A Generally Applicable Computer Algorithm Based on the Group Additivity Method for the Calculation of Seven Molecular Descriptors: Heat of Combustion, LogPO/W, LogS, Refractivity, Polarizability, Toxicity and LogBB of Organic Compounds; Scope and Limits of Applicability

Comment on Naef, R.; Acree, W.E., Jr. Calculation of the Three Partition Coefficients logPow, logKoa and logKaw of Organic Molecules at Standard Conditions at Once by Means of a Generally Applicable Group Additivity Method. Preprints, 2023120275

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Comment on Naef, R.; Acree, W.E., Jr. Revision and Extension of a Generally Applicable Group-Additivity Method for the Calculation of the Standard Heat of Combustion and Formation of Organic Molecules. Molecules 2021, 26, 6101

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Computational modeling and synthesis of pyridine variants of benzoyl-phenoxy-acetamide with high glioblastoma cytotoxicity and brain tumor penetration

Glioblastomas are highly aggressive brain tumors for which therapeutic options are very limited. In a quest for new anti-glioblastoma drugs, we focused on specific structural modifications to the benzoyl-phenoxy-acetamide (BPA) structure present in a common lipid-lowering drug, fenofibrate, and in our first prototype glioblastoma drug, PP1. Here, we propose extensive computational analyses to improve the selection of the most effective glioblastoma drug candidates. Initially, over 100 structural BPA variations were analyzed and their physicochemical properties, such as water solubility (- logS), calculated partition coefficient (ClogP), probability for BBB crossing (BBB_SCORE), probability for CNS penetration (CNS-MPO) and calculated cardiotoxicity (hERG), were evaluated. This integrated approach allowed us to select pyridine variants of BPA that show improved BBB penetration, water solubility, and low cardiotoxicity. Herein the top 24 compounds were synthesized and analyzed in cell culture. Six of them demonstrated glioblastoma toxicity with IC50 ranging from 0.59 to 3.24 µM. Importantly, one of the compounds, HR68, accumulated in the brain tumor tissue at 3.7 ± 0.5 µM, which exceeds its glioblastoma IC50 (1.17 µM) by over threefold.

Computational modeling and synthesis of Pyridine variants of Benzoyl-Phenoxy-Acetamide with high glioblastoma cytotoxicity and brain tumor penetration

Glioblastomas are highly aggressive brain tumors for which therapeutic options are very limited. In a quest for new anti-glioblastoma drugs, we focused on specific structural modifications of benzoyl-phenoxy-acetamide (BPA) present in a common lipid-lowering drug, fenofibrate, and in our first prototype glioblastoma drug, PP1. Here, we propose extensive computational analyses to improve selection of the most effective glioblastoma drug candidates. Initially over 100 structural BPA variations were analyzed and their physicochemical properties such as water solubility (-logS), calculated partition coefficient (ClogP), probability for BBB crossing (BBB_SCORE), probability for CNS penetration (CNS-MPO) and calculated cardiotoxicity (hERG), were evaluated. This integrated approach allowed us to select pyridine variants of BPA that show improved BBB penetration, water solubility, and low cardiotoxicity. Herein the top 24 compounds were synthesized and analyzed in cell culture. Six of them demonstrated glioblastoma toxicity with IC50 ranging from 0.59 to 3.24mM. Importantly, one of the compounds, HR68, accumulated in the brain tumor tissue at 3.7+/-0.5mM, which exceeds its glioblastoma IC50 (1.17mM) by over 3-fold.

Anti-glioblastoma effects of phenolic variants of benzoylphenoxyacetamide (BPA) with high potential for blood brain barrier penetration

Glioblastomas are the most aggressive brain tumors for which therapeutic options are limited. Current therapies against glioblastoma include surgical resection, followed by radiotherapy plus concomitant treatment and maintenance with temozolomide (TMZ), however, these standard therapies are often ineffective, and average survival time for glioblastoma patients is between 12 and 18 months. We have previously reported a strong anti-glioblastoma activity of several metabolic compounds, which were synthetized based compounds, which were synthetized based on the chemical structure of a common lipid-lowering drug, fenofibrate, and share a general molecular skeleton of benzoylphenoxyacetamide (BPA). Extensive computational analyses of phenol and naphthol moieties added to the BPA skeleton were performed in this study with the objective of selecting new BPA variants for subsequent compound preparation and anti-glioblastoma testing. Initially, 81 structural variations were considered and their physical properties such as solubility (logS), blood–brain partitioning (logBB), and probability of entering the CNS calculated by the Central Nervous System—Multiparameter Optimization (MPO-CNS) algorithm were evaluated. From this initial list, 18 compounds were further evaluated for anti-glioblastoma activity in vitro. Nine compounds demonstrated desirable glioblastoma cell toxicity in cell culture, and two of them, HR51, and HR59 demonstrated significantly improved capability of crossing the model blood–brain-barrier (BBB) composed of endothelial cells, astrocytes and pericytes.

QSPR Models for the Molar Refraction, Polarizability and Refractive Index of Aliphatic Carboxylic Acids Using the ZEP Topological Index

The molar refraction, polarizability, and refractive index for a series of monocarboxylic, dicarboxylic, and unsaturated monocarboxylic acids, having a symmetric or asymmetric structure, were investigated by the application of quantitative structure property relationship (QSPR) technique. We used a linear regression method and a single molecular descriptor, the ZEP topological index, calculated in a simple manner, with the help of weighted electronic distances, and also calculated on the basis of the chemical structure of the molecules. The high-quality performance and predictive ability of the QSPR models obtained were validated by means of specific validation techniques: y-randomization test, the leave-one-out cross validation procedure, and external validation. The investigated properties are well modeled (with r2 > 0.99) by the ZEP index, using the regression analysis as a statistical tool for developing reliable QSPR models. Our approach provides an alternative technique to the existing additive methods for predicting the molar refraction and polarizability of carboxylic acids, which is essentially based on the summation of atom and/or functional group contributions or bond contributions, and of some correction increments.

Revision and Extension of a Generally Applicable Group-Additivity Method for the Calculation of the Standard Heat of Combustion and Formation of Organic Molecules

The calculation of the heats of combustion ΔH°c and formation ΔH°f of organic molecules at standard conditions is presented using a commonly applicable computer algorithm based on the group-additivity method. This work is a continuation and extension of an earlier publication. The method rests on the complete breakdown of the molecules into their constituting atoms, these being further characterized by their immediate neighbor atoms. The group contributions are calculated by means of a fast Gauss-Seidel fitting calculus using the experimental data of 5030 molecules from literature. The applicability of this method has been tested by a subsequent ten-fold cross-validation procedure, which confirmed the extraordinary accuracy of the prediction of ΔH°c with a correlation coefficient R2 and a cross-validated correlation coefficient Q2 of 1, a standard deviation σ of 18.12 kJ/mol, a cross-validated standard deviation S of 19.16 kJ/mol, and a mean absolute deviation of 0.4%. The heat of formation ΔH°f has been calculated from ΔH°c using the standard enthalpies of combustion for the elements, yielding a correlation coefficient R2 for ΔH°f of 0.9979 and a corresponding standard deviation σ of 18.14 kJ/mol.

Solubility Prediction from Molecular Properties and Analytical Data Using an In-phase Deep Neural Network (Ip-DNN)

Materials informatics is an emerging field that allows us to predict the properties of materials and has been applied in various research and development fields, such as materials science. In particular, solubility factors such as the Hansen and Hildebrand solubility parameters (HSPs and SP, respectively) and Log P are important values for understanding the physical properties of various substances. In this study, we succeeded at establishing a solubility prediction tool using a unique machine learning method called the in-phase deep neural network (ip-DNN), which starts exclusively from the analytical input data (e.g., NMR information, refractive index, and density) to predict solubility by predicting intermediate elements, such as molecular components and molecular descriptors, in the multiple-step method. For improving the level of accuracy of the prediction, intermediate regression models were employed when performing in-phase machine learning. In addition, we developed a website dedicated to the established solubility prediction method, which is freely available at "http://dmar.riken.jp/matsolca/".

Surface Tension of Liquid Organic Acids: An Artificial Neural Network Model

An artificial neural network model is proposed for the surface tension of liquid organic fatty acids covering a wide temperature range. A set of 2051 data collected for 98 acids (including carboxylic, aliphatic, and polyfunctional) was considered for the training, testing, and prediction of the resulting network model. Different architectures were explored, with the final choice giving the best results, in which the input layer has the reduced temperature (temperature divided by the critical point temperature), boiling temperature, and acentric factor as an independent variable, a 41-neuron hidden layer, and an output layer consisting of one neuron. The overall absolute percentage deviation is 1.33%, and the maximum percentage deviation is 14.53%. These results constitute a major improvement over the accuracy obtained using corresponding-states correlations from the literature.

Calculation of the Vapour Pressure of Organic Molecules by Means of a Group-Additivity Method and Their Resultant Gibbs Free Energy and Entropy of Vaporization at 298.15 K

The calculation of the vapour pressure of organic molecules at 298.15 K is presented using a commonly applicable computer algorithm based on the group-additivity method. The basic principle of this method rests on the complete breakdown of the molecules into their constituting atoms, further characterized by their immediate neighbour atoms. The group contributions are calculated by means of a fast Gauss-Seidel fitting algorithm using the experimental data of 2036 molecules from literature. A ten-fold cross-validation procedure has been carried out to test the applicability of this method, which confirmed excellent quality for the prediction of the vapour pressure, expressed in log(pa), with a cross-validated correlation coefficient Q2 of 0.9938 and a standard deviation σ of 0.26. Based on these data, the molecules' standard Gibbs free energy ΔG°vap has been calculated. Furthermore, using their enthalpies of vaporization, predicted by an analogous group-additivity approach published earlier, the standard entropy of vaporization ΔS°vap has been determined and compared with experimental data of 1129 molecules, exhibiting excellent conformance with a correlation coefficient R2 of 0.9598, a standard error σ of 8.14 J/mol/K and a medium absolute deviation of 4.68%.

Artificial intelligence in the early stages of drug discovery

Although the use of computational methods within the pharmaceutical industry is well established, there is an urgent need for new approaches that can improve and optimize the pipeline of drug discovery and development. In spite of the fact that there is no unique solution for this need for innovation, there has recently been a strong interest in the use of Artificial Intelligence for this purpose. As a matter of fact, not only there have been major contributions from the scientific community in this respect, but there has also been a growing partnership between the pharmaceutical industry and Artificial Intelligence companies. Beyond these contributions and efforts there is an underlying question, which we intend to discuss in this review: can the intrinsic difficulties within the drug discovery process be overcome with the implementation of Artificial Intelligence? While this is an open question, in this work we will focus on the advantages that these algorithms provide over the traditional methods in the context of early drug discovery.

Multimodal Role of Amino Acids in Microbial Control and Drug Development

Amino acids are ubiquitous vital biomolecules found in all kinds of living organisms including those in the microbial world. They are utilised as nutrients and control many biological functions in microorganisms such as cell division, cell wall formation, cell growth and metabolism, intermicrobial communication (quorum sensing), and microbial-host interactions. Amino acids in the form of enzymes also play a key role in enabling microbes to resist antimicrobial drugs. Antimicrobial resistance (AMR) and microbial biofilms are posing a great threat to the world's human and animal population and are of prime concern to scientists and medical professionals. Although amino acids play an important role in the development of microbial resistance, they also offer a solution to the very same problem i.e., amino acids have been used to develop antimicrobial peptides as they are highly effective and less prone to microbial resistance. Other important applications of amino acids include their role as anti-biofilm agents, drug excipients, drug solubility enhancers, and drug adjuvants. This review aims to explore the emerging paradigm of amino acids as potential therapeutic moieties.

Calculation of the Isobaric Heat Capacities of the Liquid and Solid Phase of Organic Compounds at 298.15K by Means of the Group-Additivity Method

The calculation of the isobaric heat capacities of the liquid and solid phase of molecules at 298.15 K is presented, applying a universal computer algorithm based on the atom-groups additivity method, using refined atom groups. The atom groups are defined as the molecules’ constituting atoms and their immediate neighbourhood. In addition, the hydroxy group of alcohols are further subdivided to take account of the different intermolecular interactions of primary, secondary, and tertiary alcohols. The evaluation of the groups’ contributions has been carried out by solving a matrix of simultaneous linear equations by means of the iterative Gauss–Seidel balancing calculus using experimental data from literature. Plausibility has been tested immediately after each fitting calculation using a 10-fold cross-validation procedure. For the heat capacity of liquids, the respective goodness of fit of the direct (r²) and the cross-validation calculations (q²) of 0.998 and 0.9975, and the respective standard deviations of 8.24 and 9.19 J/mol/K, together with a mean absolute percentage deviation (MAPD) of 2.66%, based on the experimental data of 1111 compounds, proves the excellent predictive applicability of the present method. The statistical values for the heat capacity of solids are only slightly inferior: for r² and q², the respective values are 0.9915 and 0.9874, the respective standard deviations are 12.21 and 14.23 J/mol/K, and the MAPD is 4.74%, based on 734 solids. The predicted heat capacities for a series of liquid and solid compounds have been directly compared to those received by a complementary method based on the "true" molecular volume and their deviations have been elucidated.

Novel Pyrimidines as Multitarget Protein Tyrosine Kinase Inhibitors for the Treatment of Idiopathic Pulmonary Fibrosis (IPF)

A new class of pyrimidine derivatives were identified as potent protein tyrosine kinase (PTK) inhibitors for the treatment of idiopathic pulmonary fibrosis (IPF). Most of these small-molecule inhibitors displayed strong enzymatic activity against BTK and JAK3 kinases at concentrations lower than 10 nM. The representative compound N-(3-((5-chloro-2-(4-((1-morpholino)acetylamino)phenylamino)-4-pyrimidinyl)amino)phenyl)acrylamide (6 a) also exhibited high inhibitory potency toward both BTK and JAK kinase families, as well as ErbB4, at a concentration of 10 nM, achieving rates of inhibition higher than 57 %. Additionally, in vivo biological evaluations showed that 6 a can remarkably decrease the severity of IPF disease. All these investigations suggested that the multi-PTK inhibitor 6 a may serve as a promising agent for the treatment of IPF.

Exploring anticancer activity of structurally modified benzylphenoxyacetamide (BPA); I: Synthesis strategies and computational analyses of substituted BPA variants with high anti-glioblastoma potential

Structural variations of the benzylphenoxyacetamide (BPA) molecular skeleton were explored as a viable starting point for designing new anti-glioblastoma drug candidates. Hand-to-hand computational evaluation, chemical modifications, and cell viability testing were performed to explore the importance of some of the structural properties in order to generate, retain, and improve desired anti-glioblastoma characteristics. It was demonstrated that several structural features are required to retain the anti-glioblastoma activity, including a carbonyl group of the benzophenone moiety, as well as 4′-chloro and 2,2-dimethy substituents. In addition, the structure of the amide moiety can be modified in such a way that desirable anti-glioblastoma and physical properties can be improved. Via these structural modifications, more than 50 compounds were prepared and tested for anti-glioblastoma activity. Four compounds were identified (HR28, HR32, HR37, and HR46) that in addition to HR40 (PP1) from our previous study, have been determined to have desirable physical and biological properties. These include high glioblastoma cytotoxicity at low μM concentrations, improved water solubility, and the ability to penetrate the blood brain barrier (BBB), which indicate a potential for becoming a new class of anti-glioblastoma drugs.

A New Approach to the Assessment of the Reduction in Visibility Caused by Fires of Electrical Cables

Electricity is the most important form of household energy and one of the most important forms of energy for industry and transport. Electrical distribution in construction and transport is almost exclusively implemented using electrical cables. One of the unresolved problems associated with electrical cables is the release of smoke and the resulting reduction of visibility in case of fire in the area. In this study, a new approach was developed to assess the reduction of visibility in an area affected by an electrical cable fire. This approach is based on the determination of the critical ratio of smoke volume (in the smoke layer and exhausted from the fire compartment) to the length of the burning cable, through which the visibility of reflective and illuminated signs was reduced to a lower limit value (a standard of 10 m). The input data for this approach was the extinction area of the smoke released from one meter of burning cable and the length of the cables in the area. This approach was used to test two power cables (CHKE-V J3x1.5 and CHKE-R J3x1.5) and one signal cable J-H(St)H 1 × 2 × 0.8 with the B2ca, s1, d1, a1 fire reaction class. The smoke extinction area of the examined cables was determined using a cone calorimeter at a heat flux of 50 kW/m2. The obtained data showed that in order to maintain a visibility of 10 m for reflective signs, the critical ratio of smoke volume to length of burning cable was 7.5 m3/m. For illuminated signs, the critical ratio was 2.8 m3/m. The relationship between burning length and visibility allows the calculation of visibility in the fire compartment affected by cable fire only from cables length.

Calculation of the Isobaric Heat Capacities of the Liquid and Solid Phase of Organic Compounds at and around 298.15 K Based on Their "True" Molecular Volume

A universally applicable method for the prediction of the isobaric heat capacities of the liquid and solid phase of molecules at 298.15 K is presented, derived from their "true" volume. The molecules' "true" volume in A3 is calculated on the basis of their geometry-optimized structure and the Van-der-Waals radii of their constituting atoms by means of a fast numerical algorithm. Good linear correlations of the "true" volume of a large number of compounds encompassing all classes and sizes with their experimental liquid and solid heat capacities over a large range have been found, although noticeably distorted by intermolecular hydrogen-bond effects. To account for these effects, the total amount of 1303 compounds with known experimental liquid heat capacities has been subdivided into three subsets consisting of 1102 hydroxy-group-free compounds, 164 monoalcohols/monoacids, and 36 polyalcohols/polyacids. The standard deviations for Cp(liq,298) were 20.7 J/mol/K for the OH-free compunds, 22.91 J/mol/K for the monoalcohols/monoacids and 16.03 J/mol/K for the polyols/polyacids. Analogously, 797 compounds with known solid heat capacities have been separated into a subset of 555 OH-free compounds, 123 monoalcohols/monoacids and 119 polyols/polyacids. The standard deviations for Cp(sol,298) were calculated to 23.14 J/mol/K for the first, 21.62 J/mol/K for the second, and 19.75 J/mol/K for the last subset. A discussion of structural and intermolecular effects influencing the heat capacities as well as of some special classes, in particular hydrocarbons, ionic liquids, siloxanes and metallocenes, has been given. In addition, the present method has successfully been extended to enable the prediction of the temperature dependence of the solid and liquid heat capacities in the range between 250 and 350 K.

Structure-based modification of carbonyl-diphenylpyrimidines (Car-DPPYs) as a novel focal adhesion kinase (FAK) inhibitor against various stubborn cancer cells

A family of carbonyl-substituted diphenylpyrimidine derivatives (Car-DPPYs) with strong activity against focal adhesion kinase (FAK), were described in this manuscript. Among them, compounds 7a (IC₅₀ = 5.17 nM) and 7f (IC₅₀ = 2.58 nM) displayed equal anti-FAK enzymatic activity to the lead compound TAE226 (6.79 nM). In particular, compound 7a also exhibited strong antiproliferative activity against several stubborn cancer cells, including AsPC-1 cells (IC₅₀ = 0.105 μM), BxPC-3 cells (IC₅₀ = 0.090 μM), and MCF-7/ADR cells (IC₅₀ = 0.59 μM). Additionally, compound 7a also showed great antitumor efficacy in vivo via aAsPC-1 cancer Xenograft mouse model. The preliminary mechanism study by Western blot analysis revealed that 7a repressed FAK phosphorylation in AsPC cancer cells. Taken together, the results indicate that compound 7a may serve as a promising preclinical candidate for treatment of stubborn cancers.

Calculation of the Surface Tension of Ordinary Organic and Ionic Liquids by Means of a Generally Applicable Computer Algorithm Based on the Group-Additivity Method

The calculation of the surface tension of ordinary organic and ionic liquids, based on a computer algorithm applying a refined group-additivity method, is presented. The refinement consists of the complete breakdown of the molecules into their constituting atoms, further distinguishing them by their immediate neighbour atoms and bond constitution. The evaluation of the atom-groups' contributions was carried out by means of a fast Gauss-Seidel fitting method, founded upon the experimental data of 1893 compounds from literature. The result has been tested for plausibility using a 10-fold cross-validation (cv) procedure. The direct calculation and the cv test proved the applicability of the present method by the close similarity and excellent goodness of fit R² and Q² of 0.9039 and 0.8823, respectively. The respective standard deviations are ±1.99 and ±2.16 dyn/cm. Some correlation peculiarities have been observed in a series of ordinary and ionic liquids with homologous alkyl chains, as well as with di- and trihydroxy-groups-containing liquids, which have been discussed in detail, exhibiting the limit of the present method.

Application of a General Computer Algorithm Based on the Group-Additivity Method for the Calculation of Two Molecular Descriptors at Both Ends of Dilution: Liquid Viscosity and Activity Coefficient in Water at Infinite Dilution

The application of a commonly used computer algorithm based on the group-additivity method for the calculation of the liquid viscosity coefficient at 293.15 K and the activity coefficient at infinite dilution in water at 298.15 K of organic molecules is presented. The method is based on the complete breakdown of the molecules into their constituting atoms, further subdividing them by their immediate neighborhood. A fast Gauss–Seidel fitting method using experimental data from literature is applied for the calculation of the atom groups’ contributions. Plausibility tests have been carried out on each of the calculations using a ten-fold cross-validation procedure which confirms the excellent predictive quality of the method. The goodness of fit (Q²) and the standard deviation (σ) of the cross-validation calculations for the viscosity coefficient, expressed as log(η), was 0.9728 and 0.11, respectively, for 413 test molecules, and for the activity coefficient log(γ)^∞ the corresponding values were 0.9736 and 0.31, respectively, for 621 test compounds. The present approach has proven its versatility in that it enabled the simultaneous evaluation of the liquid viscosity of normal organic compounds as well as of ionic liquids.

vNN Web Server for ADMET Predictions

In drug development, early assessments of pharmacokinetic and toxic properties are important stepping stones to avoid costly and unnecessary failures. Considerable progress has recently been made in the development of computer-based (in silico) models to estimate such properties. Nonetheless, such models can be further improved in terms of their ability to make predictions more rapidly, easily, and with greater reliability. To address this issue, we have used our vNN method to develop 15 absorption, distribution, metabolism, excretion, and toxicity (ADMET) prediction models. These models quickly assess some of the most important properties of potential drug candidates, including their cytotoxicity, mutagenicity, cardiotoxicity, drug-drug interactions, microsomal stability, and likelihood of causing drug-induced liver injury. Here we summarize the ability of each of these models to predict such properties and discuss their overall performance. All of these ADMET models are publically available on our website (https://vnnadmet.bhsai.org/), which also offers the capability of using the vNN method to customize and build new models.

Calculation of Five Thermodynamic Molecular Descriptors by Means of a General Computer Algorithm Based on the Group-Additivity Method: Standard Enthalpies of Vaporization, Sublimation and Solvation, and Entropy of Fusion of Ordinary Organic Molecules and Total Phase-Change Entropy of Liquid Crystals

The calculation of the standard enthalpies of vaporization, sublimation and solvation of organic molecules is presented using a common computer algorithm on the basis of a group-additivity method. The same algorithm is also shown to enable the calculation of their entropy of fusion as well as the total phase-change entropy of liquid crystals. The present method is based on the complete breakdown of the molecules into their constituting atoms and their immediate neighbourhood; the respective calculations of the contribution of the atomic groups by means of the Gauss-Seidel fitting method is based on experimental data collected from literature. The feasibility of the calculations for each of the mentioned descriptors was verified by means of a 10-fold cross-validation procedure proving the good to high quality of the predicted values for the three mentioned enthalpies and for the entropy of fusion, whereas the predictive quality for the total phase-change entropy of liquid crystals was poor. The goodness of fit (Q²) and the standard deviation (σ) of the cross-validation calculations for the five descriptors was as follows: 0.9641 and 4.56 kJ/mol (N = 3386 test molecules) for the enthalpy of vaporization, 0.8657 and 11.39 kJ/mol (N = 1791) for the enthalpy of sublimation, 0.9546 and 4.34 kJ/mol (N = 373) for the enthalpy of solvation, 0.8727 and 17.93 J/mol/K (N = 2637) for the entropy of fusion and 0.5804 and 32.79 J/mol/K (N = 2643) for the total phase-change entropy of liquid crystals. The large discrepancy between the results of the two closely related entropies is discussed in detail. Molecules for which both the standard enthalpies of vaporization and sublimation were calculable, enabled the estimation of their standard enthalpy of fusion by simple subtraction of the former from the latter enthalpy. For 990 of them the experimental enthalpy-of-fusion values are also known, allowing their comparison with predictions, yielding a correlation coefficient R² of 0.6066.