QSAR Research Articles

Investigating phase changes in cyanide-containing organic compounds with high melting points based on Quantitative Structure-Property Relationship modelling

Cyanide-containing organic compounds exhibit heat-resistant properties due to their high melting points, which can exceed 250 °C. These compounds find widespread applications in various industries. To predict their thermal decomposition temperatures, a straightforward model is introduced in this study. Unlike complex descriptors and computer codes, this model relies on structural parameters specific to cyanide-containing organic compounds. The investigation revealed that certain structural variables significantly influence the onset decomposition temperature. Among these, the number and type of rings play a crucial role. The model was developed and tested using a dataset of 77 experimental thermal decomposition onset temperatures for cyanide-containing organic compounds. For training and test sets comprising 62 and 15 compounds, respectively, the predicted values demonstrated good agreement with experimental data. Specifically, the coefficient of determination (R2) was 0.922 for the training set and 0.897 for the test set. The root mean square deviation (RMSD) and average absolute deviation (AAD) were 14.37 and 17.11 K for the training set, and 11.10 and 13.80 K for the test set. Furthermore, the proposed model outperformed a more complex existing model when applied to 13 cyanide-containing organic compounds. Additionally, this method can be utilized for designing cyanide-containing organic compounds with desired decomposition temperatures.

QSAR, ADMET, molecular docking, and dynamics studies of 1,2,4-triazine-3(2H)-one derivatives as tubulin inhibitors for breast cancer therapy

Breast cancer remains a leading cause of cancer-related deaths among women globally, necessitating the development of more effective therapeutic agents with minimal side effects. This study explores novel 1,2,4-triazine-3(2H)-one derivatives as potential inhibitors of Tubulin, a pivotal protein in cancer cell division, highlighting a targeted approach in cancer therapy. Using an integrated computational approach, we combined quantitative structure–activity relationship (QSAR) modeling, ADMET profiling, molecular docking, and molecular dynamics simulations to evaluate and predict the efficacy and stability of these compounds. Our QSAR models, developed through rigorous statistical analysis, revealed that descriptors such as absolute electronegativity and water solubility significantly influence inhibitory activity, achieving a predictive accuracy (R2) of 0.849. Molecular docking studies identified compounds with high binding affinities, particularly Pred28, which exhibited the best docking score of − 9.6 kcal/mol. Molecular dynamics simulations conducted over 100 ns provided further insights into the stability of these interactions. Pred28 demonstrated notable stability, with the lowest root mean square deviation (RMSD) of 0.29 nm and root mean square fluctuation (RMSF) values indicative of a tightly bound conformation to Tubulin. The novelty of this work lies in its methodological rigor and the integration of multiple advanced computational techniques to pinpoint compounds with promising therapeutic potential. Our findings advance the current understanding of Tubulin inhibitors and open avenues for the synthesis and experimental validation of these compounds, aiming to offer new solutions for breast cancer treatment.

Near-Term Quantum Classification Algorithms Applied to Antimalarial Drug Discovery.

Computational approaches are widely applied in drug discovery to explore properties related to bioactivity, physiochemistry, and toxicology. Over at least the last 20 years, the exploitation of machine learning on molecular data sets has been used to understand the structure-activity relationships that exist between biomolecules and druggable targets. More recently, these methods have also seen application for phenotypic screening data for neglected diseases such as tuberculosis and malaria. Herein, we apply machine learning to build quantum Quantitative Structure Activity Relationship models from antimalarial data sets. There is a continual need for new antimalarials to address drug resistance, and the readily available in vitro data sets could be utilized with newer machine learning approaches as these develop. Furthermore, quantum machine learning is a relatively new method that uses a quantum computer to perform the calculations. First, we present a classical-quantum hybrid computational approach by building a Latent Bernoulli Autoencoder machine learning model for compressing bit-vector descriptors to a size that can be adapted to quantum computers for classification tasks with limited loss of embedded information. Second, we apply our method for feature map compression to quantum classification algorithms, including a completely novel machine learning algorithm with no analogy in classical computers: the Quantum Fourier Transform Classifier. We apply both these approaches to build quantum machine learning models for small-molecule antimalarials with quantum simulation software and then benchmark these quantum models against classical machine learning approaches. While there are many challenges currently facing the development of reliable quantum computers, our results demonstrate that there is potential for the use of this technology in the field of drug discovery.

Novel mandelic acid derivatives containing piperazinyls as potential candidate fungicides against Monilinia fructicola: Design, synthesis and mechanism study

Brown rot of stone fruit, a disease caused by the ascomycete fungus Monilinia fructicola, has caused significant losses to the agricultural industry. In order to explore and discover potential fungicides against M. fructicola, thirty-one novel mandelic acid derivatives containing piperazine moieties were designed and synthesized based on the amide skeleton. Among them, target compound Z31 exhibited obvious in vitro antifungal activity with the EC50 value of 11.8 mg/L, and significant effects for the postharvest pears (79.4 % protective activity and 70.5 % curative activity) at a concentration of 200 mg/L. Antifungal activity for the target compounds was found to be significantly improved by the large steric hindrance of the R1 groups and the electronegative of the piperazines in the molecular structure, according to a three-dimensional quantitative structure–activity relationship (3D-QSAR) analysis. Further mechanism studies have demonstrated that the compound Z31 can disrupt cell membrane integrity, resulting in increased membrane permeability, release of intracellular electrolytes, and affect the normal growth of hyphae. Additional, morphological study also indicated that Z31 may disrupt the integrity of the membrane by inducing generate excess endogenous reactive oxygen species (ROS) and resulting in the peroxidation of cellular lipids, which was further verified by the detection of malondialdehyde (MDA) content. These studies have provided the basis for the creation of novel fungicides to prevent brown rot in stone fruits.

Evaluating the antibacterial and antibiofilm activities of chitosan derivatives containing six-membered heterocyclics against E. coli and S. aureus

Chitosan exhibits good biocompatibility and some antibacterial activity, making it a popular choice in biomedicine, personal care products, and food packaging. Despite its advantages, the limited antibacterial effectiveness of chitosan hinders its widespread use. Introducing a six-membered heterocyclic structure through chemical modification can significantly enhance its antimicrobial properties and broaden its potential applications. In order to explore the effect of six-membered heterocyclic structure on the antibacterial and antibiofilm activities of chitosan. In this study, seven chitosan derivatives containing six-membered heterocyclics were prepared. They were characterized using Fourier transform infrared (FT-IR) spectroscopy, nuclear magnetic resonance (NMR) spectroscopy, and elemental analysis. Cell viability assay showed that they were non-toxic. The antibacterial and antibiofilm activities against Escherichia coli (E. coli) and Staphylococcus aureus (S. aureus) were evaluated. Our research findings demonstrate that increasing the hydrophobicity, alkalinity and charge density of the substitute groups improved the antibacterial and antibiofilm activities of chitosan. This study also offers valuable insights into the quantitative structure-activity relationships of chitosan derivatives in terms of antibacterial and antibiofilm activities.

Elucidating bis-pyrimidines as new and efficient mushroom tyrosinase inhibitors: synthesis, SAR, kinetics and computational studies.

In this study, a series of novel bis-pyrimidine derivatives (1P-8P) were designed, synthesized, characterized, and investigated for their in vitro inhibitory activity against mushroom tyrosinase, an enzyme critical in melanin biosynthesis and implicated in various hyperpigmentation disorders. To the best of our knowledge, the bispyrimidine scaffold has been evaluated for the first time for its tyrosinase inhibitory activity. Their inhibitory activities were assessed, revealing inhibition with IC50 values in the micromolar range. Additionally, this series of compounds were found to inhibit tyrosinase activity in a mixed-type manner, with IC50 values ranging from 12.36 ± 1.24 to 86.67 ± 3.08 μM. To further elucidate the binding interactions, molecular docking simulations were performed, identifying key residues in the active site responsible for binding affinity. Furthermore, molecular dynamics (MD) simulations were conducted to assess the dynamic behavior, stability, and binding affinity of the most potent inhibitor, compound 6P. Quantitative Structure-Activity Relationship (QSAR) models were developed to correlate the structural features of the bis-pyrimidines with their inhibitory activity, providing insights into the structure-activity relationships (SAR) that govern their potency. The experimental and theoretical findings demonstrated excellent agreement. These findings pave the way for the development of novel bis-pyrimidine-based therapeutic agents for treating hyperpigmentation and related conditions.

Random Forests Regression and Rescoring Strategy for Identifying Inhibitors of Ubiquitin Specific Protease-7

Ubiquitin-specific protease-7 (USP7) is a potential target for cancer therapy. In this work, the evaluation and design of USP7 inhibitors are conducted employing a comprehensive computational approach. Initially, a large database of compounds undergoes preliminary screening via similarity and pharmacophore analyses. Subsequently, a secondary selection process is executed based on both quantitative structure–activity relationship modeling and molecular docking. The final selection is refined through rescoring treatments via consensus scores and MM/GBSA binding free energy. A total of 138 experimental USP7 inhibitors were collected for the construction of random forest regression models using four different feature selection methods. 17 top experimentally active inhibitors were used as models in similarity searching, and eight representative USP7-ligand binding models were used in pharmacophore screening, performed over a database of 14 million compounds. The rescoring approach applied in this study offers an alternative and beneficial method for the ranking and selection of desired molecules. Based on the molecular scaffolds of the highly active inhibitors, some new derivatives have been designed and received high predicted scores for inhibition.

Role of Artificial Intelligence in Pharmaceutical Drug Development

: One of the most popular sectors in the tech and healthcare industries right now is artificial intelligence. In the search and development of new drugs, artificial intelligence is essential. Drug design using computer-assisted design (CADD) has supplanted the traditional approach. Artificial intelligence is assisting businesses in the development of new drugs in a faster, more affordable, and more efficient manner, saving money and manpower in the process of creating new drug molecules to treat any disease. Quantitative structure-activity relationship (QSAR) analysis, activity scoring, in silico testing, biomarker development, and mode of action identification are all aided by artificial intelligence. It is revolutionizing these sectors by swiftly identifying potential drug candidates, efficiently conducting clinical trials, and customizing patient care. AI optimizes drug manufacturing processes, augments safety monitoring, and streamlines market analysis. In clinical trials, AI streamlines patient recruitment and ensures more precise trial designs, leading to faster and more efficient research. AI empowers personalized medicine by tailoring treatment plans and drug dosages to individual patient characteristics. AI also optimizes pharmaceutical manufacturing processes, amplifies safety monitoring by analyzing real-time data for adverse events, and supports market analysis and sales strategies. AI in the pharmaceutical industry is a multifaceted tool. Artificial Intelligence (AI) has the potential to streamline complex pharmaceutical regulatory matters. Regulatory processes like audits and dossier completion can be automated with AI tools.

A comprehensive approach utilizing quantum machine learning in the study of corrosion inhibition on quinoxaline compounds

In this investigation, a quantitative structure-property relationship (QSPR) model coupled with a quantum neural network (QNN) was used to explore the corrosion inhibition efficiency (CIE) of quinoxaline compounds. Integrating quantum chemical properties (QCP) features reduced computational burden by strategically reducing the features from 11 to 4 while maintaining prediction accuracy. QNN models outperform traditional methods like artificial neural networks (ANN) and multilayer perceptron neural networks (MLPNN), with a coefficient of determination (R2) value of 0.987, coupled with diminished root mean square error (RMSE), mean absolute error (MAE), and mean absolute deviation (MAD) values of 0.97, 0.92, and 1.10, respectively. Predictions for six newly synthesized quinoxaline derivatives: quinoxaline-6-carboxylic acid (Q1), methyl quinoxaline-6-carboxylate (Q2), (2E,3E)-2,3-dihydrazono-1,2,3,4-tetrahydroquinoxaline (Q3), (2E,3E) 2,3-dihydrazono-6-methyl-1,2,3,4-tetrahydroquinoxaline (Q4), (E)-3-(4-methoxyethyl)-7-methylquinoxalin-2(1 H)-one (Q5), and 2-(4-methoxyphenyl)-7-methylthieno[3,2-b] quinoxaline (Q6), show remarkable CIE values of 95.12, 96.72, 91.02, 92.43, 89.58, and 93.63 %, respectively. This breakthrough technique simplifies testing and production procedures for new anti-corrosion materials.

The role of physicochemical and topological parameters in drug design

Quantitative structure activity relationship (QSAR) is a widely used tool in rational drug design that establishes relationships between the physicochemical and topological descriptors of ligands and their biological activities. Obtained QSAR models help identify descriptors that play pivotal roles in the biological activity of ligands. This not only helps the prediction of new compounds with desirable biological activities but also helps with the design of new compounds with better activities and low toxicities. QSAR commonly uses lipophilicity (logP), hydrophobicity (logD), water solubility (logS), the acid–base dissociation constant (pKa), the dipole moment, the highest occupied molecular orbital (HOMO), the lowest unoccupied molecular orbital (LUMO), molecular weight (MW), molar volume (MV), molar refractivity (MR), and the kappa index as physicochemical parameters. Some commonly used topological indices in QSAR are the Wiener index, Platt index, Hosoya index, Zagreb indices, Balaban index, and E-state index. This review presents a brief description of the significance of the most extensively used physicochemical and topological parameters in drug design.

Efficient photocatalytic degradation of ethylenethiourea with zinc oxide photocatalysts: Parameters optimization, mechanism insight, exploration of degradation pathways and toxicity assessment

Traditional advanced oxidation processes (AOPs) for the removal of toxic and refractory ethylenethiourea (ETU) is uneconomical because of the demand for continuous chemical input and full-scale systems. Herein, ETU was efficiently mineralized without sacrificial reagents for the first time through the utilization of ZnO nano-photocatalysts which were synthesized by the adjustment of ratio between the precursor ZnCl2 and alkaline source KOH during hydrothermal process. Compared to other ZnO samples, ZnO-1/3 (1:3 molar (M) ratio of ZnCl2 to KOH) exhibited higher apparent rate constant (0.00661 min−1) towards the degradation of ETU because of more effective electron-hole separation. The optimum conditions for ETU degradation (ZnO-1/3 dosage of 0.4 g/L, initial ETU concentration of 10 mg/L and photocatalytic reaction temperature of 35 °C) were acquired according to the outcome of the response surface methodology (RSM) based on the Box-Behnken design (BBD), and the predicted maximum removal efficiency of ETU reached 93.17 % and was consistent with the experimental results under this optimum conditions. The h+ and •OH were proved to the main reactive species during the photocatalytic process by the free radical capture experiments and electron spin resonance (ESR) analysis. Furthermore, the possible degradation pathways of ETU were suggested based on the identified degradation intermediates, and the reduced toxicity of intermediates was also demonstrated through the quantitative structure–activity relationship (QSAR) prediction and the growth of mung bean seedlings. Hence, photocatalysis with ZnO can be regarded as a promising alternative for ETU elimination.

Coordination Number Regulation of Iron Single‐Atom Nanozyme for Enhancing H2O2 Activation and Selectively Eliminating Cephalosporin Antibiotics

AbstractNanozymes present promising alternatives to natural enzymes, but controlling nanozymes' performance and employing them for selectively removing antibiotics are extremely challenging. Employing theoretical calculations to design the coordination environments of mental and coordination atoms for directing single‐atom nanozymes synthesis emerges as a promising strategy to enhance their efficiency and selectivity in antibiotic elimination. In this study, the peroxidase‐like specificity of iron single‐atom nanozymes (FeSA‐Nx, x = 2,3, and 4) with specific Fe–N coordination numbers is demonstrated based on theoretical calculations. These calculations guide the synthesis of corresponding ultra‐thin FeSA‐Nx, achieving a high degree of consistency between theoretical predictions and experimental results. FeSA‐N3 with a Fe─N3 coordination number proves to be the most effective. The efficient electron transfer from Fe─N3 site to H2O2 reduces the free energy required for •OH generation. Quantitative structure‐activity relationship (QSAR) analysis reveals a strong positive correlation between degradation efficiency of cephalosporins and their electron‐donating capabilities (R2 = 0.820–0.929), realizing selectively eliminating cephalosporins. Integration FeSA‐N3 into ceramic membrane (FeSA‐N3/CM) improves hydrophilicity, achieving continuous and stable removal of cephalosporin. This study provides valuable insights into coordination number regulating nanozyme properties for selective antibiotics removal and offers novel perspectives for FeSA‐N3 application in integrated systems.

Quantitative Structure-Property Relationship of the Rare-Earth Elements-Dibutyl Dithiophosphate Derivative Complexes Using Principal Component Analysis

The separation of rare-earth elements (REEs) has increasingly developed, especially using a complexing ligand of dibutyl dithiophosphate (DBDTP) that has numerous advantages as an extractant in the extraction process. Through technology development, this separation utilizes computational chemistry design to scheme the DBDTP ligand and its derivatives. One of the computational chemistry applications is a quantitative structure-property relationship (QSPR), which is useful for designing ligand derivatives by calculating molecular descriptors and connecting molecular structure with its physicochemical properties. In the present study, we aimed to get a dominant factor affecting complex stability formed from the REEs with DBDTP ligands and the REEs with DBDTP derivative ligands using principal component analysis for the QSPR study. The analysis results demonstrated that the stability of gadolinium, terbium, and dysprosium complex compounds was influenced by seven, seven, and six factors with a total variance of 93.93, 93.17, and 91.63%, respectively.

Prediction and Interpretability Study of the Glass Transition Temperature of Polyimide Based on Machine Learning with Quantitative Structure-Property Relationship (Tg-QSPR).

The glass transition temperature (Tg) is a crucial characteristic of polyimides (PIs). Developing a Tg predictive model using machine learning methodologies can facilitate the design of PI structures and expedite the development process. In this investigation, a data set comprising 1257 PIs was assembled, with Tg values determined using differential scanning calorimetry. 210 molecular descriptors were computed using RDKit, and subsequently, six distinct feature selection methodologies were employed to refine the descriptor set. Quantitative structure-property relationship models targeting Tg (Tg-QSPR) were then constructed using five ensemble learning algorithms and one deep learning algorithm. These models exhibited high predictive accuracy and robustness, with the CATBoost model demonstrating the highest accuracy, achieving a coefficient of determination of 0.823 for the test set, a mean absolute error of 20.1 °C, and a root-mean-square error of 29.0 °C. The study identified the NumRotatableBonds descriptor as particularly influential on Tg, showing a negative correlation with the property. Additionally, the model's accuracy was validated using ten new PI films not included in the original data set, resulting in absolute errors ranging from 2.5 to 26.9 °C and absolute percentage error rates of 1.0-12.8%. These findings underscore the importance of utilizing extensive and diverse data sets for predictive modeling to enhance accuracy and stability. Furthermore, exploring the interpretability of the model and experimentally validating newly synthesized PIs have augmented the practical utility of the model. Under the guidance of the Tg-QSPR models, it will be possible to accelerate the performance prediction and structural design of PIs in the future.

Elucidating structure-property relationships of guar gum biomolecules: insights from -polynomial and QSPR modeling.

This study investigates the quantitative structure-property relationship (QSPR) modeling of guar gum biomolecules, focusing on their structural parameters. Guar gum, a polysaccharide with diverse industrial applications, exhibits various properties such as viscosity, solubility, and emulsifying ability, which are influenced by its molecular structure. In this research, -polynomial and associated topological indices are employed as structural descriptors to represent the molecular structure of guar gum. The -polynomial and associated topological indices capture important structural features, including size, shape, branching, and connectivity. By correlating these descriptors with experimental data on guar gum properties, predictive models are developed using regression analysis techniques. The analysis revealed a strong correlation between the boiling point and molecular weight and all the considered topological descriptors. The resulting models offer insights into the relationship between guar gum structure and its properties, facilitating the optimization of guar gum production and application in various industries. This study demonstrates the utility of -polynomial and QSPR modeling in elucidating structure-property relationships of complex biomolecules like guar gum, contributing to the advancement of biomaterial science and industrial applications.

Application of machine learning models for property prediction to targeted protein degraders

Machine learning (ML) systems can model quantitative structure-property relationships (QSPR) using existing experimental data and make property predictions for new molecules. With the advent of modalities such as targeted protein degraders (TPD), the applicability of QSPR models is questioned and ML usage in TPD-centric projects remains limited. Herein, ML models are developed and evaluated for TPDs’ property predictions, including passive permeability, metabolic clearance, cytochrome P450 inhibition, plasma protein binding, and lipophilicity. Interestingly, performance on TPDs is comparable to that of other modalities. Predictions for glues and heterobifunctionals often yield lower and higher errors, respectively. For permeability, CYP3A4 inhibition, and human and rat microsomal clearance, misclassification errors into high and low risk categories are lower than 4% for glues and 15% for heterobifunctionals. For all modalities, misclassification errors range from 0.8% to 8.1%. Investigated transfer learning strategies improve predictions for heterobifunctionals. This is the first comprehensive evaluation of ML for the prediction of absorption, distribution, metabolism, and excretion (ADME) and physicochemical properties of TPD molecules, including heterobifunctional and molecular glue sub-modalities. Taken together, our investigations show that ML-based QSPR models are applicable to TPDs and support ML usage for TPDs’ design, to potentially accelerate drug discovery.

In Silico and In Vitro Studies of Terpenes from the Fabaceae Family Using the Phenotypic Screening Model against the SARS-CoV-2 Virus.

In 2019, the emergence of the seventh known coronavirus to cause severe illness in humans triggered a global effort towards the development of new drugs and vaccines for the SARS-CoV-2 virus. These efforts are still ongoing in 2024, including the present work where we conducted a ligand-based virtual screening of terpenes with potential anti-SARS-CoV-2 activity. We constructed a Quantitative Structure-Activity Relationship (QSAR) model from compounds with known activity against SARS-CoV-2 with a model accuracy of 0.71. We utilized this model to predict the activity of a series of 217 terpenes isolated from the Fabaceae family. Four compounds, predominantly triterpenoids from the lupane series, were subjected to an in vitro phenotypic screening in Vero CCL-81 cells to assess their inhibitory activity against SARS-CoV-2. The compounds which showed high rates of SARS-CoV-2 inhibition along with substantial cell viability underwent molecular docking at the SARS-CoV-2 main protease, papain-like protease, spike protein and RNA-dependent RNA polymerase. Overall, virtual screening through our QSAR model successfully identified compounds with the highest probability of activity, as validated using the in vitro study. This confirms the potential of the identified triterpenoids as promising candidates for anti-SARS-CoV-2 therapeutics.

Machine-Learning-Guided Peptide Drug Discovery: Development of GLP-1 Receptor Agonists with Improved Drug Properties.

Peptide-based drug discovery has surged with the development of peptide hormone-derived analogs for the treatment of diabetes and obesity. Machine learning (ML)-enabled quantitative structure-activity relationship (QSAR) approaches have shown great promise in small molecule drug discovery but have been less successful in peptide drug discovery due to limited data availability. We have developed a peptide drug discovery platform called streaMLine, enabling rigorous design, synthesis, screening, and ML-driven analysis of large peptide libraries. Using streaMLine, this study systematically explored secretin as a peptide backbone to generate potent, selective, and long-acting GLP-1R agonists with improved physicochemical properties. We synthesized and screened a total of 2688 peptides and applied ML-guided QSAR to identify multiple options for designing stable and potent GLP-1R agonists. One candidate, GUB021794, was profiled in vivo (S.C., 10 nmol/kg QD) and showed potent body weight loss in diet-induced obese mice and a half-life compatible with once-weekly dosing.

Prediction of Human Liver Microsome Clearance with Chirality-Focused Graph Neural Networks.

In drug candidate design, clearance is one of the most crucial pharmacokinetic parameters to consider. Recent advancements in machine learning techniques coupled with the growing accumulation of drug data have paved the way for the construction of computational models to predict drug clearance. However, concerns persist regarding the reliability of data collected from public sources, and a majority of current in silico quantitative structure-property relationship models tend to neglect the influence of molecular chirality. In this study, we meticulously examined human liver microsome (HLM) data from public databases and constructed two distinct data sets with varying HLM data quantity and quality. Two baseline models (RF and DNN) and three chirality-focused GNNs (DMPNN, TetraDMPNN, and ChIRo) were proposed, and their performance on HLM data was evaluated and compared with each other. The TetraDMPNN model, which leverages chirality from 2D structure, exhibited the best performance with a test R2 of 0.639 and a test root-mean-squared error of 0.429. The applicability domain of the model was also defined by using a molecular similarity-based method. Our research indicates that graph neural networks capable of capturing molecular chirality have significant potential for practical application and can deliver superior performance.

Photochemical transformation of liquid crystal monomers in simulated environmental media: Kinetics, mechanism, toxicity variation and QSAR modeling

Liquid crystal monomers (LCMs) are a new class of emerging pollutants with high octanol-water partition coefficients; however, their transformation behavior and associated risk to environments with high organic matter content has rarely been reported. In this study, we investigated the photodegradation kinetics, mechanism, and toxicity variation of 23 LCMs on leaf wax models (e.g., organic solvents methanol and n-hexane). The order of the photolysis rates of these LCMs were biphenylethyne LCMs > phenylbenzoate LCMs > diphenyl/terphenyl LCMs under simulated sunlight, while the phenylcyclohexane LCMs were resistant to photodegradation. The phenylbenzoate and biphenylethyne LCMs mainly undergo direct photolysis, while the diphenyl/terphenyl LCMs mainly undergo self-sensitized photolysis. The main photolysis pathways are the cleavage of ester bonds for phenylbenzoate LCMs, the addition, oxidation and cleavage of alkynyl groups for biphenylethyne LCMs, and the cleavage/oxidation of chains attached to phenyls and the benzene ring opening for diphenyl/terphenyls LCMs. Most photolysis products remained toxic to aquatic organisms to some degree. Additionally, two quantitative structure-activity relationship models for predicting kobs of LCMs in methanol and n-hexane were developed, and employed to predict kobs of 93 LCMs to fill the kobs data gap in systems mimicking leaf surfaces. These results can be helpful for evaluating the fate and risk of LCMs in environments with high content of organic phase.