QSPR with extended topochemical atom (ETA) indices, 3: Modeling of critical micelle concentration of cationic surfactants

QSPR for the prediction of critical micelle concentration of different classes of surfactants using machine learning algorithms

The determination of the critical micelle concentration (CMC) is a crucial factor when evaluating surfactants, making it an essential tool in studying the properties of surfactants in various industrial fields. In this present research, we assembled a comprehensive set of 593 different classes of surfactants including, anionic, cationic, nonionic, zwitterionic, and Gemini surfactants to establish a link between their molecular structure and the negative logarithmic value of critical micelle concentration (pCMC) utilizing quantitative structure-property relationship (QSPR) methodologies. Statistical analysis revealed that a set of 14 significant Mordred descriptors (SlogP, GATS6d, nAcid, GATS8dv, GATS4dv, PEOE_VSA11, GATS8d, ATS0p, GATS1d, MATS5p, GATS3d, NdssC, GATS6dv and EState_VSA4), along with temperature, served as appropriate inputs. Different machine learning methods, such as multiple linear regression (MLR), random forest regression (RFR), artificial neural network (ANN), and support vector regression (SVM), were employed in this study to build QSPR models. According to the statistical coefficients of QSPR models, SVR with Dragonfly hyperparameter optimization (SVR-DA) was the most accurate in predicting pCMC values, achieving (R2 = 0.9740, Q2 = 0.9739, r‾m2 = 0.9627, and Δrm2 = 0.0244) for the entire dataset.

Quantitative structure-activity relationship for the oxidation of organic contaminants by peracetic acid using GA-MLR method

Peracetic acid (PAA) is considered as an effective and powerful oxidant for eliminating organic contaminants in wastewater treatment. The second-order rate constant (k_app) for the reaction of PAA with organic contaminants is practically important for evaluating their removal efficiency in wastewater treatment, but only limited numbers of k_app values are available. In this study, 70 organic compounds with various structures were selected, and the k_app of PAA with each organic compound was used to develop two quantitative structure-activity relationship (QSAR) models based on three kinds of descriptors including constitutional, quantum chemical, and the PaDEL descriptors. The genetic algorithm (GA) was applied to select the molecular descriptors, then the models developed by multiple linear regression (MLR). The most important descriptors that explain the reactivity of organic compounds with PAA are the E_HOMO for the model with the constitutional and quantum chemical descriptors. The maxHdsCH and minHdCH₂ are two most important descriptors for the model with only PaDEL descriptors. The developed models can be used to predict k_app for a wide range of organic contaminants. The accuracy of the developed models was proved by the internal, external validation and the Y-scrambling technique. The developed QSAR models using the GA-MLR method can be used as a screening tool for predicting the elimination of organic contaminants by PAA and increasing the understanding of chemical pollutant fate.

On the hydrophobic chains effect on critical micelle concentration of cationic gemini surfactants using molecular connectivity indices

The influence of the structure of hydrophobic tail chains on the critical micelle concentration of cationic gemini surfactants, using only the molecular connectivity indices, has been investigated in this work. The best model obtained shows that the relationship between the logarithm of critical micelle concentration and the alkyl chains length is parabolic. The formula has been derived for compounds with the same head groups and the same, medium length, spacer but with various hydrocarbon tail chains. Good-quality QSPR model obtained can be used to predict the critical micelle concentration value of structurally similar gemini surfactants as well as to design the structure of the hydrophobic tail chains to obtain new molecules more active in micelle formation.

Graphic abstract

Quantitative Structure-Property Relationship for pH-Triggered Drug Release Performance of Acid-Responsive Four/Six-Arms Star Polymeric Micelles

PURPOSE

The pH-responsive copolymer micelles are widely used as carriers in drug delivery system, but there are few micro-level mechanistically explorations on the pH-triggered drug release. Here we elucidate the relationship between drug release behavior of four/six-arms star copolymer micelles and the copolymer structures.

METHOD

The net cumulative drug release percentage (E_n) was taken as the dependent variables, block unit autocorrelation descriptors as independent variables. The quantitative structure-property relationship models of drug release from block copolymers were developed at pH 7.4 and 5.0 of two periods (stage I: 0~12 h, stage II: 12~96 h).

RESULTS

The models built are of good fitting ability, internal predictive ability, stability and statistically significance. Drug diffusion is mainly influenced by the intra-block force, and micellar erosion by inter-block force. At pH 5.0, lowest unoccupied molecular orbital energy of copolymer unit is the main factor influencing the E_n. Stage I of drug release is affected by hydrophobic property and stage II by regional polar of copolymer molecules.

CONCLUSION

The models present good performance, factors affecting drug release behavior at different pH conditions can offer guidance for the design of copolymer structures to control the drug release behavior of micelles in a targeted and quantitatively way.

Diverse classes of HDAC8 inhibitors: in search of molecular fingerprints that regulate activity

AIM

HDAC8 is one of the crucial enzymes involved in malignancy. Structural explorations of HDAC8 inhibitory activity and selectivity are required.

MATERIALS & METHODS

A mathematical framework was constructed to explore important molecular fragments responsible for HDAC8 inhibition. Bayesian classification models were developed on a large set of structurally diverse HDAC8 inhibitors.

RESULTS

This study helps to understand the structural importance of HDAC8 inhibitors. The hydrophobic aryl cap function is important for HDAC8 inhibition whereas benzamide moiety shows a negative impact on HDAC8 inhibition.

CONCLUSION

This work validates our previously proposed structural features for better HDAC8 inhibition. The comparative learning between the statistical and intelligent methods will surely enrich future drug design aspects of HDAC8 inhibitors.

The “ETA” Indices in QSAR/QSPR/QSTR Research

Descriptors are one of the most essential components of predictive Quantitative Structure-Activity/Property/Toxicity Relationship (QSAR/QSPR/QSTR) modeling analysis, as they encode chemical information of molecules in the form of quantitative numbers, which are used to develop mathematical correlation models. The quality of a predictive model not only depends on good modeling statistics, but also on the extraction of chemical features. A significant amount of research since the beginning of QSAR analysis paradigm has led to the introduction of a large number of predictor variables or descriptors. The Extended Topochemical Atom (ETA) indices, developed by the authors' group, successfully address the aspects of molecular topology, electronic information, and different types of bonded interactions, and have been extensively employed for the modeling of different types of activity/property and toxicity endpoints. This chapter provides explicit information regarding the basis, algorithm, and applicability of the ETA indices for a predictive modeling paradigm.

Molecular connectivity indices for modeling the critical micelle concentration of cationic (chloride) Gemini surfactants

The molecular connectivity indices were used to derive the simple model relating the critical micelle concentration of cationic (chloride) gemini surfactants to their structure. One index was selected as the best to describe the effect of the structure of investigated compounds on critical micelle concentration consistent with the experimental results. This index encodes the information about molecular size, the branches, and also the information about heteroatoms. The selected model can be helpful in designing novel chloride gemini surfactants.

The “ETA” Indices in QSAR/QSPR/QSTR Research

Descriptors are one of the most essential components of predictive Quantitative Structure-Activity/Property/Toxicity Relationship (QSAR/QSPR/QSTR) modeling analysis, as they encode chemical information of molecules in the form of quantitative numbers, which are used to develop mathematical correlation models. The quality of a predictive model not only depends on good modeling statistics, but also on the extraction of chemical features. A significant amount of research since the beginning of QSAR analysis paradigm has led to the introduction of a large number of predictor variables or descriptors. The Extended Topochemical Atom (ETA) indices, developed by the authors' group, successfully address the aspects of molecular topology, electronic information, and different types of bonded interactions, and have been extensively employed for the modeling of different types of activity/property and toxicity endpoints. This chapter provides explicit information regarding the basis, algorithm, and applicability of the ETA indices for a predictive modeling paradigm.