3D Descriptors Research Articles

Unveiling the interspecies correlation and sensitivity factor analysis of rat and mouse acute oral toxicity of antimicrobial agents: first QSTR and QTTR Modeling report

Abstract This study aims to identify toxic potential and environmental hazardousness of antimicrobials. In this regard, the available experimental toxicity data with rat and mouse acute oral toxicity have been gathered from ChemID Plus database (n = 202) and subjected to data curation. Upon the data curation 51 and 68 compounds were left for the rat and mouse respectively for the modeling. The quantitative structure toxicity relationship (QSTR) and interspecies correlation analysis by quantitative toxicity-toxicity relationship (QTTR) modeling was approached in this study. The models were developed from 2D descriptors under OECD guidelines by using multiple linear regressions (MLR) with genetic algorithm (GA) for feature selection as a chemometric tool. The developed models were robust (Q2LOO = 0.600–0.679) and predictive enough (Q2Fn = 0.626–0.958, CCCExt = 0.840–0.893). The leverage approach of applicability domain (ad) analysis assures the model’s reliability. The antimicrobials without experimental toxicity values were classified as high, moderate and low toxic based on prediction and ad. The occurrence of the same classification from QSTR and QTTR models revealed the reliability of QTTR models.Finally, the applied “sensitivity factor analysis” typifies the sensitivity of chemicals toward each species. Overall, the first report will be helpful in the toxicity assessment of upcoming antimicrobials in rodents.

RJAN: Region‐based joint attention network for 3D shape recognition

AbstractAs an essential field of multimedia and computer vision, 3D shape recognition has attracted much research attention in recent years. Multiview‐based approaches have demonstrated their superiority in generating effective 3D shape representations. Typical methods usually extract the multiview global features and aggregate them together to generate 3D shape descriptors. However, there exist two disadvantages: First, the mainstream methods ignore the comprehensive exploration of local information in each view. Second, many approaches roughly aggregate multiview features by adding or concatenating them together. The information loss for some discriminative characteristics limits the representation effectiveness. To address these problems, a novel architecture named region‐based joint attention network (RJAN) was proposed. Specifically, the authors first design a hierarchical local information exploration module for view descriptor extraction. The region‐to‐region and channel‐to‐channel relationships from different granularities can be comprehensively explored and utilised to provide more discriminative characteristics for view feature learning. Subsequently, a novel relation‐aware view aggregation module is designed to aggregate the multiview features for shape descriptor generation, considering the view‐to‐view relationships. Extensive experiments were conducted on three public databases: ModelNet40, ModelNet10, and ShapeNetCore55. RJAN achieves state‐of‐the‐art performance in the tasks of 3D shape classification and 3D shape retrieval, which demonstrates the effectiveness of RJAN. The code has been released on https://github.com/slurrpp/RJAN.

Synthesis, Cytotoxicity, and Quantitative Structure–Activity Relationship Studies of Alkyl Triphenylphosphonium Pinostrobin Derivatives

AbstractPinostrobin, an isolated compound from Boesenbergia rotunda, has been shown to have potent anti‐proliferation and apoptosis effects in cancer stem‐like cells. However, its hydrophobic properties reduce its bioavailability. Eight new alkyl‐TPP+ pinostrobin derivatives were synthesized and assessed for their cytotoxicity against the human HepG2 liver cancer cell line using the alamarBlue assay. All derivatives exhibited moderate cytotoxicity, with the IC50 values ranging from 38.55–101.95 μM, and were more potent than pinostrobin and pinostrobin hydrazone (IC50>100 μM). Four alkyl‐TPP+ pinostrobin hydrazone amide derivatives showed more potent cytotoxicity than four alkyl‐TPP+ pinostrobin ester derivatives. QSAR analysis showed key 3D structural descriptors of TPP+‐based compounds responsible for cytotoxicity against HepG2 cells for the first time. The resultant 3D‐QSAR models using PLS and PCR methods were evaluated and found reliable in predicting the cytotoxicity of other TPP+‐based compounds.

Chemography-guided analysis of a reaction path network for ethylene hydrogenation with a model Wilkinson's catalyst.

Visualization and analysis of large chemical reaction networks become rather challenging when conventional graph-based approaches are used. As an alternative, we propose to use the chemical cartography ("chemography") approach, describing the data distribution on a 2-dimensional map. Here, the Generative Topographic Mapping (GTM) algorithm - an advanced chemography approach - has been applied to visualize the reaction path network of a simplified Wilkinson's catalyst-catalyzed hydrogenation containing some 105 structures generated with the help of the Artificial Force Induced Reaction (AFIR) method using either Density Functional Theory or Neural Network Potential (NNP) for potential energy surface calculations. Using new atoms permutation invariant 3D descriptors for structure encoding, we've demonstrated that GTM possesses the abilities to cluster structures that share the same 2D representation, to visualize potential energy surface, to provide an insight on the reaction path exploration as a function of time and to compare reaction path networks obtained with different methods of energy assessment.

Identification Tools of Microplastics from Surface Water Integrating Digital Image Processing and Statistical Techniques.

The primary objective of this study was to demonstrate the potential of digital image analysis as a tool to identify microplastic (MP) particles in surface waters and to facilitate their characterisation in terms of 2D and 3D morphology. Digital image analysis preceded by microscopic analysis was used for an exhaustive quantitative and qualitative evaluation of MPs isolated from the Vistula River. Using image processing procedures, 2D and 3D shape descriptors were determined. Principal Component Analysis was used to interpret the relationships between the parameters studied, characterising MP particle geometry, type and colour. This multivariate analysis of the data allowed three or four main factors to be extracted, explaining approximately 90% of the variation in the data characterising MP morphology. It was found that the first principal component for granules, flakes and films was largely represented by strongly correlated with 2D shape descriptors (area, perimeter, equivalent area diameter) and 3D shape descriptors (Corey Shape Factor, Compactness, Dimensionality). Considering the scraps, principal component PC1 was represented by only five of the above descriptors, and the Compactness variable had the largest contribution to principal component PC2. In addition, for granules, flakes and films, a relationship between 2D shape and the colour of their particles could be observed. For the most numerous MP group identified of multicoloured scraps, no such association was found. The results of our study can be used for further multivariate analysis regarding the presence of microplastic floating on the river surface, with a particular focus on particles of secondary origin. This is of key importance for optimising future efforts in conducting small-scale and multidimensional monitoring of and reducing plastics in the aquatic environment.

Intelligent Consensus Predictions of the Retention Index of Flavor and Fragrance Compounds Using 2D Descriptors

Intelligent Consensus Predictions of the Retention Index of Flavor and Fragrance Compounds Using 2D Descriptors

2D3D-DescNet: Jointly Learning 2D and 3D Local Feature Descriptors for Cross-Dimensional Matching

The cross-dimensional matching of 2D images and 3D point clouds is an effective method by which to establish the spatial relationship between 2D and 3D space, which has potential applications in remote sensing and artificial intelligence (AI). In this paper, we propose a novel multi-task network, 2D3D-DescNet, to learn 2D and 3D local feature descriptors jointly and perform cross-dimensional matching of 2D image patches and 3D point cloud volumes. The 2D3D-DescNet contains two branches with which to learn 2D and 3D feature descriptors, respectively, and utilizes a shared decoder to generate the feature maps of 2D image patches and 3D point cloud volumes. Specifically, the generative adversarial network (GAN) strategy is embedded to distinguish the source of the generated feature maps, thereby facilitating the use of the learned 2D and 3D local feature descriptors for cross-dimensional retrieval. Meanwhile, a metric network is embedded to compute the similarity between the learned 2D and 3D local feature descriptors. Finally, we construct a 2D-3D consistent loss function to optimize the 2D3D-DescNet. In this paper, the cross-dimensional matching of 2D images and 3D point clouds is explored with the small object of the 3Dmatch dataset. Experimental results demonstrate that the 2D and 3D local feature descriptors jointly learned by 2D3D-DescNet are similar. In addition, in terms of 2D and 3D cross-dimensional retrieval and matching between 2D image patches and 3D point cloud volumes, the proposed 2D3D-DescNet significantly outperforms the current state-of-the-art approaches based on jointly learning 2D and 3D feature descriptors; the cross-dimensional retrieval at TOP1 on the 3DMatch dataset is improved by over 12%.

Complementary pseudo multimodal feature for point cloud anomaly detection

Point cloud anomaly detection is steadily emerging as a promising research area. Recognizing the importance of feature descriptiveness in this task, this study introduces the Complementary Pseudo Multimodal Feature (CPMF), which combines local geometrical information extracted by 3D handcrafted descriptors with global semantic information extracted from 2D pre-trained neural networks. Specifically, to leverage 2D pre-trained neural networks for point-wise feature extraction, this study projects original point clouds into multi-view images. These images are then fed into a pre-trained 2D neural network for informative 2D modality feature extraction. Following the 2D–3D correspondence, the multi-view 2D modality features are projected back to 3D space and aggregated to obtain point-wise 2D modality features. Finally, the point-wise 3D and 2D modality features are fused to derive the CPMF for point cloud anomaly detection. Extensive experiments conducted on MVTec 3D and Real3D datasets demonstrate the complementary capacity between 2D and 3D modality features and the effectiveness of CPMF. Notably, CPMF achieves a significantly higher object-level AUROC of 95.15% compared to other methods on the MVTec 3D benchmark. Code is available at https://github.com/caoyunkang/CPMF.

Geometric epitope and paratope prediction.

Identifying the binding sites of antibodies is essential for developing vaccines and synthetic antibodies. In this article, we investigate the optimal representation for predicting the binding sites in the two molecules and emphasize the importance of geometric information. Specifically, we compare different geometric deep learning methods applied to proteins' inner (I-GEP) and outer (O-GEP) structures. We incorporate 3D coordinates and spectral geometric descriptors as input features to fully leverage the geometric information. Our research suggests that different geometrical representation information is useful for different tasks. Surface-based models are more efficient in predicting the binding of the epitope, while graph models are better in paratope prediction, both achieving significant performance improvements. Moreover, we analyze the impact of structural changes in antibodies and antigens resulting from conformational rearrangements or reconstruction errors. Through this investigation, we showcase the robustness of geometric deep learning methods and spectral geometric descriptors to such perturbations. The python code for the models, together with the data and the processing pipeline, is open-source and available at https://github.com/Marco-Peg/GEP.

Machine learning models and performance dependency on 2D chemical descriptor space for retention time prediction of pharmaceuticals

The predictive modeling of liquid chromatography methods can be an invaluable asset, potentially saving countless hours of labor while also reducing solvent consumption and waste. Tasks such as physicochemical screening and preliminary method screening systems where large amounts of chromatography data are collected from fast and routine operations are particularly well suited for both leveraging large datasets and benefiting from predictive models. Therefore, the generation of predictive models for retention time is an active area of development. However, for these predictive models to gain acceptance, researchers first must have confidence in model performance and the computational cost of building them should be minimal. In this study, a simple and cost-effective workflow for the development of machine learning models to predict retention time using only Molecular Operating Environment 2D descriptors as input for support vector regression is developed. Furthermore, we investigated the relative performance of models based on molecular descriptor space by utilizing uniform manifold approximation and projection and clustering with Gaussian mixture models to identify chemically distinct clusters. Results outlined herein demonstrate that local models trained on clusters in chemical space perform equivalently when compared to models trained on all data. Through 10-fold cross-validation on a comprehensive set containing 67,950 of our company's proprietary analytes, these models achieved coefficients of determination of 0.84 and 3 % error in terms of retention time. This promising statistical significance is found to translate from cross-validation to prospective prediction on an external test set of pharmaceutically relevant analytes. The observed equivalency of global and local modeling of large datasets is retained with METLIN's SMRT dataset, thereby confirming the wider applicability of the developed machine learning workflows for global models.

From molecular descriptors to the developmental toxicity prediction of pesticides/veterinary drugs/bio-pesticides against zebrafish embryo: Dual computational toxicological approaches for prioritization

The escalating introduction of pesticides/veterinary drugs into the environment has necessitated a rapid evaluation of their potential risks to ecosystems and human health. The developmental toxicity of pesticides/veterinary drugs was less explored, and much less the large-scale predictions for untested pesticides, veterinary drugs and bio-pesticides. Alternative methods like quantitative structure-activity relationship (QSAR) are promising because their potential to ensure the sustainable and safe use of these chemicals. We collected 133 pesticides and veterinary drugs with half-maximal active concentration (AC50) as the zebrafish embryo developmental toxicity endpoint. The QSAR model development adhered to rigorous OECD principles, ensuring that the model possessed good internal robustness (R2 > 0.6 and QLOO2 > 0.6) and external predictivity (Rtest2 > 0.7, QFn2 >0.7, and CCCtest > 0.85). To further enhance the predictive performance of the model, a quantitative read-across structure-activity relationship (q-RASAR) model was established using the combined set of RASAR and 2D descriptors. Mechanistic interpretation revealed that dipole moment, the presence of C-O fragment at 10 topological distance, molecular size, lipophilicity, and Euclidean distance (ED)-based RA function were main factors influencing toxicity. For the first time, the established QSAR and q-RASAR models were combined to prioritize the developmental toxicity of a vast array of true external compounds (pesticides/veterinary drugs/bio-pesticides) lacking experimental values. The prediction reliability of each query molecule was evaluated by leverage approach and prediction reliability indicator. Overall, the dual computational toxicology models can inform decision-making and guide the design of new pesticides/veterinary drugs with improved safety profiles.

Models for the No-Observed-Effect Concentration (NOEC) and Maximal Half-Effective Concentration (EC50).

Typical in silico models for ecotoxicology focus on a few endpoints, but there is a need to increase the diversity of these models. This study proposes models using the NOEC for the harlequin fly (Chironomus riparius) and EC50 for swollen duckweed (Lemna gibba) for the first time. The data were derived from the EFSA OpenFoodTox database. The models were based on the correlation weights of molecular features used to calculate the 2D descriptor in CORAL software. The Monte Carlo method was used to calculate the correlation weights of the algorithms. The determination coefficients of the best models for the external validation set were 0.74 (NOAEC) and 0.85 (EC50).

Robust Point Cloud Registration for Aircraft Engine Pipeline Systems.

Aircraft engine systems are composed of numerous pipelines. It is crucial to regularly inspect these pipelines to detect any damages or failures that could potentially lead to serious accidents. The inspection process typically involves capturing complete 3D point clouds of the pipelines using 3D scanning techniques from multiple viewpoints. To obtain a complete and accurate representation of the aircraft pipeline system, it is necessary to register and align the individual point clouds acquired from different views. However, the structures of aircraft pipelines often appear similar from different viewpoints, and the scanning process is prone to occlusions, resulting in incomplete point cloud data. The occlusions pose a challenge for existing registration methods, as they can lead to missing or wrong correspondences. To this end, we present a novel registration framework specifically designed for aircraft pipeline scenes. The proposed framework consists of two main steps. First, we extract the point feature structure of the pipeline axis by leveraging the cylindrical characteristics observed between adjacent blocks. Then, we design a new 3D descriptor called PL-PPFs (Point Line-Point Pair Features), which combines information from both the pipeline features and the engine assembly line features within the aircraft pipeline point cloud. By incorporating these relevant features, our descriptor enables accurate identification of the structure of the engine's piping system. Experimental results demonstrate the effectiveness of our approach on aircraft engine pipeline point cloud data.

Comparative Analysis of Chemical Descriptors by Machine Learning Reveals Atomistic Insights into Solute-Lipid Interactions.

This study explores the research area of drug solubility in lipid excipients, an area persistently complex despite recent advancements in understanding and predicting solubility based on molecular structure. To this end, this research investigated novel descriptor sets, employing machine learning techniques to understand the determinants governing interactions between solutes and medium-chain triglycerides (MCTs). Quantitative structure-property relationships (QSPR) were constructed on an extended solubility data set comprising 182 experimental values of structurally diverse drug molecules, including both development and marketed drugs to extract meaningful property relationships. Four classes of molecular descriptors, ranging from traditional representations to complex geometrical descriptions, were assessed and compared in terms of their predictive accuracy and interpretability. These include two-dimensional (2D) and three-dimensional (3D) descriptors, Abraham solvation parameters, extended connectivity fingerprints (ECFPs), and the smooth overlap of atomic position (SOAP) descriptor. Through testing three distinct regularized regression algorithms alongside various preprocessing schemes, the SOAP descriptor enabled the construction of a superior performing model in terms of interpretability and accuracy. Its atom-centered characteristics allowed contributions to be estimated at the atomic level, thereby enabling the ranking of prevalent molecular motifs and their influence on drug solubility in MCTs. The performance on a separate test set demonstrated high predictive accuracy (RMSE = 0.50) for 2D and 3D, SOAP, and Abraham Solvation descriptors. The model trained on ECFP4 descriptors resulted in inferior predictive accuracy. Lastly, uncertainty estimations for each model were introduced to assess their applicability domains and provide information on where the models may extrapolate in chemical space and, thus, where more data may be necessary to refine a data-driven approach to predict solubility in MCTs. Overall, the presented approaches further enable computationally informed formulation development by introducing a novel in silico approach for rational drug development and prediction of dose loading in lipids.

Graph theoretical descriptors differentiate d-Mannose isomers in the principal component proposed feature space: A computational approach

The intricate nature of carbohydrates, particularly monosaccharides, stems from the existence of several chiral centers within their tertiary structures. Predicting and characterizing the molecular geometries and electrostatic landscapes of these substances is difficult due to their complex electrical properties. Moreover, these structures can display a substantial degree of conformational flexibility due to the presence of many rotatable bonds. Moreover, identifying and distinguishing between D and L enantiomers of monosaccharides presents a significant analytical obstacle since there is a need for empirically measurable properties that can distinguish them. This work uses Principal Component Analysis (PCA) to explore the chemical information included in 3D descriptors in order to comprehend the conformational space of d-Mannose stereoisomers. The isomers may be discriminated by utilizing 3D matrix-based indices, geometrical descriptors, and RDF descriptors. The isomers can be distinguished by descriptors, such as the Harary-like index from the reciprocal squared geometrical matrix (H_RG), Harary-like index from Coulomb matrix (H_Coulomb), Wiener-like index from Coulomb matrix (Wi_Coulomb), Wiener-like index from geometrical matrix (Wi_G), Graph energy from Coulomb matrix (SpAbs_Coulomb), Spectral absolute deviation from Coulomb matrix (SpAD_Coulomb), and Spectral positive sum from Coulomb matrix (SpPos_Coulomb). Among these descriptors, the first two, H_RG and H_Coulomb, perform the best in differentiation among the 3D-Matrix-Based Descriptors (3D-MBD) class. The results obtained from this study provide a significant chemical insight into the structural characteristics of the compounds inside the graph theoretical framework. These findings are likely to serve as the basis for developing new methods for analytical experiments.

Alternative weighting schemes for fine‐tuned extended similarity indices

AbstractExtended similarity indices (i.e., generalization of pairwise similarity) have recently gained importance because of their simplicity, fast computation, and superiority in tasks like diversity picking. However, they operate with several meta parameters that should be optimized. Earlier, we extended the binary similarity indices to “discrete non‐binary” and “continuous” data; now we continue with introducing and comparing multiple weighting functions. As a case study, the similarity of CYP enzyme inhibitors (4016 molecules after curation) was characterized by their extended similarities, based on 2D descriptors, MACCS and Morgan fingerprints. A statistical workflow based on sum of ranking differences (SRD) and analysis of variance (ANOVA) was used for finding the optimal weight function(s). Overall, the best weighting function is the fraction (“frac”), which corresponds to the principle of parsimony. Optimal extended similarity indices were also found, and their differences are revealed across different data sets. We intend this work to be a guideline for users of extended similarity indices regarding the various weighting options available. Source code for the calculations is available at https://github.com/mqcomplab/MultipleComparisons.

Quantitative read-across structure-activity relationship (q-RASAR): A novel approach to estimate the subchronic oral safety (NOAEL) of diverse organic chemicals in rats

We have developed a quantitative safety prediction model for subchronic repeated doses of diverse organic chemicals on rats using the novel quantitative read-across structure-activity relationship (q-RASAR) approach, which uses similarity-based descriptors for predictive model generation. The experimental -Log (NOAEL) values have been used here as a potential indicator of oral subchronic safety on rats as it determines the maximum dose level for which no observed adverse effects of chemicals are found. A total of 186 data points of diverse organic chemicals have been used for the model generation using structural and physicochemical (0D–2D) descriptors. The read-across-derived similarity, error, and concordance measures (RASAR descriptors) have been extracted from the preliminary 0D–2D descriptors. Then, the combined pool of RASAR and the identified 0D–2D descriptors of the training set were employed to develop the final models by using the partial least squares (PLS) algorithm. The developed PLS model was rigorously validated by various internal and external validation metrics as suggested by the Organization for Economic Co-operation and Development (OECD). The final q-RASAR model is proven to be statistically sound, robust and externally predictive (R2 = 0.85, Q2LOO = 0.82 and Q2F1 = 0.94), superseding the internal as well as external predictivity of the corresponding quantitative structure-activity relationship (QSAR) model as well as previously reported subchronic repeated dose toxicity model found in the literature. In a nutshell, the q-RASAR is an effective approach that has the potential to be used as a good alternative way to improve external predictivity, interpretability, and transferability for subchronic oral safety prediction as well as ecotoxicity risk identification.

3D shape descriptor design based on HKS and persistent homology with stability analysis

In recent years, with the rapid development of the computer aided design and computer graphics, a large number of 3D models have emerged, making it a challenge to quickly find models of interest. As a concise and informative representation of 3D models, shape descriptors are a key factor in achieving effective retrieval. In this paper, we propose a novel global descriptor for 3D models that incorporates both geometric and topological information. We refer to this descriptor as the persistent heat kernel signature descriptor (PHKS). Constructed by concatenating our isometry-invariant geometric descriptor with topological descriptor, PHKS possesses high recognition ability, while remaining insensitive to noise and can be efficiently calculated. Retrieval experiments of 3D models on the benchmark datasets show considerable performance gains of the proposed method compared to other descriptors based on HKS and advanced topological descriptors.

Three-dimensional biphase fabric estimation from 2D images by deep learning

A pruned VGG19 model subjected to Axial Coronal Sagittal (ACS) convolutions and a custom VGG16 model are benchmarked to predict 3D fabric descriptors from a set of 2D images. The data used for training and testing are extracted from a set of 600 3D biphase microstructures created numerically. Fabric descriptors calculated from the 3D microstructures constitute the ground truth, while the input data are obtained by slicing the 3D microstructures in each direction of space at regular intervals. The computational cost to train the custom ACS-VGG19 model increases linearly with p (the number of images extracted in each direction of space), and increasing p does not improve the performance of the model - or only does so marginally. The best performing ACS-VGG19 model provides a MAPE of 2 to 5% for the means of aggregate size, aspect ratios and solidity, but cannot be used to estimate orientations. The custom VGG16 yields a MAPE of 2% or less for the means of aggregate size, distance to nearest neighbor, aspect ratios and solidity. The MAPE is less than 3% for the mean roundness, and in the range of 5-7% for the aggregate volume fraction and the mean diagonal components of the orientation matrix. Increasing p improves the performance of the custom VGG16 model, but becomes cost ineffective beyond 3 images per direction. For both models, the aggregate volume fraction is predicted with less accuracy than higher order descriptors, which is attributed to the bias given by the loss function towards highly-correlated descriptors. Both models perform better to predict means than standard deviations, which are noisy quantities. The custom VGG16 model performs better than the pruned version of the ACS-VGG19 model, likely because it contains 3 times (p = 1) to 28 times (p = 10) less parameters than the ACS-VGG19 model, allowing better and faster cnvergence, with less data. The custom VGG16 model predicts the second and third invariants of the orientation matrix with a MAPE of 2.8% and 8.9%, respectively, which suggests that the model can predict orientation descriptors regardless of the orientation of the input images.

The rat acute oral toxicity of trifluoromethyl compounds (TFMs): a computational toxicology study combining the 2D-QSTR, read-across and consensus modeling methods.

All areas of the modern society are affected by fluorine chemistry. In particular, fluorine plays an important role in medical, pharmaceutical and agrochemical sciences. Amongst various fluoro-organic compounds, trifluoromethyl (CF3) group is valuable in applications such as pharmaceuticals, agrochemicals and industrial chemicals. In the present study, following the strict OECD modelling principles, a quantitative structure-toxicity relationship (QSTR) modelling for the rat acute oral toxicity of trifluoromethyl compounds (TFMs) was established by genetic algorithm-multiple linear regression (GA-MLR) approach. All developed models were evaluated by various state-of-the-art validation metrics and the OECD principles. The best QSTR model included nine easily interpretable 2D molecular descriptors with clear physical and chemical significance. The mechanistic interpretation showed that the atom-type electro-topological state indices, molecular connectivity, ionization potential, lipophilicity and some autocorrelation coefficients are the main factors contributing to the acute oral toxicity of TFMs against rats. To validate that the selected 2D descriptors can effectively characterize the toxicity, we performed the chemical read-across analysis. We also compared the best QSTR model with public OPERA tool to demonstrate the reliability of the predictions. To further improve the prediction range of the QSTR model, we performed the consensus modelling. Finally, the optimum QSTR model was utilized to predict a true external set containing many untested/unknown TFMs for the first time. Overall, the developed model contributes to a more comprehensive safety assessment approach for novel CF3-containing pharmaceuticals or chemicals, reducing unnecessary chemical synthesis whilst saving the development cost of new drugs.