Large, chemically diverse dataset of logP measurements for benchmarking studies

Comprehensive benchmarking of computational tools for predicting toxicokinetic and physicochemical properties of chemicals

Ensuring the safety of chemicals for environmental and human health involves assessing physicochemical (PC) and toxicokinetic (TK) properties, which are crucial for absorption, distribution, metabolism, excretion, and toxicity (ADMET). Computational methods play a vital role in predicting these properties, given the current trends in reducing experimental approaches, especially those that involve animal experimentation. In the present manuscript, twelve software tools implementing Quantitative Structure-Activity Relationship (QSAR) models were selected for the prediction of 17 relevant PC and TK properties. A total of 41 validation datasets were collected from the literature, curated and used for assessing the models' external predictivity, emphasizing the performance of the models inside the applicability domain. Overall, the results confirmed the adequate predictive performance of the majority of the selected tools, with models for PC properties (R2 average = 0.717) generally outperforming those for TK properties (R2 average = 0.639 for regression, average balanced accuracy = 0.780 for classification). Notably, several of the tools evaluated exhibited good predictivity across different properties and were identified as recurring optimal choices. Moreover, a systematic analysis of the chemical space covered by the external validation datasets confirmed the validity of the collected results for relevant chemical categories (e.g., drugs and industrial chemicals), further increasing the confidence in the overall evaluation. The best performing models were ultimately suggested for each investigated property and proposed as robust computational tools for high-throughput assessment of highly relevant chemical properties. SCIENTIFIC CONTRIBUTION: The present manuscript provides an overview of the state-of-the-art available computational tools for predicting the PC and TK properties of chemicals. The results here offer valuable guidance to researchers, regulatory authorities, and the industry in identifying robust computational tools suitable for predicting relevant chemical properties in the context of chemical design, toxicity and environmental fate assessment.

Overcoming Challenges in Small-Molecule Drug Bioavailability: A Review of Key Factors and Approaches

The bioavailability of small-molecule drugs remains a critical challenge in pharmaceutical development, significantly impacting therapeutic efficacy and commercial viability. This review synthesizes recent advances in understanding and overcoming bioavailability limitations, focusing on key physicochemical and biological factors influencing drug absorption and distribution. We examine cutting-edge strategies for enhancing bioavailability, including innovative formulation approaches, rational structural modifications, and the application of artificial intelligence in drug design. The integration of nanotechnology, 3D printing, and stimuli-responsive delivery systems are highlighted as promising avenues for improving drug delivery. We discuss the importance of a holistic, multidisciplinary approach to bioavailability optimization, emphasizing early-stage consideration of ADME properties and the need for patient-centric design. This review also explores emerging technologies such as CRISPR-Cas9-mediated personalization and microbiome modulation for tailored bioavailability enhancement. Finally, we outline future research directions, including advanced predictive modeling, overcoming biological barriers, and addressing the challenges of emerging therapeutic modalities. By elucidating the complex interplay of factors affecting bioavailability, this review aims to guide future efforts in developing more effective and accessible small-molecule therapeutics.

Autonomous, multiproperty-driven molecular discovery: From predictions to measurements and back

A closed-loop, autonomous molecular discovery platform driven by integrated machine learning tools was developed to accelerate the design of molecules with desired properties. We demonstrated two case studies on dye-like molecules, targeting absorption wavelength, lipophilicity, and photooxidative stability. In the first study, the platform experimentally realized 294 unreported molecules across three automatic iterations of molecular design-make-test-analyze cycles while exploring the structure-function space of four rarely reported scaffolds. In each iteration, the property prediction models that guided exploration learned the structure-property space of diverse scaffold derivatives, which were realized with multistep syntheses and a variety of reactions. The second study exploited property models trained on the explored chemical space and previously reported molecules to discover nine top-performing molecules within a lightly explored structure-property space.

Recent Studies of Artificial Intelligence on In Silico Drug Absorption

Absorption is an important area of research in pharmacochemistry and drug development, because the drug has to be absorbed before any drug effects can occur. Furthermore, the ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) profile of drugs can be directly and considerably altered by modulating factors affecting absorption. Many drugs in development fail because of poor absorption. The research and continuous efforts of researchers in recent years have brought many successes and promises in drug absorption property prediction, especially in silico, which helps to reduce the time and cost significantly for screening undesirable drug candidates. In this report, we explicitly provide an overview of recent in silico studies on predicting absorption properties, especially from 2019 to the present, using artificial intelligence. Additionally, we have collected and investigated public databases that support absorption prediction research. On those grounds, we also proposed the challenges and development directions of absorption prediction in the future. We hope this review can provide researchers with valuable guidelines on absorption prediction to facilitate the development of newer approaches in drug discovery.

A robust, viable, and resource sparing HPLC-based logP method applied to common drugs

Reliable, experimentally determined partition coefficient P (logP) for most drugs are often unavailable in the literature. Many values are from in silico predictions and may not accurately reflect drug lipophilicity. In this study, a robust, viable, and resource sparing method to measure logP was developed using reverse phase high performance liquid chromatography (RP-HPLC). The logP of twelve common drugs was measured using calibration curves at pH 6 and 9 that were created using reference standards with well-established logP. The HPLC method reported here can be used for high throughput estimation of logP of commonly used drugs. LogP values here showed general agreement with the other few HPLC-based literature logP values available. Additionally, the HPLC-based logP values found here agreed partially with literature logP values found using other methodologies (±10%). However, there was no strong agreement since there are few experimentally determined literature logP values. This paper shows a facile method to estimate logP without using octanol or computational approaches. This method has excellent promise to provide reliable logP values of commonly used drugs available in literature. A larger pool of reliable logP values of commonly drugs has promise to improve quality of medicinal chemistry and pharmacokinetic (PK) models.

LogD7.4 prediction enhanced by transferring knowledge from chromatographic retention time, microscopic pKa and logP

Lipophilicity is a fundamental physical property that significantly affects various aspects of drug behavior, including solubility, permeability, metabolism, distribution, protein binding, and toxicity. Accurate prediction of lipophilicity, measured by the logD7.4 value (the distribution coefficient between n-octanol and buffer at physiological pH 7.4), is crucial for successful drug discovery and design. However, the limited availability of data for logD modeling poses a significant challenge to achieving satisfactory generalization capability. To address this challenge, we have developed a novel logD7.4 prediction model called RTlogD, which leverages knowledge from multiple sources. RTlogD combines pre-training on a chromatographic retention time (RT) dataset since the RT is influenced by lipophilicity. Additionally, microscopic pKa values are incorporated as atomic features, providing valuable insights into ionizable sites and ionization capacity. Furthermore, logP is integrated as an auxiliary task within a multitask learning framework. We conducted ablation studies and presented a detailed analysis, showcasing the effectiveness and interpretability of RT, pKa, and logP in the RTlogD model. Notably, our RTlogD model demonstrated superior performance compared to commonly used algorithms and prediction tools. These results underscore the potential of the RTlogD model to improve the accuracy and generalization of logD prediction in drug discovery and design. In summary, the RTlogD model addresses the challenge of limited data availability in logD modeling by leveraging knowledge from RT, microscopic pKa, and logP. Incorporating these factors enhances the predictive capabilities of our model, and it holds promise for real-world applications in drug discovery and design scenarios.

Bridging the Gap between Differential Mobility, Log S, and Log P Using Machine Learning and SHAP Analysis

Aqueous solubility, log S, and the water-octanol partition coefficient, log P, are physicochemical properties that are used to screen the viability of drug candidates and to estimate mass transport in the environment. In this work, differential mobility spectrometry (DMS) experiments performed in microsolvating environments are used to train machine learning (ML) frameworks that predict the log S and log P of various molecule classes. In lieu of a consistent source of experimentally measured log S and log P values, the OPERA package was used to evaluate the aqueous solubility and hydrophobicity of 333 analytes. With ion mobility/DMS data (e.g., CCS, dispersion curves) as input, we used ML regressors and ensemble stacking to derive relationships with a high degree of explainability, as assessed via SHapley Additive exPlanations (SHAP) analysis. The DMS-based regression models returned scores of R² = 0.67 and RMSE = 1.03 ± 0.10 for log S predictions and R² = 0.67 and RMSE = 1.20 ± 0.10 for log P after 5-fold random cross-validation. SHAP analysis reveals that the regressors strongly weighted gas-phase clustering in log P correlations. The addition of structural descriptors (e.g., # of aromatic carbons) improved log S predictions to yield RMSE = 0.84 ± 0.07 and R² = 0.78. Similarly, log P predictions using the same data resulted in an RMSE of 0.83 ± 0.04 and R² = 0.84. The SHAP analysis of log P models highlights the need for additional experimental parameters describing hydrophobic interactions. These results were achieved with a smaller dataset (333 instances) and minimal structural correlation compared to purely structure-based models, underscoring the value of employing DMS data in predictive models.

Development and test of highly accurate endpoint free energy methods. 2: Prediction of logarithm of n-octanol-water partition coefficient (logP) for druglike molecules using MM-PBSA method

The logarithm of n-octanol-water partition coefficient (logP) is frequently used as an indicator of lipophilicity in drug discovery, which has substantial impacts on the absorption, distribution, metabolism, excretion, and toxicity of a drug candidate. Considering that the experimental measurement of the property is costly and time-consuming, it is of great importance to develop reliable prediction models for logP. In this study, we developed a transfer free energy-based logP prediction model-FElogP. FElogP is based on the simple principle that logP is determined by the free energy change of transferring a molecule from water to n-octanol. The underlying physical method to calculate transfer free energy is the molecular mechanics-Poisson Boltzmann surface area (MM-PBSA), thus this method is named as free energy-based logP (FElogP). The superiority of FElogP model was validated by a large set of 707 structurally diverse molecules in the ZINC database for which the measurement was of high quality. Encouragingly, FElogP outperformed several commonly-used QSPR or machine learning-based logP models, as well as some continuum solvation model-based methods. The root-mean-square error (RMSE) and Pearson correlation coefficient (R) between the predicted and measured values are 0.91 log units and 0.71, respectively, while the runner-up, the logP model implemented in OpenBabel had an RMSE of 1.13 log units and R of 0.67. Given the fact that FElogP was not parameterized against experimental logP directly, its excellent performance is likely to be expanded to arbitrary organic molecules covered by the general AMBER force fields.

Identification of vital chemical information via visualization of graph neural networks

Qualitative or quantitative prediction models of structure-activity relationships based on graph neural networks (GNNs) are prevalent in drug discovery applications and commonly have excellently predictive power. However, the network information flows of GNNs are highly complex and accompanied by poor interpretability. Unfortunately, there are relatively less studies on GNN attributions, and their developments in drug research are still at the early stages. In this work, we adopted several advanced attribution techniques for different GNN frameworks and applied them to explain multiple drug molecule property prediction tasks, enabling the identification and visualization of vital chemical information in the networks. Additionally, we evaluated them quantitatively with attribution metrics such as accuracy, sparsity, fidelity and infidelity, stability and sensitivity; discussed their applicability and limitations; and provided an open-source benchmark platform for researchers. The results showed that all attribution techniques were effective, while those directly related to the predicted labels, such as integrated gradient, preferred to have better attribution performance. These attribution techniques we have implemented could be directly used for the vast majority of chemical GNN interpretation tasks.

Fast Prediction of Lipophilicity of Organofluorine Molecules: Deep Learning-Derived Polarity Characters and Experimental Tests

Fast and accurate estimation of lipophilicity for organofluorine molecules is in great demand for accelerating drug and materials discovery. A lipophilicity data set of organofluorine molecules (OFL data set), containing 1907 samples, is constructed through density functional theory (DFT) calculations and experimental measurements. An efficient and interpretable model, called PoLogP, is developed to predict the n-octanol/water partition coefficient, log P_o/w, of organofluorine molecules on the basis of the descriptors of polarization, which is a combination of polarity descriptors, including the molecular polarity index and molecular polarizability (α), and hydrogen bond (HBs) index, consisting of the number of donors (N_HB^D) and acceptors (N_HB^A and N_HB-F^A). The present PoLogP with a combination of polarity descriptors is demonstrated to perform better than the dipole moment (μ) alone for the F-contained molecules. With the aid of a multilevel attention graph convolutional neural network model, the fast generation of polarity descriptors of organofluorine molecules could be achieved with the DFT accuracy based only on a topological molecular graph structure. The performance of PoLogP is further validated on synthesized organofluorine molecules and 2626 non-fluorinated molecules with satisfactory accuracy, highlighting the potential usage of PoLogP in high-throughput screening of the functional molecules with the desired solubility in various solvent media.

Molecular partition coefficient from machine learning with polarization and entropy embedded atom-centered symmetry functions

Efficient prediction of the partition coefficient (log P) between polar and non-polar phases could shorten the cycle of drug and materials design. In this work, a descriptor, named 〈q - ACSFs〉_conf, is proposed to take the explicit polarization effects in the polar phase and the conformation ensemble of energetic and entropic significance in the non-polar phase into consideration. The polarization effects are involved by embedding the partial charge directly derived from force fields or quantum chemistry calculations into the atom-centered symmetry functions (ACSFs), together with the entropy effects, which are averaged according to the Boltzmann distribution of different conformations taken from the similarity matrix. The model was trained with high-dimensional neural networks (HDNNs) on a public dataset PhysProp (with 41 039 samples). Satisfactory log P prediction performance was achieved on three other datasets, namely, Martel (707 molecules), Star & Non-Star (266) and Huuskonen (1870). The present 〈q - ACSFs〉_conf model was also applicable to n-carboxylic acids with the number of carbons ranging from 2 to 14 and 54 kinds of organic solvent. It is easy to apply the present method to arbitrary sized systems and give a transferable atom-based partition coefficient.

Small Molecules as Toll-like Receptor 4 Modulators Drug and In-House Computational Repurposing

The innate immunity toll-like receptor 4 (TLR4) system is a receptor of paramount importance as a therapeutic target. Virtual screening following a “computer-aided drug repurposing” approach was applied to the discovery of novel TLR4 modulators with a non-lipopolysaccharide-like structure. We screened almost 29,000 approved drugs and drug-like molecules from commercial, public, and in-house academia chemical libraries and, after biological assays, identified several compounds with TLR4 antagonist activity. Our computational protocol showed to be a robust approach for the identification of hits with drug-like scaffolds as possible inhibitors of the TLR4 innate immune pathways. Our collaborative work broadens the chemical diversity for inspiration of new classes of TLR4 modulators.

MRlogP: Transfer Learning Enables Accurate logP Prediction Using Small Experimental Training Datasets

Small molecule lipophilicity is often included in generalized rules for medicinal chemistry. These rules aim to reduce time, effort, costs, and attrition rates in drug discovery, allowing the rejection or prioritization of compounds without the need for synthesis and testing. The availability of high quality, abundant training data for machine learning methods can be a major limiting factor in building effective property predictors. We utilize transfer learning techniques to get around this problem, first learning on a large amount of low accuracy predicted logP values before finally tuning our model using a small, accurate dataset of 244 druglike compounds to create MRlogP, a neural network-based predictor of logP capable of outperforming state of the art freely available logP prediction methods for druglike small molecules. MRlogP achieves an average root mean squared error of 0.988 and 0.715 against druglike molecules from Reaxys and PHYSPROP. We have made the trained neural network predictor and all associated code for descriptor generation freely available. In addition, MRlogP may be used online via a web interface.

Multitask machine learning models for predicting lipophilicity (logP) in the SAMPL7 challenge

Accurate prediction of lipophilicity—logP—based on molecular structures is a well-established field. Predictions of logP are often used to drive forward drug discovery projects. Driven by the SAMPL7 challenge, in this manuscript we describe the steps that were taken to construct a novel machine learning model that can predict and generalize well. This model is based on the recently described Directed-Message Passing Neural Networks (D-MPNNs). Further enhancements included: both the inclusion of additional datasets from ChEMBL (RMSE improvement of 0.03), and the addition of helper tasks (RMSE improvement of 0.04). To the best of our knowledge, the concept of adding predictions from other models (Simulations Plus logP and logD@pH7.4, respectively) as helper tasks is novel and could be applied in a broader context. The final model that we constructed and used to participate in the challenge ranked 2/17 ranked submissions with an RMSE of 0.66, and an MAE of 0.48 (submission: Chemprop). On other datasets the model also works well, especially retrospectively applied to the SAMPL6 challenge where it would have ranked number one out of all submissions (RMSE of 0.35). Despite the fact that our model works well, we conclude with suggestions that are expected to improve the model even further.

Exploring the octanol-water partition coefficient dataset using deep learning techniques and data augmentation

Today more and more data are freely available. Based on these big datasets deep neural networks (DNNs) rapidly gain relevance in computational chemistry. Here, we explore the potential of DNNs to predict chemical properties from chemical structures. We have selected the octanol-water partition coefficient (log P) as an example, which plays an essential role in environmental chemistry and toxicology but also in chemical analysis. The predictive performance of the developed DNN is good with an rmse of 0.47 log units in the test dataset and an rmse of 0.33 for an external dataset from the SAMPL6 challenge. To this end, we trained the DNN using data augmentation considering all potential tautomeric forms of the chemicals. We further demonstrate how DNN models can help in the curation of the log P dataset by identifying potential errors, and address limitations of the dataset itself.

Recent applications of retention modelling in liquid chromatography

Recent applications of retention modelling in liquid chromatography (2015–2020) are comprehensively reviewed. The fundamentals of the field, which date back much longer, are summarized. Retention modeling is used in retention‐mechanism studies, for determining physical parameters, such as lipophilicity, and for various more‐practical purposes, including method development and optimization, method transfer, and stationary‐phase characterization and comparison. The review focusses on the effects of mobile‐phase composition on retention, but other variables and novel models to describe their effects are also considered. The five most‐common models are addressed in detail, i.e. the log‐linear (linear‐solvent‐strength) model, the quadratic model, the log–log (adsorption) model, the mixed‐mode model, and the Neue–Kuss model. Isocratic and gradient‐elution methods are considered for determining model parameters and the evaluation and validation of fitted models is discussed. Strategies in which retention models are applied for developing and optimizing one‐ and two‐dimensional liquid chromatographic separations are discussed. The review culminates in some overall conclusions and several concrete recommendations.

Biological Activities and ADMET-Related Properties of Novel Set of Cinnamanilides

A series of nineteen novel ring-substituted N-arylcinnamanilides was synthesized and characterized. All investigated compounds were tested against Staphylococcus aureus as the reference strain, two clinical isolates of methicillin-resistant S. aureus (MRSA), and Mycobacterium tuberculosis. (2E)-N-[3-Fluoro-4-(trifluoromethyl)phenyl]-3-phenylprop-2-enamide showed even better activity (minimum inhibitory concentration (MIC) 25.9 and 12.9 µM) against MRSA isolates than the commonly used ampicillin (MIC 45.8 µM). The screening of the cell viability was performed using THP1-Blue™ NF-κB cells and, except for (2E)-N-(4-bromo-3-chlorophenyl)-3-phenylprop-2-enamide (IC₅₀ 6.5 µM), none of the discussed compounds showed any significant cytotoxic effect up to 20 μM. Moreover, all compounds were tested for their anti-inflammatory potential; several compounds attenuated the lipopolysaccharide-induced NF-κB activation and were more potent than the parental cinnamic acid. The lipophilicity values were specified experimentally as well. In addition, in silico approximation of the lipophilicity values was performed employing a set of free/commercial clogP estimators, corrected afterwards by the corresponding pK_a calculated at physiological pH and subsequently cross-compared with the experimental parameters. The similarity-driven property space evaluation of structural analogs was carried out using the principal component analysis, Tanimoto metrics, and Kohonen mapping.

A comparison of molecular representations for lipophilicity quantitative structure-property relationships with results from the SAMPL6 logP Prediction Challenge

Effective representation of a molecule is required to develop useful quantitative structure-property relationships (QSPR) for accurate prediction of chemical properties. The octanol-water partition coefficient logP, a measure of lipophilicity, is an important property for pharmacological and toxicological endpoints used in the pharmaceutical and regulatory spheres. We compare physicochemical descriptors, structural keys, and circular fingerprints in their ability to effectively represent a chemical space and characterise molecular features to correlate with lipophilicity. Exploratory landscape continuity analyses revealed that whole-molecule physicochemical descriptors could map together compounds that were similar in both molecular features and logP, indicating higher potential for use in logP QSPRs compared to the substructural approach of structural keys and circular fingerprints. Indeed, logP QSPR models parameterised by physicochemical descriptors consistently performed with the lowest error. Our best performing model was a stochastic gradient descent-optimised multilinear regression with 1438 descriptors, returning an internal benchmark RMSE of 1.03 log units. This corroborates the well-established notion that lipophilicity is an additive, whole-molecule property. We externally tested the model by participating in the 2019 SAMPL6 logP Prediction Challenge and blindly predicting for 11 protein kinase inhibitor fragment-like molecules. Our model returned an RMSE of 0.49 log units, placing eighth overall and third in the empirical methods category (submission ID 'hdpuj'). Permutation feature importance analyses revealed that physicochemical descriptors could characterise predictive molecular features highly relevant to the kinase inhibitor fragment-like molecules.

Druggability profile of stilbene-derived PPAR agonists: determination of physicochemical properties and PAMPA study

PPAR agonists represent a new therapeutic opportunity for the prevention and treatment of neurodegenerative disorders, but their pharmacological success depends on favourable pharmacokinetic properties and capability to cross the BBB. In this study, we assayed some PPAR agonists previously synthesized by us for their physicochemical properties, with particular references to lipophilicity, solubility and permeability profiles, using the PAMPA. Although tested compounds showed high lipophilicity and low aqueous solubility, the results revealed a good overall druggability profile, encouraging further studies in the field of neurodegenerative diseases.

Bioactivity of Methoxylated and Methylated 1-Hydroxynaphthalene-2-Carboxanilides: Comparative Molecular Surface Analysis

A series of twenty-six methoxylated and methylated N-aryl-1-hydroxynaphthalene- 2-carboxanilides was prepared and characterized as potential anti-invasive agents. The molecular structure of N-(2,5-dimethylphenyl)-1-hydroxynaphthalene-2-carboxamide as a model compound was determined by single-crystal X-ray diffraction. All the analysed compounds were tested against the reference strain Staphylococcus aureus and three clinical isolates of methicillin-resistant S.aureus as well as against Mycobacterium tuberculosis and M. kansasii. In addition, the inhibitory profile of photosynthetic electron transport in spinach (Spinacia oleracea L.) chloroplasts was specified. In vitro cytotoxicity of the most effective compounds was tested on the human monocytic leukaemia THP-1 cell line. The activities of N-(3,5-dimethylphenyl)-, N-(3-fluoro-5-methoxy-phenyl)- and N-(3,5-dimethoxyphenyl)-1-hydroxynaphthalene-2-carbox- amide were comparable with or even better than the commonly used standards ampicillin and isoniazid. All promising compounds did not show any cytotoxic effect at the concentration >30 µM. Moreover, an in silico evaluation of clogP features was performed for the entire set of the carboxamides using a range of software lipophilicity predictors, and cross-comparison with the experimentally determined lipophilicity (log k), in consensus lipophilicity estimation, was conducted as well. Principal component analysis was employed to illustrate noticeable variations with respect to the molecular lipophilicity (theoretical/experimental) and rule-of-five violations. Additionally, ligand-oriented studies for the assessment of the three-dimensional quantitative structure–activity relationship profile were carried out with the comparative molecular surface analysis to determine electron and/or steric factors that potentially contribute to the biological activities of the investigated compounds.

Ligand potency - an essential estimator for drug design: between intuition, misinterpretation and serendipity

This is a review of developments in the field of potency, which is an essential estimator for drug design. We discuss the basic concepts of research and discovery, focusing on how misinterpretation in this area, - but also intuition and serendipity, which are common in drug design - helped us to pave a bumpy road towards better drugs. How far we still are from this goal can be seen in the Eroom law, which states that the efficiency of pharma research and development is decreasing. At the same time, pharma bestsellers are getting older.

JPlogP: an improved logP predictor trained using predicted data

The partition coefficient between octanol and water (logP) has been an important descriptor in QSAR predictions for many years and therefore the prediction of logP has been examined countless times. One of the best performing models is to predict the logP using multiple methods and average the result. We have used those averaged predictions to develop a training-set which was able to distil the information present across the disparate logP methods into one single model. Our model was built using extendable atom-types, where each atom is distilled down into a 6 digit number, and each individual atom is assumed to have a small additive effect on the overall logP of the molecule. Beyond the simple coefficient model a consensus model is evaluated, which uses known compounds as a starting point in the calculation and modifies the experimental logP using the same coefficients as in the first model. We then test the performance of our models against two different datasets, one where many different models routinely perform well against, and another designed to more represent pharmaceutical space. The true strength of the model is represented in the pharmaceutical benchmark set, where both models perform better than any previously developed models.

KuQuinones Equilibria Assessment for Biomedical Applications

A small library of pentacyclic quinoid compounds, called KuQuinones (KuQs), has been prepared through a one-pot reaction. KuQuinones complex structure is made up by two naphthoquinone units connected by a five-membered ring. Due to KuQs structural features, keto-enol tautomerization in solution likely occurs, leading to the generation of four different species, i.e., the enol, the enolate, the external enol and the diquinoid species. The interchange among KuQ tautomers leads to substantial spectral variations of the dye depending on the experimental conditions used. The comprehension of tautomeric equilibria of this new class of quinoid compounds is strongly required in order to explain their behavior in solution and in biological environment. UV-vis, ¹H NMR spectroscopies, and DFT calculations resulted appropriate tools to understand the nature of the prevalent KuQuinone species in solution. Moreover, due to the structural similarity of KuQuinones with camptothecin (CPT), a largely used anticancer agent, KuQs have been tested against Cisplatin-resistant SKOV3 and SW480 cancer cell lines. Results highlighted that KuQs are highly active toward the analyzed cell lines and almost nontoxic for healthy cell, indicating a high specific activity.

The cellular membrane as a mediator for small molecule interaction with membrane proteins

The cellular membrane constitutes the first element that encounters a wide variety of molecular species to which a cell might be exposed. Hosting a large number of structurally and functionally diverse proteins associated with this key metabolic compartment, the membrane not only directly controls the traffic of various molecules in and out of the cell, it also participates in such diverse and important processes as signal transduction and chemical processing of incoming molecular species. In this article, we present a number of cases where details of interaction of small molecular species such as drugs with the membrane, which are often experimentally inaccessible, have been studied using advanced molecular simulation techniques. We have selected systems in which partitioning of the small molecule with the membrane constitutes a key step for its final biological function, often binding to and interacting with a protein associated with the membrane. These examples demonstrate that membrane partitioning is not only important for the overall distribution of drugs and other small molecules into different compartments of the body, it may also play a key role in determining the efficiency and the mode of interaction of the drug with its target protein. This article is part of a Special Issue entitled: Biosimulations edited by Ilpo Vattulainen and Tomasz Róg.

Towards understanding the mechanism of action of antibacterial N-alkyl-3-hydroxypyridinium salts: Biological activities, molecular modeling and QSAR studies

In this study, we have carried out a combined experimental and computational investigation to elucidate several bred-in-the-bone ideas standing out in rational design of novel cationic surfactants as antibacterial agents. Five 3-hydroxypyridinium salts differing in the length of N-alkyl side chain have been synthesized, analyzed by high performance liquid chromatography, tested for in vitro activity against a panel of pathogenic bacterial and fungal strains, computationally modeled in water by a SCRF B3LYP/6-311++G(d,p) method, and evaluated by a systematic QSAR analysis. Given the results of this work, the hypothesis suggesting that higher positive charge of the quaternary nitrogen should increase antimicrobial efficacy can be rejected since 3-hydroxyl group does increase the positive charge on the nitrogen but, simultaneously, it significantly derogates the antimicrobial activity by lowering the lipophilicity and by escalating the desolvation energy of the compounds in comparison with non-hydroxylated analogues. Herein, the majority of the prepared 3-hydroxylated substances showed notably lower potency than the parent pyridinium structures, although compound 8 with C12 alkyl chain proved a distinctly better antimicrobial activity in submicromolar range. Focusing on this anomaly, we have made an effort to reveal the reason of the observed activity through a molecular dynamics simulation of the interaction between the bacterial membrane and compound 8 in GROMACS software.

Discovery of Novel, Potent, and Specific Cell-Death Inducers in the Jurkat Acute Lymphoblastic Leukemia Cell Line

The limited clinical efficacy of many cancer therapeutics has initiated intense research efforts toward the discovery of novel chemical entities in this field. In this study, 31 hit candidates were selected from nearly 800,000 database compounds in a ligand-based virtual screening campaign. In turn, three of these hits were found to have (sub)micromolar potencies in proliferation assays with the Jurkat acute lymphatic leukemic cell line. In this assay, the three hits were found to exhibit higher potency than clinically tested cell-death inducers (GDC-0152, AT-406, and birinapant). Importantly, antiproliferative activity toward non-cancer peripheral blood mononuclear cells (PBMCs) was found to be marginal. Further biological characterization demonstrated the cell-death-inducing properties of these compounds. Biological testing of hit congeners excluded a nonspecific, toxic effect of the novel structures. Altogether, these findings may have profound relevance for the development of clinical candidates in tumor therapy.

Enantioselective synthesis and physicochemical properties of libraries of 3-amino- and 3-amidofluoropiperidines

The enantioselective syntheses of 3-amino-5-fluoropiperidines and 3-amino-5,5-difluoropiperidines were developed using the ring enlargement of prolinols to access libraries of 3-amino- and 3-amidofluoropiperidines. The study of the physicochemical properties revealed that fluorine atom(s) decrease(s) the pKa and modulate(s) the lipophilicity of 3-aminopiperidines. The relative stereochemistry of the fluorine atoms with the amino groups at C3 on the piperidine core has a small effect on the pKa due to conformationnal modifications induced by fluorine atom(s). In the protonated forms, the C-F bond is in an axial position due to a dipole-dipole interaction between the N-H(+) and C-F bonds. Predictions of the physicochemical properties using common software appeared to be limited to determine correct values of pKa and/or differences of pKa between cis- and trans-3-amino-5-fluoropiperidines.