ClogPalk: a method for predicting alkane/water partition coefficient

Energy-entropy multiscale cell correlation method to predict toluene-water log P in the SAMPL9 challenge

The energy-entropy multiscale cell correlation (EE-MCC) method is used to calculate toluene-water log P values of 16 drug molecules in the SAMPL9 physical properties challenge. EE-MCC calculates the free energy, energy and entropy from molecular dynamics (MD) simulations of the water and toluene solutions. Specifically, MCC evaluates entropy by partitioning the system into cells of correlated atoms at multiple length scales and further partitioning the local coordinates into energy wells, yielding vibrational and topographical terms from the energy-well sizes and probabilities. The log P values calculated by EE-MCC using three 200 ns MD simulations have a mean average error of 0.82 and standard error of the mean of 0.97 versus experiment, which is comparable with the best methods entered in SAMPL9. The main contribution to log P is from energy. Less polar drugs have more favourable energies of transfer. The entropy of transfer consists of increased solute vibrational and conformational terms in toluene due to weaker interactions, fewer solute positions in the larger-molecule solvent, reduced water vibrational entropy, negligible change in toluene vibrational entropy, and gains in solvent orientational entropy. The solvent entropy contributions here may be slightly underestimated because software limitations and statistical fluctuations meant that only the first shell could be included while averaged over the whole solution. Nonetheless, such issues will be addressed in future software to offer a general method to calculate entropy directly from MD simulation and to provide molecular understanding or guide system design.

Novel N-(Heterocyclylphenyl)benzensulfonamide Sharing an Unreported Binding Site with T-Cell Factor 4 at the β-Catenin Armadillo Repeats Domain as an Anticancer Agent

Despite intensive efforts, no inhibitors of the Wnt/β-catenin signaling pathway have been approved so far for the clinical treatment of cancer. We synthesized novel N-(heterocyclylphenyl)benzenesulfonamides as β-catenin inhibitors. Compounds 5-10 showed strong inhibition of the luciferase activity. Compounds 5 and 6 inhibited the MDA-MB-231, HCC1806, and HCC1937 TNBC cells. Compound 9 induced in vitro cell death in SW480 and HCT116 cells and in vivo tumorigenicity of a human colorectal cancer line HCT116. In a co-immunoprecipitation study in HCT116 cells transfected with Myc-tagged T-cell factor 4 (Tcf-4), compound 9 abrogated the association between β-catenin and Tcf-4. The crystallographic analysis of the β-catenin Armadillo repeats domain revealed that compound 9 and Tcf-4 share a common binding site within the hotspot binding region close to Lys508. To our knowledge, compound 9 is the first small molecule ligand of this region to be reported. These results highlight the potential of this novel class of β-catenin inhibitors as anticancer agents.

Prediction of toluene/water partition coefficients in the SAMPL9 blind challenge: assessment of machine learning and IEF-PCM/MST continuum solvation models

In recent years the use of partition systems other than the widely used biphasic n-octanol/water has received increased attention to gain insight into the molecular features that dictate the lipophilicity of compounds. Thus, the difference between n-octanol/water and toluene/water partition coefficients has proven to be a valuable descriptor to study the propensity of molecules to form intramolecular hydrogen bonds and exhibit chameleon-like properties that modulate solubility and permeability. In this context, this study reports the experimental toluene/water partition coefficients (log P_tol/w) for a series of 16 drugs that were selected as an external test set in the framework of the Statistical Assessment of the Modeling of Proteins and Ligands (SAMPL) blind challenge. This external set has been used by the computational community to calibrate their methods in the current edition (SAMPL9) of this contest. Furthermore, the study also investigates the performance of two computational strategies for the prediction of log P_tol/w. The first relies on the development of two machine learning (ML) models, which are built up by combining the selection of 11 molecular descriptors in conjunction with either the multiple linear regression (MLR) or the random forest regression (RFR) model to target a dataset of 252 experimental log P_tol/w values. The second consists of the parametrization of the IEF-PCM/MST continuum solvation model from B3LYP/6-31G(d) calculations to predict the solvation free energies of 163 compounds in toluene and benzene. The performance of the ML and IEF-PCM/MST models has been calibrated against external test sets, including the compounds that define the SAMPL9 log P_tol/w challenge. The results are used to discuss the merits and weaknesses of the two computational approaches.

Forces Driving a Magic Bullet to Its Target: Revisiting the Role of Thermodynamics in Drug Design, Development, and Optimization

Drug discovery strategies have advanced significantly towards prioritizing target selectivity to achieve the longstanding goal of identifying “magic bullets” amongst thousands of chemical molecules screened for therapeutic efficacy. A myriad of emerging and existing health threats, including the SARS-CoV-2 pandemic, alarming increase in bacterial resistance, and potentially fatal chronic ailments, such as cancer, cardiovascular disease, and neurodegeneration, have incentivized the discovery of novel therapeutics in treatment regimens. The design, development, and optimization of lead compounds represent an arduous and time-consuming process that necessitates the assessment of specific criteria and metrics derived via multidisciplinary approaches incorporating functional, structural, and energetic properties. The present review focuses on specific methodologies and technologies aimed at advancing drug development with particular emphasis on the role of thermodynamics in elucidating the underlying forces governing ligand–target interaction selectivity and specificity. In the pursuit of novel therapeutics, isothermal titration calorimetry (ITC) has been utilized extensively over the past two decades to bolster drug discovery efforts, yielding information-rich thermodynamic binding signatures. A wealth of studies recognizes the need for mining thermodynamic databases to critically examine and evaluate prospective drug candidates on the basis of available metrics. The ultimate power and utility of thermodynamics within drug discovery strategies reside in the characterization and comparison of intrinsic binding signatures that facilitate the elucidation of structural–energetic correlations which assist in lead compound identification and optimization to improve overall therapeutic efficacy.

A novel bee-friendly peptidomimetic insecticide: Synthesis, aphicidal activity and 3D-QSAR study of insect kinin analogs at Phe2 modification

BACKGROUND

As one of the most abundant and destructive pests in agriculture, aphids cause significant damage to crops due to their sap-taking and as virus vectors. Chemical insecticides are the most effective method to control aphids, but they bring insecticide resistance problems and harm nontarget organisms, especially bees, therefore the search for novel eco-friendly aphid control agents with low bee toxicity is urgent. Insect kinins are a class of small neuropeptides that control important functions in insects. In our previous study, we found insect kinin analog IV-3 has good aphicidal activity and the location of the aromatic ring on the side chain of Phe² is the key to the formation of the β-turn resulting in the biological activity of insect kinin analogs. However, there are few studies on insect kinin Phe² substitution and modification, and its structure-activity relationship is still unclear.

RESULTS

In this project, 44 insect kinin analogs with the Phe² modification, replacing it with different natural or unnatural amino acids, were designed and synthesized based on the lead IV-3 to explore the role of the Phe² residues. Bioassays with soybean aphids of Aphis glycines indicated that nine analogs have better aphicidal activity than the lead IV-3. In particular, compound L₂₅ exhibits excellent aphicidal activity (LC₅₀  = 0.0047 mmol L^-1 ) and has low toxicity to bees. Furthermore, a reliable three-dimensional quantitative structure-activity relationship (3D-QSAR) was established to produce a helpful clue that introducing hydrophobic groups away from the backbone chain is beneficial to improve aphicidal activity.

CONCLUSION

The residue Phe² of insect kinin analogs is the key position and has a significant impact on the activity. L₂₅ has a high toxicity for aphids, while a low toxicity to bees, and therefore can be considered as a lead compound to develop new biosafe aphid control agents. Finally, we provide a useful 3D-QSAR model as theoretical guidance for further structural optimization. © 2022 Society of Chemical Industry.

Thermoresponsive Carbohydrate-b-Polypeptoid Polymer Vesicles with Selective Solute Permeability and Permeable Factors for Solutes

Solute-permeable polymer vesicles are structural compartments for nanoreactors/nanofactories in the context of drug delivery and artificial cells. We previously proposed design guidelines for polymers that form solute-permeable vesicles, yet we did not provide enough experimental verification. In addition, the fact that there is no clear factor for identifying permeable solutes necessitates extensive trial and error. Herein, we report solute-permeable polymer vesicles based on an amphiphilic copolymer, thermoresponsive oligosaccharide-block-poly(N-n-propylglycine). The introduction of a thermoresponsive polymer as a hydrophobic segment into amphiphilic polymers is a viable approach to construct solute-permeable polymer vesicles. We also demonstrate that the polymer vesicles are preferentially permeable to cationic and neutral fluorophores and are hardly permeable to anionic fluorophores due to the electrostatic repulsion between the bilayer and anionic fluorophores. In addition, the permeability of neutral fluorophores increases with the increasing log P value of the fluorophores. Thus, the electrical charge and log P value are important factors for membrane permeability. These findings will help researchers develop advanced nanoreactors based on permeable vesicles for a broad range of fundamental and biomedical applications.

A general linear free energy relationship for predicting partition coefficients of neutral organic compounds

Predicting the effects of organic compounds on environments and biological systems is an important objective for environmental chemistry and human health. The logarithm (to base 10) of the n-octanoll-water partition coefficient has been widely used to predict the mentioned properties. However, the suitability of this parameter for the purpose has been questioned, since the environments relating to the properties may be quite different from that of bulk n-octanol. In this study, we used a theoretical derivation approach to develop a model for predicting the partition coefficients of solutes between water and an organic solvent that may be similar to n-octanol or quite different from it. Our model relies on solute descriptors that can be calculated based on solute structures. It was used to predict the n-octanoll-water, hexadecanel-water and chloroforml-water partition coefficients of solutes. The calculated values of the examined parameters agreed well with their experimental counterparts. The model can find application in the accurate prediction of the effects of organic compounds on environments and the physicochemical properties of organic compounds by a full in-silico approach and can provide useful guidance for improving some properties of organic compounds.

Weighted Averaging Scheme and Local Atomic Descriptor for pKa Prediction Based on Density Functional Theory

As a continuation of our work on developing a density functional theory-based pK_a predictor, we present conceptual improvements to our previously published shell model, which is a hierarchical organization of pK_a training sets and which, in principle, covers all chemical space. The improvements concern the way the studied chemical compound is associated with the data points from the training sets. By introducing a new descriptor of the local atomic environment which foregoes dependence on chemical bonding and connectivity, we are able to automatically locate molecules from the training set that are most relevant to the proton dissociation equilibrium under study. This new scheme leads to the prediction of a single pK_a value weighted across multiple training sets and thus patches a defect disclosed in the formulation of our previous model. Using the new parametrization approach, the pK_a prediction gets rid of outliers reported in previous applications of our approach, eliminates ambiguity in interpreting the results, and improves the overall accuracy. Our new treatment accounts for multiple conformations both on the level of energetics and parametrization. Illustrative results are shown for several types of chemical structures containing guanidine, amidine, amine, and phenol functional groups, and which are representative of practically important large and flexible drug-like molecules. Our method's performance is compared to the performance of other previously published pK_a prediction methods. Further possible improvements to the organization of the training sets and the potential application of our new local atomic descriptor to other kinds of parametrizations are discussed.

The History and Development of Quantitative Structure-Activity Relationships (QSARs)

Following the publication of the history and development of QSAR, it became apparent that a number of matters had not been covered. This addendum is an attempt to rectify that. A very early approach (ca. 60 B.C.) by Lucretius shows that he understood how molecular size and complexity affect liquid viscosity. Comments by Kant (1724-1804) emphasized the necessity of mathematics in science. A claim that the work of von Bibra and Harless in 1847 pre-dated that of Overton and H.H. Meyer is shown not to be correct. K.H. Meyer and Gottlieb-Billroth published in 1920 what is probably the first QSAR equation. Brown, who with his co-author Fraser is credited with the first definitive recognition in 1868-9 that biological activity is a function of molecular structure, is often cited as Crum Brown; in fact, Crum was his second given name. The QSAR work of the Soviet chemist N.V. Lazarev in the 1940s was far ahead of his time, showing numerous correlations of biological activities and physicochemical properties with molecular descriptors. The subject of inverse QSAR is discussed.

The influence of hydrogen bonding on partition coefficients

This Perspective explores how consideration of hydrogen bonding can be used to both predict and better understand partition coefficients. It is shown how polarity of both compounds and substructures can be estimated from measured alkane/water partition coefficients. When polarity is defined in this manner, hydrogen bond donors are typically less polar than hydrogen bond acceptors. Analysis of alkane/water partition coefficients in conjunction with molecular electrostatic potential calculations suggests that aromatic chloro substituents may be less lipophilic than is generally believed and that some of the effect of chloro-substitution stems from making the aromatic π-cloud less available to hydrogen bond donors. Relationships between polarity and calculated hydrogen bond basicity are derived for aromatic nitrogen and carbonyl oxygen. Aligned hydrogen bond acceptors appear to present special challenges for prediction of alkane/water partition coefficients and this may reflect 'frustration' of solvation resulting from overlapping hydration spheres. It is also shown how calculated hydrogen bond basicity can be used to model the effect of aromatic aza-substitution on octanol/water partition coefficients.

Extended solvent-contact model approach to blind SAMPL5 prediction challenge for the distribution coefficients of drug-like molecules

The performance of the extended solvent-contact model has been addressed in the SAMPL5 blind prediction challenge for distribution coefficient (LogD) of drug-like molecules with respect to the cyclohexane/water partitioning system. All the atomic parameters defined for 41 atom types in the solvation free energy function were optimized by operating a standard genetic algorithm with respect to water and cyclohexane solvents. In the parameterizations for cyclohexane, the experimental solvation free energy (ΔG _sol ) data of 15 molecules for 1-octanol were combined with those of 77 molecules for cyclohexane to construct a training set because ΔG _sol values of the former were unavailable for cyclohexane in publicly accessible databases. Using this hybrid training set, we established the LogD prediction model with the correlation coefficient (R), average error (AE), and root mean square error (RMSE) of 0.55, 1.53, and 3.03, respectively, for the comparison of experimental and computational results for 53 SAMPL5 molecules. The modest accuracy in LogD prediction could be attributed to the incomplete optimization of atomic solvation parameters for cyclohexane. With respect to 31 SAMPL5 molecules containing the atom types for which experimental reference data for ΔG _sol were available for both water and cyclohexane, the accuracy in LogD prediction increased remarkably with the R, AE, and RMSE values of 0.82, 0.89, and 1.60, respectively. This significant enhancement in performance stemmed from the better optimization of atomic solvation parameters by limiting the element of training set to the molecules with experimental ΔG _sol data for cyclohexane. Due to the simplicity in model building and to low computational cost for parameterizations, the extended solvent-contact model is anticipated to serve as a valuable computational tool for LogD prediction upon the enrichment of experimental ΔG _sol data for organic solvents.

Regulation of protein-ligand binding affinity by hydrogen bond pairing

Hydrogen (H)-bonds potentiate diverse cellular functions by facilitating molecular interactions. The mechanism and the extent to which H-bonds regulate molecular interactions are a largely unresolved problem in biology because the H-bonding process continuously competes with bulk water. This interference may significantly alter our understanding of molecular function, for example, in the elucidation of the origin of enzymatic catalytic power. We advance this concept by showing that H-bonds regulate molecular interactions via a hitherto unappreciated donor-acceptor pairing mechanism that minimizes competition with water. On the basis of theoretical and experimental correlations between H-bond pairings and their effects on ligand binding affinity, we demonstrate that H-bonds enhance receptor-ligand interactions when both the donor and acceptor have either significantly stronger or significantly weaker H-bonding capabilities than the hydrogen and oxygen atoms in water. By contrast, mixed strong-weak H-bond pairings decrease ligand binding affinity due to interference with bulk water, offering mechanistic insight into why indiscriminate strengthening of receptor-ligand H-bonds correlates poorly with experimental binding affinity. Further support for the H-bond pairing principle is provided by the discovery and optimization of lead compounds targeting dietary melamine and Clostridium difficile toxins, which are not realized by traditional drug design methods. Synergistic H-bond pairings have therefore evolved in the natural design of high-affinity binding and provide a new conceptual framework to evaluate the H-bonding process in biological systems. Our findings may also guide wider applications of competing H-bond pairings in lead compound design and in determining the origin of enzymatic catalytic power.

Where are signal molecules likely to be located in anaerobic granular sludge?

Quorum sensing is a concentration-sensing mechanism that plays a vital role in sludge granulation. In this study, the regularities of distribution of different signal molecules, including intra- and interspecific signal molecules (diffusible signal factor, DSF), interspecific signal molecules (autoinducter-2, AI-2) and intraspecific signal molecules (acyl-homoserine lactones, AHLs), from three types of anaerobic granular sludge were investigated. The results showed that 70-90% of DSF was distributed in sludge, while AI-2 in the Water phase accounted for over 80% of the total content. Interestingly, there was a positive correlation between DSF and AI-2, which played opposite roles in granulation. Moreover, more than 55% of short and medium acyl chain AHLs tended to spread in aqueous water, while the long acyl chain AHLs were closer to granular sludge than the short and medium acyl chain AHLs. With the exception of one type of sludge, the percentage of long acyl chain AHLs in the sludge phase was greater than 70%. The different distributions of signal molecules were primarily determined based on their physicochemical properties, including molecular weight and solubility in water or organic solutions. In addition, the basic properties of sludge, such as the granular level or the production of EPS, were closely related to the diversity, distribution and concentration of signal molecules. As a medium in granulation, extracellular polymeric substances production was regulated by different signal molecules from different parts of anaerobic granular sludge. This study provides a foundation for investigation of quorum sensing in the system of anaerobic granular sludge.