the blood-brain barrier with mall molecular weights of 277-566 kDa, b) low Topological Polar Surface Area (TPSA) of 76-115 A°, c) a low number of Hydrogen Bond Donors (HBDs) (1-4), and d) cLogP values ranging from 2-4. Following counter screens, we focused on a CDK2 inhibitor that significantly and most efficiently reduced OSM/OSMR signalling and STAT3 activation. Importantly, we found that the compound inhibited the self-renewal and growth of BTSCs in EGFRvIII subtype of BTSCs. This research has led to future investigation on in vivo assessment of this CDK2 inhibitor in combination with ionizing radiation and chemotherapy in preclinical models of GB.

Finding structural requirements of structurally diverse α-glucosidase and α-amylase inhibitors through validated and predictive 2D-QSAR and 3D-QSAR analyses

Diabetes mellitus (DM) is a chronic metabolic disorder characterized by hyperglycemic state. The α-glucosidase and α-amylase are considered two major targets for the management of Type 2 DM due to their ability of metabolizing carbohydrates into simpler sugars. In the current study, cheminformatics analyses were performed to develop validated and predictive models with a dataset of 187 α-glucosidase and α-amylase dual inhibitors. Separate linear, interpretable and statistically robust 2D-QSAR models were constructed with datasets containing the activities of α-glucosidase and α-amylase inhibitors with an aim to explain the crucial structural and physicochemical attributes responsible for higher activity towards these targets. Consequently, some descriptors of the models pointed out the importance of specific structural moieties responsible for the higher activities for these targets and on the other hand, properties such as ionization potential and mass of the compounds as well as number of hydrogen bond donors in molecules were found to be crucial in determining the binding potentials of the dataset compounds. Statistically significant 3D-QSAR models were developed with both α-glucosidase and α-amylase inhibition datapoints to estimate the importance of 3D electrostatic and steric fields for improved potentials towards these two targets. Molecular docking performed with selected compounds with homology model of α-glucosidase and X-ray crystal structure of α-amylase largely supported the interpretations obtained from the cheminformatic analyses. The current investigation should serve as important guidelines for the design of future α-glucosidase and α-amylase inhibitors. Besides, the current investigation is entirely performed by using non-commercial open-access tools to ensure easy accessibility and reproducibility of the investigation which may help researchers throughout the world to work more on drug design and discovery.

Prediction of protonation constants of hydrazones and Schiff bases derived from pyridoxal 5′-phosphate, pyridoxal, 3-hydroxyisonicotinaldehyde and salicylic aldehyde

Hydrazones and Schiff bases are potent metal chelators applied in various areas such as analytical chemistry and medicine because of their various biological activities, including antimicrobial, antitumor, analgesic, and anti-inflammatory action. The hydrazones and Schiff bases derived from aldehyde forms of the B6 vitamin are arguably the most biologically active representatives of these classes. Protonation constants associated with pharmacokinetic characteristics are among the most important properties of drug-like molecules as they affect their binding to proteins, membrane transport, excretion, etc. The present paper accumulates data on protonation constants of hydrazones and Schiff bases derived from pyridoxal, pyridoxal 5′-phosphate, 3-hydroxyisonicotinaldehyde, and salicylic aldehyde available in the literature. A novel simple chemometric model is constructed based on these data to describe the protolytic equilibria constants. The model proposed takes into account such molecular descriptors as molecular weight, number of hydrogen bond donors and acceptors, polarizable surface area, and log P. The contribution of ionic strength is also included in the model. The adequacy of the model and the statistical significance of separate parameters are studied using standard mathematical procedures. The protonation constants of five hydrazones derived from pyridoxal and hydrazides of isonicotinic, 2- and 3-furoic, 2- and 3-thiophenecarboxylic acids are determined experimentally in aqueous solution at T = 298.2 K, p = 0.1 MPa and I = 0.50 mol/L (NaCl) and compared with the model predictions. Protonation constants determined can be used further in studies of coordination equilibria of the hydrazones.

Estimating the hydrophobicity extent of molecular fragments using reversed-phase liquid chromatography.

A fast HPLC method was developed to study the hydrophobicity extent of pharmaceutically relevant molecular fragments. By this strategy, the reduced amount of sample available for physico-chemical evaluations in early-phase drug discovery programs does not represent a limiting factor. The sixteen acid fragments investigated were previously synthesized also determining potentiometrically their experimental log D values. For four fragments it was not possible to determine such property since their values were outside of the instrumental working range (2<pKa <12). An RP-HPLC method was therefore optimized. For each scrutinized method, some derived chromatographic indices were calculated, and Pearson's correlation coefficient (r) allowed to select the so-called "φ0 index" as the best correlating with the log D. The was fixed at 3.5 and a modification of some variables [organic modifier (methanol vs. ACN), stationary phase (octyl vs. octadecyl), presence/absence of the additives n-octanol, n-butylamine, and n-octylamine], allowed to select the best correlation conditions, producing a r=0.94 (p<0.001). Importantly, the φ0 index enabled the estimation of log D values for four fragments which were unattainable by potentiometric titration. Moreover, a series of molecular descriptors were calculated to identify the chemical characteristics of the fragments explaining the obtained φ0 . The number of hydrogen bond donors and the index of cohesive interaction correlated with the experimental data.

TARGETING CANCER STEM CELLS WITH A CDK2 INHIBITOR IN GLIOBLASTOMA

Abstract Glioblastoma (GB) is the most common and aggressive adult brain tumor with no cure. Brain tumor stem cells (BTSCs) are a rare population of self-renewing multipotent stem cells in GB that contribute to tumorigenesis, therapeutic resistance, and tumour recurrence. Here, we report a CDK2 inhibitor that suppresses BTSCs via OSM/OSMR/STAT3 signalling pathway. To begin with, we performed high throughput screening (HTS) of ~8400 compounds including FDA-approved drugs in patient derived human BTSCs that naturally harbour EGFRvIII mutation and elevated STAT3 phosphorylation, in search of compounds that can suppress EGFRvIII/OSMR/STAT3 oncogenic pathway. The screen led to the identification of a panel of CDK inhibitors that possessed important characteristics including a) ability to cross the blood-brain barrier with mall molecular weights of 277-566 kDa, b) low Topological Polar Surface Area (TPSA) of 76-115 A°, c) a low number of Hydrogen Bond Donors (HBDs) (1-4), and d) cLogP values ranging from 2-4. Following counter screens, we focused on a CDK2 inhibitor that significantly and most efficiently reduced OSM/OSMR signalling and STAT3 activation. Importantly, we found that the compound inhibited the self-renewal and growth of BTSCs in EGFRvIII subtype of BTSCs. This research has led to future investigation on in vivo assessment of this CDK2 inhibitor in combination with ionizing radiation and chemotherapy in preclinical models of GB.

High thermal conductivity and excellent mechanical properties of liquid crystal co‐polyester based on hydron bond

AbstractHigh thermal conductivity liquid crystal polymer has become a hot topic in electronic device research. Herein, we introduce a series of thermotropic liquid crystals synthesized by diphenyl diol, m‐phthalic acid, and 5‐hydroxyisophthalic acid. The intermolecular forces are tuned by adjusting the ratio (0, 20%, 40%, 60%, 80%, 100%) of monomers containing hydrogen bond donors in copolymers. The formation and dissociation of hydrogen bonds are analyzed from the red shift and blue shift of infrared spectra at variable temperatures. The thermal conductivity is increased to 0.31 Wm−1 k−1. Meanwhile, its mechanical properties and thermal stability have improved significantly. A sharp increment of 2120% in tensile strength was observed, the maximum tensile strength is 17.39 MPa. While Young's modulus increased from 0.26 to 1.224 GPa. The increase in intermolecular forces is the main reason for this result. Moreover,X‐raysingle‐crystal diffraction, polarizing microscope, differential scanning calorimetry, and thermogravimetric analysis (TGA) were also analyzed to further explore the thermal conduction mechanism. The formation of hydrogen bonds makes the linear polymer chain segments undergo reversible cross‐linking, which affects their crystallization behavior and provides more thermal conduction pathways. By controlling the number of hydrogen bond donors to control the cross‐linking density, the thermal conductivity and mechanical properties can be greatly improved.

COSMO-RS predictive screening of type 5 hydrophobic deep eutectic solvents for selective platform chemicals absorption

Platform chemicals are crucial to the development of designer chemicals in industry; however, the utilization of these chemicals is limited from requiring separation from an aqueous phase. Type 5 hydrophobic deep eutectic solvents (HDES) have recently proven their ability to extract various low concentration solutes from aqueous solution. However, identifying a suitable HDES experimentally is a daunting task due to the large number of hydrogen bond donors (HBD), hydrogen bond acceptors (HBA), and their mixing ratio, with the HDES being ever increasing. In this study, Conductor-like Screening MOdel for Real Solvents (COSMO-RS) was utilized for over one hundred HDES and their relative solubilization ability for sorbitol, 5-hydroxymethyl furfural, and levulinic acid. Moreover, energetic mechanisms of solubilization were analyzed through the prediction of sigma profiles, sigma potentials, activity coefficients, and excess enthalpy of absorption. COSMO-RS results show that HBAs with tetra alkyl chains and amino acid-based HBDs are suitable HDES components for absorbing sorbitol, 5-hydroxymethyl furfural, and levulinic acid through a combination of van der Waals and hydrogen bonding interactions. These interactions are quantitatively examined through calculated excess enthalpy predictions, for example tetrabutylammonium bromide and arginine with a compositional ratio of 8:1, respectively, had an excess enthalpy of mixing with HMF of −7.9 kcal/mol despite the steric hindrance factor valuing ∼ 4.0 kcal/mol.

Design synthesis and biological evaluation of thiophene 2- pentafluoro benzamide derivatives as antitubercular agent

Tuberculosis, a major contagious air-borne disease, killing millions in a year. Many of the existing anti-tubercular drugs have acquired bacterial resistance. Though they are highly bactericidal but, later on due to several factors like poor patient compliance, incomplete therapy, etc. leading to treatment relapse. This enables researcher to focus on identifying new leads utilizing structure-based drug design which plays a crucial role in drug development. The mycolic acid synthesized by Pks 13 is identified as a perfect target and hence a series of molecules were designed and the best fit ligands with a least energy were picked and analysed for Insilco bioactivity and drug likeness. Five best docked Molecules satisfied the Lipinski rule of five, namely the number of hydrogen bond donors, acceptors, log P, total polar surface area and the number of rotatable bonds. The bioactivity was found to be moderate to excellent against the receptor. The target molecules were synthesised via amino esters to yield pentafluorinated thiophene derivatives. Among the series, molecule 12 was found to be more potent when tested for antitubercular activity by Microplate Alomar blue assay technique.

Quantitative structure-activity relationship for the photooxidation of aromatic micro-pollutants induced by graphene oxide in water

The photocatalytic degradation behavior of aromatic micro-pollutants (AMPs) exhibits complexity and uncertainty, which mainly depends on the properties of different substituents on benzene. And with similar catalytic reaction substrates, the reaction rate constant could reveal the influence of different characteristics of molecular structure in a specific system. Therefore, the photooxidation pseudo first-order kinetic rate constants (kobs) of 30 AMPs were experimentally determined in Photo-GO system. A quantitative structure-activity relationship (QSAR) model for predicting the photooxidation reaction of AMPs has been developed by stepwise multiple linear regression (MLR) method, based on the lg kobs and representative molecule descriptors (20 in total) including physicochemical, quantum chemical and electrostatic descriptors. Afterwards, Radj2, QLOO2, and Qext2 were calculated as 0.870, 0.841, and 0.732 respectively, which exhibited the excellent goodness-of-fit, robustness, and predictability of the QSAR model, indicating its great prediction ability for photooxidation behavior of AMPs. Meanwhile, during the photooxidation process of AMPs with GO, the model revealed that the one-electron oxidation potential (Eox), molecular dipole moment (μ), and number of hydrogen bond donors (#HD) were the most important molecular structural parameters, which showed that the single electron transfer pathway and adsorption were as the significant steps. Additionally, the Hammett correlation showed that photooxidation of AMPs in Photo-GO system is of typical electrophilic reactions, which demonstrated that the electron-donating substituents could promote the photooxidation of AMPs. The QSAR model was constructed and evaluated to perform the prediction of AMPs reaction kinetics, which provided a guidance for the study of the mechanism and selective oxidation of AOPs photooxidation system based on GO.

Evaluating the feasibility of SMILES-based autoencoders for drug discovery

The vast majority of molecules with desirable drug-like properties have not yet been discovered. With the advent of machine learning for de novo molecular generation, the process of designing these molecules has become increasingly efficient. However, to what extent are these machine learning models actually learning chemical properties versus memorizing the syntax of a training set? In this project, we trained a Simplified Molecular Input Line Entry System (SMILES)-based generative autoencoder for up to 200 epochs to investigate whether the latent space can separate molecules based on five chemical properties (partition coefficient, molecular weight, topological polar surface area, number of hydrogen bond donors, and number of hydrogen bond acceptors) and how generated molecules compare to the training set. We hypothesized that the model would preferentially encode molecular weight and that generated molecules would be similar to the training set. Consistent with our hypothesis, the model quickly learned to distinguish molecules primarily by their molecular weight, while other properties were considered to a lesser extent. Moreover, generated molecules were very similar to the training set both in terms of structure and properties. These results suggested that the model overfits the training set. In particular, the model best learns chemical properties that directly depend on atomic composition while it is difficult for the model to encode higher-level properties that rely on connectivity and structure. Our results may represent fundamental limitations of SMILES-based generative models and could assist in development of new research to mitigate these issues.

Physics-informed graph neural networks for predicting cetane number with systematic data quality analysis

Designing alternative fuels for advanced compression ignition engines necessitates a predictive model for cetane number (CN). In this study, the physics-informed graph neural networks are introduced for a reliable CN prediction by considering molecular features pertinent to the physical properties of molecules that affect CN. The reliability of measured data is another key factor to consider for improving the predictive model. Various experimental instruments for measuring CN exist, including standard and non-standard methods. In this regard, a systematic data quality analysis was carried out for the total 630 CNs collected from literature and new measurements in this study using Advanced Fuel Ignition Delay Analyzer (AFIDA). The results from this data curation process were reflected in the model by imposing lower sample weights on the data coming from less reliable measurement techniques. This approach effectively maximized the prediction accuracy while incorporating data from all available sources. Using the sample weights decreased the mean absolute error (MAE) up to 0.8 CN units. The accuracy was also improved by introducing the CN-related physical properties (the number of hydrogen bond donors and acceptors); the test set MAE is 5.74 and 7.01 for the model with and without such properties, respectively. Investigating molecular structural effects on CN was also carried out to gain chemical insights into factors used to design new fuel candidates. The dimensionality reduction analysis of feature vectors showed a clear clustering in terms of functional groups and CN and the structural effect derived from the model was consistent with the physicochemical insights. This physics-informed model and data curation would be helpful for accurate CN prediction and inform rational fuel design.

Modeling and insights into the structural characteristics of drug-induced autoimmune diseases.

The incidence and complexity of drug-induced autoimmune diseases (DIAD) have been on the rise in recent years, which may lead to serious or fatal consequences. Besides, many environmental and industrial chemicals can also cause DIAD. However, there are few effective approaches to estimate the DIAD potential of drugs and other chemicals currently, and the structural characteristics and mechanism of action of DIAD compounds have not been clarified. In this study, we developed the in silico models for chemical DIAD prediction and investigated the structural characteristics of DIAD chemicals based on the reliable drug data on human autoimmune diseases. We collected 148 medications which were reported can cause DIAD clinically and 450 medications that clearly do not cause DIAD. Several different machine learning algorithms and molecular fingerprints were combined to develop the in silico models. The best performed model provided the good overall accuracy on validation set with 76.26%. The model was made freely available on the website http://diad.sapredictor.cn/. To further investigate the differences in structural characteristics between DIAD chemicals and non-DIAD chemicals, several key physicochemical properties were analyzed. The results showed that AlogP, molecular polar surface area (MPSA), and the number of hydrogen bond donors (nHDon) were significantly different between the DIAD and non-DIAD structures. They may be related to the DIAD toxicity of chemicals. In addition, 14 structural alerts (SA) for DIAD toxicity were detected from predefined substructures. The SAs may be helpful to explain the mechanism of action of drug induced autoimmune disease, and can used to identify the chemicals with potential DIAD toxicity. The structural alerts have been integrated in a structural alert-based web server SApredictor (http://www.sapredictor.cn). We hope the results could provide useful information for the recognition of DIAD chemicals and the insights of structural characteristics for chemical DIAD toxicity.

Membrane Permeating Macrocycles: Design Guidelines from Machine Learning.

The ability to predict cell-permeable candidate molecules has great potential to assist drug discovery projects. Large molecules that lie beyond the Rule of Five (bRo5) are increasingly important as drug candidates and tool molecules for chemical biology. However, such large molecules usually do not cross cell membranes and cannot access intracellular targets or be developed as orally bioavailable drugs. Here, we describe a random forest (RF) machine learning model for the prediction of passive membrane permeation rates developed using a set of over 1000 bRo5 macrocyclic compounds. The model is based on easily calculated chemical features/descriptors as independent variables. Our random forest (RF) model substantially outperforms a multiple linear regression model based on the same features and achieves better performance metrics than previously reported models using the same underlying data. These features include: (1) polar surface area in water, (2) the octanol-water partitioning coefficient, (3) the number of hydrogen-bond donors, (4) the sum of the topological distances between nitrogen atoms, (5) the sum of the topological distances between nitrogen and oxygen atoms, and (6) the multiple molecular path count of order 2. The last three features represent molecular flexibility, the ability of the molecule to adopt different conformations in the aqueous and membrane interior phases, and the molecular "chameleonicity." Guided by the model, we propose design guidelines for membrane-permeating macrocycles. It is anticipated that this model will be useful in guiding the design of large, bioactive molecules for medicinal chemistry and chemical biology applications.

Phytovid19: a compilation of phytochemicals research in coronavirus.

The COVID-19 pandemic has immensely impacted global health causing colossal damage. The recent outbreak of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has increased the quest to explore phytochemicals as treatment options. We summarize phytochemicals with activity against various coronaviruses including SARS-CoV and Middle East respiratory syndrome coronavirus (MERS-CoV). We compiled 705 phytochemical compounds through text mining of 893 PubMed articles. The physicochemical properties including molecular weight, lipophilicity, and the number of hydrogen bond donors and acceptors were determined from the structures of these compounds. A structure-based evaluation of these properties with respect to drug likeness showed that most compounds have a positive score of drug likeness. QSAR analysis showed that 5 descriptors, namely polar surface area, relative polar surface area, number of hydrogen bond donors, solubility, and lipophilicity, are significantly related to IC50. We envisage that these phytochemicals could be further explored for developing new potential therapeutic molecules for COVID-19.Supplementary InformationThe online version contains supplementary material available at 10.1007/s11224-022-02035-6.