First report of q-RASAR modeling toward an approach of easy interpretability and efficient transferability

Predicting zeta potential of liposomes from their structure: A nano-QSPR model for DOPE, DC-Chol, DOTAP, and EPC formulations

Liposomes, nanoscale spherical structures composed of amphiphilic lipids, hold great promise for various pharmaceutical applications, especially as nanocarriers in targeted drug delivery, due to their biocompatibility, biodegradability, and low immunogenicity. Understanding the factors influencing their physicochemical properties is crucial for designing and optimizing liposomes. In this study, we have presented the kernel-weighted local polynomial regression (KwLPR) nano-quantitative structure-property relationships (nano-QSPR) model to predict the zeta potential (ZP) based on the structure of 12 liposome formulations, including 1,2-dioleoyl-sn-glycero-3-phosphoethanolamine (DOPE), 3ß-[N-(N',N'-dimethylaminoethane)-carbamoyl]cholesterol (DC-Chol), 1,2-dioleoyl-3-trimethylammonium-propane (DOTAP), and L-α-phosphatidylcholine (EPC). The developed model is well-fitted (R 2 = 0.96, RMSE C = 5.76), flexible (Q CVloo 2 = 0.83, RMSE CVloo = 10.77), and reliable (Q Ext 2 = 0.89 RMSE Ext = 5.17). Furthermore, we have established the formula for computing molecular nanodescriptors for liposomes, based on constituent lipids' molar fractions. Through the correlation matrix and principal component analysis (PCA), we have identified two key structural features affecting liposomes' zeta potential: hydrophilic-lipophilic balance (HLB) and enthalpy of formation. Lower HLB values, indicating a more lipophilic nature, are associated with a higher zeta potential, and thus stability. Higher enthalpy of formation reflects reduced zeta potential and decreased stability of liposomes. We have demonstrated that the nano-QSPR approach allows for a better understanding of how the composition and molecular structure of liposomes affect their zeta potential, filling a gap in ZP nano-QSPR modeling methodologies for nanomaterials (NMs). The proposed proof-of-concept study is the first step in developing a comprehensive and computationally based system for predicting the physicochemical properties of liposomes as one of the most important drug nano-vehicles.

Machine learning-based q-RASAR predictions of the bioconcentration factor of organic molecules estimated following the organisation for economic co-operation and development guideline 305

In this study, we utilized an innovative quantitative read-across (RA) structure-activity relationship (q-RASAR) approach to predict the bioconcentration factor (BCF) values of a diverse range of organic compounds, based on a dataset of 575 compounds tested using Organisation for Economic Co-operation and Development Test Guideline 305 for bioaccumulation in fish. Initially, we constructed the q-RASAR model using the partial least squares regression method, yielding promising statistical results for the training set (R2 =0.71, Q2LOO=0.68, mean absolute error [MAE]training=0.54). The model was further validated using the test set (Q2F1=0.77, Q2F2=0.75, MAEtest=0.51). Subsequently, we explored the q-RASAR method using other regression-based supervised machine-learning algorithms, demonstrating favourable results for the training and test sets. All models exhibited R2 and Q2F1 values exceeding 0.7, Q2LOO values greater than 0.6, and low MAE values, indicating high model quality and predictive capability for new, unidentified chemical substances. These findings represent the significance of the RASAR method in enhancing predictivity for new unknown chemicals due to the incorporation of similarity functions in the RASAR descriptors, independent of a specific algorithm.

Classification and regression machine learning models for predicting the combined toxicity and interactions of antibiotics and fungicides mixtures

Antibiotics and triazole fungicides coexist in varying concentrations in natural aquatic environments, resulting in complex mixtures. These mixtures can potentially affect aquatic ecosystems. Accurately distinguishing synergistic and antagonistic mixtures and predicting mixture toxicity are crucial for effective mixture risk assessment. We tested the toxicities of 75 binary mixtures of antibiotics and fungicides against Auxenochlorella pyrenoidosa. Both regression and classification models for these mixtures were developed using machine learning models: random forest (RF), k-nearest neighbors (KNN), and kernel k-nearest neighbors (KKNN). The KKNN model emerged as the best regression model with high values of determination coefficient (R2 = 0.977), explained variance in prediction leave-one-out (Q2LOO = 0.894), and explained variance in external prediction (Q2F1 = 0.929, Q2F2 = 0.929, and Q2F3 = 0.923). The RF model, the leading classifier, exhibited high accuracy (accuracy = 1 for the training set and 0.905 for the test set) in distinguishing the synergistic and antagonistic mixtures. These results provide crucial value for the risk assessment of mixtures.

Molecular similarity in chemical informatics and predictive toxicity modeling: from quantitative read-across (q-RA) to quantitative read-across structure-activity relationship (q-RASAR) with the application of machine learning

This article aims to provide a comprehensive critical, yet readable, review of general interest to the chemistry community on molecular similarity as applied to chemical informatics and predictive modeling with a special focus on read-across (RA) and read-across structure-activity relationships (RASAR). Molecular similarity-based computational tools, such as quantitative structure-activity relationships (QSARs) and RA, are routinely used to fill the data gaps for a wide range of properties including toxicity endpoints for regulatory purposes. This review will explore the background of RA starting from how structural information has been used through to how other similarity contexts such as physicochemical, absorption, distribution, metabolism, and elimination (ADME) properties, and biological aspects are being characterized. More recent developments of RA's integration with QSAR have resulted in the emergence of novel models such as ToxRead, generalized read-across (GenRA), and quantitative RASAR (q-RASAR). Conventional QSAR techniques have been excluded from this review except where necessary for context.

How safe are wild-caught salmons exposed to various industrial chemicals? First ever in silico models for salmon toxicity data gaps filling

Salmons are crucial to ecosystems and economic activities like commercial fishing and aquaculture, while also serving as an important source of nutrients, underscoring their ecological significance and the need for sustainable management. To better understand the toxicity and biological interactions between the salmon and industrial chemicals in the aquatic environment, we utilized the ToxValDB database to develop first ever computational toxicity models for six salmon subspecies (covering Atlantic and Pacific salmon) across two genera, employing Quantitative Structure-Activity Relationship (QSAR) and quantitative Read-Across Structure-Activity Relationship (q-RASAR) methods. For three smaller datasets (Oncorhynchus nerka, Oncorhynchus keta, and Oncorhynchus gorbuscha), we created mathematical models using the entire datasets where QSAR models demonstrated superior statistical quality compared to q-RASAR. Conversely, the three larger datasets (Oncorhynchus kisutch, Oncorhynchus tshawytscha, and Salmon salar) were divided into training and test sets, the q-RASAR models yielded better results compared to QSAR models. Mechanistic interpretations of these models revealed that descriptors such as Burden eigenvalues (BCUT), autocorrelation of topological structure (ATSC), and molecular polarizability were significant predictors of toxicity. For instance, higher polarizability and certain topological features were associated with increased toxicity as per the developed models. Statistically superior models for each subspecies were used to predict the aquatic toxicity of 1085 untested organic chemicals for toxicity data gap filling and risk assessment considering the applicability domain (AD). These insights are pivotal for designing safer chemicals and emphasize the need for sustainable management of salmon populations.

Introducing third-generation periodic table descriptors for nano-qRASTR modeling of zebrafish toxicity of metal oxide nanoparticles

Metal oxide nanoparticles (MONPs) are widely used in medicine and environmental remediation because of their unique properties. However, their size, surface area, and reactivity can cause toxicity, potentially leading to oxidative stress, inflammation, and cellular or DNA damage. In this study, a nano-quantitative structure-toxicity relationship (nano-QSTR) model was initially developed to assess zebrafish toxicity for 24 MONPs. Previously established 23 first- and second-generation periodic table descriptors, along with five newly proposed third-generation descriptors derived from the periodic table, were employed. Subsequently, to enhance the quality and predictive capability of the nano-QSTR model, a nano-quantitative read across structure-toxicity relationship (nano-qRASTR) model was created. This model integrated read-across descriptors with modeled descriptors from the nano-QSTR approach. The nano-qRASTR model, featuring three attributes, outperformed the previously reported simple QSTR model, despite having one less MONP. This study highlights the effective utilization of the nano-qRASTR algorithm in situations with limited data for modeling, demonstrating superior goodness-of-fit, robustness, and predictability (R 2 = 0.81, Q 2 LOO = 0.70, Q 2 F1/R 2 PRED = 0.76) compared to simple QSTR models. Finally, the developed nano-qRASTR model was applied to predict toxicity data for an external dataset comprising 35 MONPs, addressing gaps in zebrafish toxicity assessment.

Leveraging new approach methodologies: ecotoxicological modelling of endocrine disrupting chemicals to Danio rerio through machine learning and toxicity studies

New approach methodologies (NAMs) offer information tailored to the intended application while reducing the use of animals. NAMs aim to develop quantitative structure-activity relationship (QSAR) and quantitive-Read-Across structure-activity relationship (q-RASAR) models to predict and categorize the acute toxicity of known and unknown endocrine-disrupting chemicals (EDCs) against zebrafish. EDCs are a diverse group of toxic substances that disrupt the endocrine system of humans and animals. The q-RASAR model was constructed and verified using validation metrics (R2 = 0.886 and Q2 = 0.814) which found to be more reliable model compare to QSAR model. The substructure fingerprint was well-fitted for the classification model and it was validated using 10-fold average accuracy (Q = 86.88%), specificity (Sp = 88.89%), Matthew's correlation curve (MCC = 0.621) and receiver operating characteristics (ROC = 0.828). The dataset of unknown substances revealed that phenolphthalein (Php) exhibited a significant level of toxicity based on q-RASAR model. The docking and simulation study indicated that the computationally derived important features successfully bound to the target zebrafish sex hormone binding globulin (zfSHBG). The experimental LC50 value of 0.790 mg L-1 was very close to the predicted value of 0.763 mg L-1, which provides high confidence to the developed model.

The application of chemical similarity measures in an unconventional modeling framework c-RASAR along with dimensionality reduction techniques to a representative hepatotoxicity dataset

With the exponential progress in the field of cheminformatics, the conventional modeling approaches have so far been to employ supervised and unsupervised machine learning (ML) and deep learning models, utilizing the standard molecular descriptors, which represent the structural, physicochemical, and electronic properties of a particular compound. Deviating from the conventional approach, in this investigation, we have employed the classification Read-Across Structure-Activity Relationship (c-RASAR), which involves the amalgamation of the concepts of classification-based quantitative structure-activity relationship (QSAR) and Read-Across to incorporate Read-Across-derived similarity and error-based descriptors into a statistical and machine learning modeling framework. ML models developed from these RASAR descriptors use similarity-based information from the close source neighbors of a particular query compound. We have employed different classification modeling algorithms on the selected QSAR and RASAR descriptors to develop predictive models for efficient prediction of query compounds' hepatotoxicity. The predictivity of each of these models was evaluated on a large number of test set compounds. The best-performing model was also used to screen a true external data set. The concepts of explainable AI (XAI) coupled with Read-Across were used to interpret the contributions of the RASAR descriptors in the best c-RASAR model and to explain the chemical diversity in the dataset. The application of various unsupervised dimensionality reduction techniques like t-SNE and UMAP and the supervised ARKA framework showed the usefulness of the RASAR descriptors over the selected QSAR descriptors in their ability to group similar compounds, enhancing the modelability of the dataset and efficiently identifying activity cliffs. Furthermore, the activity cliffs were also identified from Read-Across by observing the nature of compounds constituting the nearest neighbors for a particular query compound. On comparing our simple linear c-RASAR model with the previously reported models developed using the same dataset derived from the US FDA Orange Book ( https://www.accessdata.fda.gov/scripts/cder/ob/index.cfm ), it was observed that our model is simple, reproducible, transferable, and highly predictive. The performance of the LDA c-RASAR model on the true external set supersedes that of the previously reported work. Therefore, the present simple LDA c-RASAR model can efficiently be used to predict the hepatotoxicity of query chemicals.

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.

Identification of structural features of surface modifiers in engineered nanostructured metal oxides regarding cell uptake through ML-based classification

Nanoparticles (NPs) are considered as versatile tools in various fields including medicine, electronics, and environmental science. Understanding the structural aspects of surface modifiers in nanoparticles that govern their cellular uptake is crucial for optimizing their efficacy and minimizing potential cytotoxicity. The cellular uptake is influenced by multiple factors, namely, size, shape, and surface charge of NPs, as well as their surface functionalization. In the current study, classification-based ML models (i.e., Bayesian classification, random forest, support vector classifier, and linear discriminant analysis) have been developed to identify the features/fingerprints that significantly contribute to the cellular uptake of ENMOs in multiple cell types, including pancreatic cancer cells (PaCa2), human endothelial cells (HUVEC), and human macrophage cells (U937). The best models have been identified for each cell type and analyzed to detect the structural fingerprints/features governing the cellular uptake of ENMOs. The study will direct scientists in the design of ENMOs of higher cellular uptake efficiency for better therapeutic response.

ARKA: a framework of dimensionality reduction for machine-learning classification modeling, risk assessment, and data gap-filling of sparse environmental toxicity data

Due to the lack of experimental toxicity data for environmental chemicals, there arises a need to fill data gaps by in silico approaches. One of the most commonly used in silico approaches for toxicity assessment of small datasets is the Quantitative Structure-Activity Relationship (QSAR), which generates predictive models for the efficient prediction of query compounds. However, the reliability of the predictions from QSARs derived from small datasets is often questionable from a statistical point of view. This is due to the presence of a larger number of descriptors as compared to the number of training compounds, which reduces the degree of freedom of the developed model. To reduce the overall prediction error for a particular QSAR model, we have proposed here the computation of the novel Arithmetic Residuals in K-groups Analysis (ARKA) descriptors. We have reduced the number of modeling descriptors in a supervised manner by partitioning them into K classes (K = 2 here) depending on the higher mean normalized values of the descriptors to a particular response class, thus preventing the loss of chemical information. A scatter plot of the data points using the values of two ARKA descriptors (ARKA_2 vs. ARKA_1) can potentially identify activity cliffs, less confident data points, and less modelable data points. We have used here five representative environmentally relevant endpoints (skin sensitization, earthworm toxicity, milk/plasma partitioning, algal toxicity, and rodent carcinogenicity of hazardous chemicals) with graded responses to which the ARKA framework was applied for classification modeling. On comparing the performance of the models generated using conventional QSAR descriptors and the ARKA descriptors, the prediction quality of the models derived from ARKA descriptors was found, based on multiple graded-data validation metrics-derived decision criteria, much better than the models derived from QSAR descriptors signifying the potential of ARKA descriptors in ecotoxicological classification modeling of small data sets. Additionally, this holds true for the Read-Across approach as well, since the Read-Across predictions using ARKA descriptors supersede the predictions generated from QSAR descriptors. For the ease of users, a Java-based expert system has been developed that computes the ARKA descriptors from the input of QSAR descriptors.

Unveiling critical structural features for effective HDAC8 inhibition: a comprehensive study using quantitative read-across structure-activity relationship (q-RASAR) and pharmacophore modeling

Histone deacetylases constitute a group of enzymes that participate in several biological processes. Notably, inhibiting HDAC8 has become a therapeutic strategy for various diseases. The current inhibitors for HDAC8 lack selectivity and target multiple HDACs. Consequently, there is a growing recognition of the need for selective HDAC8 inhibitors to enhance the effectiveness of therapeutic interventions. In our current study, we have utilized a multi-faceted approach, including Quantitative Structure-Activity Relationship (QSAR) combined with Quantitative Read-Across Structure-Activity Relationship (q-RASAR) modeling, pharmacophore mapping, molecular docking, and molecular dynamics (MD) simulations. The developed q-RASAR model has a high statistical significance and predictive ability (Q2F1:0.778, Q2F2:0.775). The contributions of important descriptors are discussed in detail to gain insight into the crucial structural features in HDAC8 inhibition. The best pharmacophore hypothesis exhibits a high regression coefficient (0.969) and a low root mean square deviation (0.944), highlighting the importance of correctly orienting hydrogen bond acceptor (HBA), ring aromatic (RA), and zinc-binding group (ZBG) features in designing potent HDAC8 inhibitors. To confirm the results of q-RASAR and pharmacophore mapping, molecular docking analysis of the five potent compounds (44, 54, 82, 102, and 118) was performed to gain further insights into these structural features crucial for interaction with the HDAC8 enzyme. Lastly, MD simulation studies of the most active compound (54, mapped correctly with the pharmacophore hypothesis) and the least active compound (34, mapped poorly with the pharmacophore hypothesis) were carried out to validate the observations of the studies above. This study not only refines our understanding of essential structural features for HDAC8 inhibition but also provides a robust framework for the rational design of novel selective HDAC8 inhibitors which may offer insights to medicinal chemists and researchers engaged in the development of HDAC8-targeted therapeutics.

Data-Driven Approaches to Predict Dendrimer Cytotoxicity

Dendrimers are employed as functional elements in contrast agents and are proposed as nontoxic vehicles for drug delivery. Toxicity is a property that is to be evaluated for this novel class of bionanomaterials for in vivo applications. The current research is hampered due to the lack of structured data sets for toxicity studies for dendrimers. In this work, we have built a data set by curating literature for toxicity data and augmented it with structural and physicochemical features. We present a comprehensive, feature-rich database of dendrimer toxicity measured across various cell lines for prediction, design, and optimization studies. We have also explored novel computational approaches for predicting dendrimer cytotoxicity. We demonstrate superior outcomes for toxicity prediction using essential regression in the space of small data sets.

Chemometrics-driven prediction and prioritization of diverse pesticides on chickens for addressing hazardous effects on public health

The extensive use of various pesticides in the agriculture field badly affects both chickens and humans, primarily through residues in food products and environmental exposure. This study offers the first quantitative structure-toxicity relationship (QSTR) and quantitative read-across-structure toxicity relationship (q-RASTR) models encompassing the LOEL and NOEL endpoints for acute toxicity in chicken, a widely consumed protein. The study's significance lies in the direct link between chemical toxicity in chicken, human intake, and environmental damage. Both the QSTR and the similarity-based read-across algorithms are applied concurrently to improve the predictability of the models. The q-RASTR models were generated by combining read-across derived similarity and error-based parameters, alongside structural and physicochemical descriptors. Machine Learning approaches (SVM and RR) were also employed with the optimization of relevant hyperparameters based on the cross-validation approach, and the final test set prediction results were compared. The PLS-based q-RASTR models for NOEL and LOEL endpoints showed good statistical performance, as traced from the external validation metrics Q2F1: 0.762-0.844; Q2F2: 0.759-0.831 and MAEtest: 0.195-0.214. The developed models were further used to screen the Pesticide Properties DataBase (PPDB) for potential toxicants in chickens. Thus, established models can address eco-toxicological data gaps and development of novel and safe eco-friendly pesticides.

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.

Prediction of acute toxicity for Chlorella vulgaris caused by tire wear particle-derived compounds using quantitative structure-activity relationship models

Tire wear particles (TWPs) enter aquatic ecosystems through various pathways, such as rainwater and urban runoff. Additives in TWPs can harm aquatic organisms in these ecosystems. Therefore, it is essential to investigate their toxicity to aquatic organisms. In our study, we initially recorded the median effective concentrations of 21 TWP-derived compounds on Chlorella vulgaris growth, ranging from 0.04 to 8.60 mg/L. Subsequently, through an extensive review of the literature, we incorporated 112 compounds with specific toxicity endpoints to construct the QSAR model using genetic algorithm and multiple linear regression techniques, followed by the construction of the consensus model and the quantitative read-across structure-activity relationship (q-RASAR) model. Meanwhile, we employed rigorous internal and external validation measures to assess the performance of the model. The results indicated that the developed q-RASAR model exhibited strong adaptation, robustness, and reliable prediction, with q-RASAR indicators of Q2LOO = 0.7673, R2tr = 0.8079, R2test = 0.8610, Q2Fn = 0.8285-0.8614, and CCCtest = 0.9222. Based on an external dataset containing 128 emerging TWP-derived compounds, the model's applicability domain coverage was 90.6 %. The q-RASAR model predicted that the structure of diphenylamine was associated with higher toxicity, possibly liked to the SpMax2_Bhm and LogBCF descriptors. The established model reliably provides prediction and fills a critical data gap. These findings highlight the potential risks posed by emerging TWP-derived compounds to aquatic organisms.

Breaking the Barriers: Machine-Learning-Based c-RASAR Approach for Accurate Blood-Brain Barrier Permeability Prediction

The intricate nature of the blood-brain barrier (BBB) poses a significant challenge in predicting drug permeability, which is crucial for assessing central nervous system (CNS) drug efficacy and safety. This research utilizes an innovative approach, the classification read-across structure-activity relationship (c-RASAR) framework, that leverages machine learning (ML) to enhance the accuracy of BBB permeability predictions. The c-RASAR framework seamlessly integrates principles from both read-across and QSAR methodologies, underscoring the need to consider similarity-related aspects during the development of the c-RASAR model. It is crucial to note that the primary goal of this research is not to introduce yet another model for predicting BBB permeability but rather to showcase the refinement in predicting the BBB permeability of organic compounds through the introduction of a c-RASAR approach. This groundbreaking methodology aims to elevate the accuracy of assessing neuropharmacological implications and streamline the process of drug development. In this study, an ML-based c-RASAR linear discriminant analysis (LDA) model was developed using a dataset of 7807 compounds, encompassing both BBB-permeable and -nonpermeable substances sourced from the B3DB database (freely accessible from https://github.com/theochem/B3DB), for predicting BBB permeability in lead discovery for CNS drugs. The model's predictive capability was then validated using three external sets: one containing 276,518 natural products (NPs) from the LOTUS database (accessible from https://lotus.naturalproducts.net/download) for data gap filling, another comprising 13,002 drug-like/drug compounds from the DrugBank database (available from https://go.drugbank.com/), and a third set of 56 FDA-approved drugs to assess the model's reliability. Further diversifying the predictive arsenal, various other ML-based c-RASAR models were also developed for comparison purposes. The proposed c-RASAR framework emerged as a powerful tool for predicting BBB permeability. This research not only advances the understanding of molecular determinants influencing CNS drug permeability but also provides a versatile computational platform for the rapid assessment of diverse compounds, facilitating informed decision-making in drug development and design.

Application of machine learning-based read-across structure-property relationship (RASPR) as a new tool for predictive modelling: Prediction of power conversion efficiency (PCE) for selected classes of organic dyes in dye-sensitized solar cells (DSSCs)

The application of various in-silico-based approaches for the prediction of various properties of materials has been an effective alternative to experimental methods. Recently, the concepts of Quantitative structure-property relationship (QSPR) and read-across (RA) methods were merged to develop a new emerging chemoinformatic tool: read-across structure-property relationship (RASPR). The RASPR method can be applicable to both large and small datasets as it uses various similarity and error-based measures. It has also been observed that RASPR models tend to have an increased external predictivity compared to the corresponding QSPR models. In this study, we have modeled the power conversion efficiency (PCE) of organic dyes used in dye-sensitized solar cells (DSSCs) by using the quantitative RASPR (q-RASPR) method. We have used relatively larger classes of organic dyes-Phenothiazines (n=207), Porphyrins (n=281), and Triphenylamines (n=229) for the modelling purpose. We have divided each of the datasets into training and test sets in 3 different combinations, and with the training sets we have developed three different QSPR models with structural and physicochemical descriptors and validated them with the corresponding test sets. These corresponding modeled descriptors were used to calculate the RASPR descriptors using a Java-based tool RASAR Descriptor Calculator v2.0 (https://sites.google.com/jadavpuruniversity.in/dtc-lab-software/home), and then data fusion was performed by pooling the previously selected structural and physicochemical descriptors with the calculated RASPR descriptors. Further feature selection algorithm was employed to develop the final RASPR PLS models. Here, we also developed different machine learning (ML) models with the descriptors selected in the QSPR PLS and RASPR PLS models, and it was found that models with RASPR descriptors superseded in external predictivity the models with only structural and physicochemical descriptors: RMSEP reduced for phenothiazines from 1.16-1.25 to 1.07-1.18, for porphyrins from 1.60-1.79 to 1.45-1.53, for triphenylamines from 1.27-1.54 to 1.20-1.47.

First report on pesticide sub-chronic and chronic toxicities against dogs using QSAR and chemical read-across

Excessive use of chemicals is the outcome of the industrialization of agricultural sectors which leads to disturbance of ecological balance. Various agrochemicals are widely used in agricultural fields, urban green areas, and to protect from various pest-associated diseases. Due to their long-term health and environmental hazards, chronic toxicity assessment is crucial. Since in vivo and in vitro toxicity assessments are costly, lengthy, and require a large number of animal experiments, in silico toxicity approaches are better alternatives to save time, cost, and animal experimentation. We have developed the first regression-based 2D-QSAR models using different sub-chronic and chronic toxicity data of pesticides against dogs employing 2D descriptors. From the statistical results (n train = 53 - 62 ,   r 2  = 0.614 to 0.754, Q L O O 2  = 0.501 to 0.703 and   Q F 1 2  = 0.531 to 0.718, Q F 2 2 = 0.523 - 0.713 ), it was concluded that the models are robust, reliable, interpretable, and predictive. Similarity-based read-across algorithm was also used to improve the predictivity (Q F 1 2 = 0.595 - 0.813 , Q F 2 2 = 0.573 - 0.809 ) of the models. 5132 chemicals obtained from the CPDat and 1694 pesticides obtained from the PPDB database were also screened using the developed models, and their predictivity and reliability were checked. Thus, these models will be helpful for eco-toxicological data-gap filling, toxicity prediction of untested pesticides, and development of novel, safer & eco-friendly pesticides.

Protein-protein interaction network analysis for the identification of novel multi-target inhibitors and target miRNAs against Alzheimer's disease

This study presents a strategy for extracting significant gene complexes and then provides prospective therapeutics for AD. In this research, a total of 7905 reports published from 1981 to 2022 were retrieved. Following a review of all those articles, only the genetic association studies on AD were considered. Finally, there is a list of 453 Alzheimer-related genes in our dataset for network analysis. To this end, an experimentally derived protein-protein interaction (PPI) network from the String database was utilized to extract four meaningful gene complexes functionally interconnected using Cytoscape v3.9.1 software. The acquired gene complexes were subjected to an enrichment analysis using the ClueGO v2.5.9 tool to emphasize the most significant biological processes and pathways. Afterward, extracted gene complexes were used to extract the drugs related to AD from DGI v3.0 database and introduce some new drugs which may be helpful for this disease. Finally, a comprehensive network that included every gene connected to each gene complex group as well as the drug targets for each gene has been shown. Moreover, molecular docking studies have been performed with the selected compounds to identify the interaction pattern with the respective targets. Finally, we proposed a list of 62 compounds as multi-targeted directed drug-like compounds with a degree value between 2 and 5 and 30 compounds as target-specific drug-like compounds, which have not been proclaimed as AD-related drugs in prior scientific and medical investigations. Then, new drugs were suggested that can be experimentally examined for future work. In addition to this, four bipartite networks representing each group's genes and target miRNAs were established to introduce target miRNAs by using the miRWalk v3 server.

The pursuit of accurate predictive models of the bioactivity of small molecules

Property prediction is a key interest in chemistry. For several decades there has been a continued and incremental development of mathematical models to predict properties. As more data is generated and accumulated, there seems to be more areas of opportunity to develop models with increased accuracy. The same is true if one considers the large developments in machine and deep learning models. However, along with the same areas of opportunity and development, issues and challenges remain and, with more data, new challenges emerge such as the quality and quantity and reliability of the data, and model reproducibility. Herein, we discuss the status of the accuracy of predictive models and present the authors' perspective of the direction of the field, emphasizing on good practices. We focus on predictive models of bioactive properties of small molecules relevant for drug discovery, agrochemical, food chemistry, natural product research, and related fields.

Integrated predictive QSAR, Read Across, and q-RASAR analysis for diverse agrochemical phytotoxicity in oat and corn: A consensus-based approach for risk assessment and prioritization

In the modern fast-paced lifestyle, time-efficient and nutritionally rich foods like corn and oat have gained popularity for their amino acids and antioxidant contents. The increasing demand for these cereals necessitates higher production which leads to dependency on agrochemicals, which can pose health risks through residual present in the plant products. To first report the phytotoxicity for corn and oat, our study employs QSAR, quantitative Read-Across and quantitative RASAR (q-RASAR). All developed QSAR and q-RASAR models were equally robust (R2 = 0.680-0.762, Q2Loo = 0.593-0.693, Q2F1 = 0.680-0.860) and find their superiority in either oat or corn model, respectively, based on MAE criteria. AD and PRI had been performed which confirm the reliability and predictability of the models. The mechanistic interpretation reveals that the symmetrical arrangement of electronegative atoms and polar groups directly influences the toxicity of compounds. The final phytotoxicity and prioritization are performed by the consensus approach which results into selection of 15 most toxic compounds for both species.

Unveiling first report on in silico modeling of aquatic toxicity of organic chemicals to Labeo rohita (Rohu) employing QSAR and q-RASAR

Labeo rohita, a fish species within the Carp family, holds significant dietary and aquacultural importance in South Asian countries. However, the habitats of L. rohita often face exposure to various harmful pesticides and organic compounds originating from industrial and agricultural runoff. It is challenging to individually investigate the effects of each potentially harmful compound. In such cases, in silico techniques like Quantitative Structure-Activity Relationship (QSAR) and quantitative Read-Across Structure-Activity Relationship (q-RASAR) can be employed to construct algorithmic models capable of simultaneously assessing the toxicity of numerous compounds. We utilized the US EPA's ToxValDB database to curate data regarding acute median lethal concentration (LC50) toxicity for L. rohita. The experimental variables included study type (mortality), study duration (ranging from 0.25 h to 4 h), exposure route (static, flowthrough, and renewal), exposure method (drinking water), and types of chemicals (industrial chemicals and pharmaceuticals). Using this dataset, we developed regression-based QSAR and q-RASAR models to predict chemical toxicity to L. rohita based on chemical descriptors. The key descriptors for predicting the toxicity of L. rohita in the regression-based QSAR model include F05[S-Cl], SpMax_EA(ri), s4_relPathLength_2, and SpDiam_AEA(ed). These descriptors can be employed to estimate the toxicity of untested compounds and aid in the development of compounds with lower toxicity based on the presence or absence of these descriptors. Both the QSAR and q-RASAR models serve as valuable tools for understanding the chemicals' structural features responsible for toxicity and for filling gaps in aquatic toxicity data by predicting the toxicity of newly untested compounds in relation to L. rohita. Finally, the developed best model was employed to predict 297 external chemicals, the most toxic substances to L. rohita were identified as cyhalothrin, isobornyl thiocyanatoacetate, and paclobutrzol, while the least toxic ones included ethyl acetate, ethylthiourea, and n-butyric acid.

First report on chemometric modeling of tilapia fish aquatic toxicity to organic chemicals: Toxicity data gap filling

The Toxic Substances Control Act (TSCA) mandates the Environmental Protection Agency (EPA) to document chemicals entering the US. Due to the vast range of toxicity endpoints, experimental toxicological study for all chemicals is impossible to conduct. To address this, in silico methods like QSAR and read-across are strategically used to prioritize testing for chemicals lacking ecotoxicity data. Aquatic toxicity is one of the most critical endpoints directly related to aquatic species, mainly fish, followed by direct to indirect effects on humans through drinking water and fish as food, respectively. Therefore, we have employed the ToxValDB database to curate acute LC50 toxicity data for three Tilapia species covering two different genera, an ideal species for aquatic toxicity testing. Employing the curated dataset, we have developed multiple robust and predictive QSAR and quantitative read-across structure-activity relationship (q-RASAR) models for Tilapia zillii, Oreochromis niloticus, and Oreochromis mossambicus which helped to understand the toxicological mode of action (MoA) of the modeled chemicals and predict the aquatic toxicity of new untested chemicals followed by toxicity data gap filling. The best three QSAR models showed encouraging statistical quality in terms of determination coefficient R2 (0.94, 0.74, and 0.77), cross-validated leave-one-out Q2 (0.90, 0.67 and 0.70), and predictive capability in terms of R2pred (0.95, 0.77, and 0.74) for T. zillii, O. niloticus, and O. mossambicus datasets, respectively. The developed best mathematical models were used for the prediction of aquatic toxicity in terms of pLC50 for 297 untested organic chemicals across three major Tilapia species ranging from 1.841 to 8.561 M in terms of environmental risk assessment.

A deep-learning approach for identifying prospective chemical hazards

With the aim of helping to set safe exposure limits for the general population, various techniques have been implemented to conduct risk assessments for chemicals and other environmental stressors; however, none of these tools facilitate the identification of completely new chemicals that are likely hazardous and elicit an adverse biological effect. Here, we detail a novel in silico, deep-learning framework that is designed to systematically generate structures for new chemical compounds that are predicted to be chemical hazards. To assess the utility of the framework, we applied the tool to four endpoints related to environmental toxicants and their impacts on human and animal health: (i) toxicity to honeybees, (ii) immunotoxicity, (iii) endocrine disruption via ER-α antagonism, and (iv) mutagenicity. In addition, we characterized the predicted potency of these compounds and examined their structural relationship to existing chemicals of concern. As part of the array of emerging new approach methodologies (NAMs), we anticipate that such a framework will be a significant asset to risk assessors and other environmental scientists when planning and forecasting. Though not in the scope of the present study, we expect that the methodology detailed here could also be useful in the de novo design of more environmentally-friendly industrial chemicals.

q-RASTR modelling for prediction of diverse toxic chemicals towards T. pyriformis

A series of diverse organic compounds impose serious detrimental effects on the health of living organisms and the environment. Determination of the structural aspects of compounds that impart toxicity and evaluation of the same is crucial before public usage. The present study aims to determine the structural characteristics of compounds for Tetrahymena pyriformis toxicity using the q-RASTR (Quantitative Read Across Structure-Toxicity Relationship) model. It was developed using RASTR and 2-D descriptors for a dataset of 1792 compounds with defined endpoint (pIGC50) against a model organism, T. pyriformis. For the current study, the whole dataset was divided based on activity/property into the training and test sets, and the q-RASTR model was developed employing six descriptors (three latent variables) having r2, Q2F1 and Q2 values of 0.739, 0.767, and 0.735, respectively. The generated model was thoroughly validated using internationally recognized internal and external validation criteria to assess the model's dependability and predictability. It was highlighted that high molecular weight, aromatic hydroxyls, nitrogen, double bonds, and hydrophobicity increase the toxicity of organic compounds. The current study demonstrates the applicability of the RASTR algorithm in QSTR model development for the prediction of toxic chemicals (pIGC50) towards T. pyriformis.

Development of a read-across-derived classification model for the predictions of mutagenicity data and its comparison with traditional QSAR models and expert systems

Mutagenicity is considered an important endpoint from the regulatory, environmental and medical points of view. Due to the wide number of compounds that may be of concern and the enormous expenses (in terms of time, money, and animals) associated with rodent mutagenicity bioassays, this endpoint is a major target for the development of alternative approaches for screening and prediction. The majority of old-aged expert systems and quantitative structure-activity relationship (QSAR) models may show reduced performance over time for their application on newer chemical candidates; thus, researchers constantly try to improve the modeling strategies. In our report, we initially performed traditional classification-based linear discriminant analysis (LDA) QSAR modeling using the benchmark Ames dataset of diverse chemicals (6512 compounds) to recognize the relationship between the molecules and their potential mutagenic behavior. The classical LDA QSAR model is developed from a selected set of 2D descriptors. The LDA QSAR model was developed by using a total of 31 descriptors identified from the analysis of the most discriminating features. Additionally, we have used similarity-derived features obtained from the read-across (RA) to develop an RA-based QSAR model. The developed RA-based LDA QSAR model has better predictivity, transferability, and interpretability compared to the LDA QSAR model, and it uses a very small number of descriptors compared to the classical QSAR model. Different machine learning (ML) models were also developed using the descriptors appearing in the read-across-based LDA QSAR model for comparative studies. We have checked the prediction quality of 216 true external set compounds using the novel similarity-derived RA model. The performance of the OECD toolbox is also compared with the RA-derived LDA QSAR model for a true external set. The current study aimed to explore the significance of the read-across-based algorithm and its application to the most current experimental mutagenicity data to complement already available expert systems.

Quantitative Read-across structure-activity relationship (q-RASAR): A new approach methodology to model aquatic toxicity of organic pesticides against different fish species

We have developed quantitative toxicity prediction models for organic pesticides of agricultural importance considering different fish species using a novel quantitative Read-across structure-activity relationship (q-RASAR) approach. The current study uses experimental (Log 1/LC50) data of organic pesticides to various fish species, including Rainbow trout (RT: Oncorhynchus mykiss: 715 data points), Lepomis (LP: Lepomis macrochirus: 136 data points), and Miscellaneous (Pimephales promelas, Brachydanio rerio: 226 data points). This study has also discussed the validation of the developed models and the analysis of structural features that are important for aquatic toxicity towards fishes. The read-across-derived similarity, error, and concordance measures (RASAR descriptors) have been extracted from the preliminary 0D-2D descriptors; the combined pool of RASAR and selected 0D-2D descriptors have been used to develop the final models by employing partial least squares algorithm. All the q-RASAR models are acceptable in terms of goodness of fit, robustness, and external predictivity, superseding the quality of the respective QSAR models, as seen from the computed validation metrics. The q-RASAR is an effective approach that has the potential to be used as a good alternative way to enhance external predictivity, interpretability, and transferability for aquatic toxicity prediction as well as ecotoxicity potential identification.

Read-across-based intelligent learning: development of a global q-RASAR model for the efficient quantitative predictions of skin sensitization potential of diverse organic chemicals

Environmental chemicals and contaminants cause a wide array of harmful implications to terrestrial and aquatic life which ranges from skin sensitization to acute oral toxicity. The current study aims to assess the quantitative skin sensitization potential of a large set of industrial and environmental chemicals acting through different mechanisms using the novel quantitative Read-Across Structure-Activity Relationship (q-RASAR) approach. Based on the identified important set of structural and physicochemical features, Read-Across-based hyperparameters were optimized using the training set compounds followed by the calculation of similarity and error-based RASAR descriptors. Data fusion, further feature selection, and removal of prediction confidence outliers were performed to generate a partial least squares (PLS) q-RASAR model, followed by the application of various Machine Learning (ML) tools to check the quality of predictions. The PLS model was found to be the best among different models. A simple user-friendly Java-based software tool was developed based on the PLS model, which efficiently predicts the toxicity value(s) of query compound(s) along with their status of Applicability Domain (AD) in terms of leverage values. This model has been developed using structurally diverse compounds and is expected to predict efficiently and quantitatively the skin sensitization potential of environmental chemicals to estimate their occupational and health hazards.

Machine learning - based q-RASAR modeling to predict acute contact toxicity of binary organic pesticide mixtures in honey bees

We have reported here a quantitative read-across structure-activity relationship (q-RASAR) model for the prediction of binary mixture toxicity (acute contact toxicity) in honey bees. Both the quantitative structure-activity relationship (QSAR) and the similarity-based read-across algorithms are used simultaneously for enhancing the predictability of the model. Several similarity and error-based parameters, obtained from the read-across prediction tool, have been put together with the structural and physicochemical descriptors to develop the final q-RASAR model. The calculated statistical and validation metrics indicate the goodness-of-fit, robustness, and good predictability of the partial least squares (PLS) regression model. Machine learning algorithms like ridge regression, linear support vector machine (SVM), and non-linear SVM have been used to further enhance the predictability of the q-RASAR model. The prediction quality of the q-RASAR models outperforms the previously reported quasi-SMILEs-based QSAR model in terms of external correlation coefficient (Q2F1 SVM q-RASAR: 0.935 vs. Q2VLD QSAR: 0.89). In this research, the toxicity values of several new untested binary mixtures have been predicted with the new models, and the reliability of the PLS predictions has been validated by the prediction reliability indicator tool. The q-RASAR approach can be used as reliable, complementary, and integrative to the conventional experimental approaches of pesticide mixture risk assessment.

"Data fusion" quantitative read-across structure-activity-activity relationships (q-RASAARs) for the prediction of toxicities of binary and ternary antibiotic mixtures toward three bacterial species

Antibiotics are often found in the environment as pollutants. They are usually found as mixtures in the environment and may produce toxicity against different ecological species due to joint exposure in the sub-optimal range. Sometimes the degradation products of parent chemicals also interact with it and cause mixture toxicity. In this study, we have developed three different mixture-Quantitative Structure-Activity Relationship (mixture-QSAR) models for three different bacterial species (Vibrio fischeri, Escherichia coli, and Bacillus subtilis). The toxicity data were collected from a previous experimental report in the literature, which comprised binary and ternary mixtures of sulfonamides (SAs), sulfonamide potentiators (SAPs), and tetracyclines (TCs). We have also explored the interspecies modeling to find inter-correlation among the toxicity of these studied organisms and have developed quantitative structure activity-activity relationship (QSAAR) models by employing the "data fusion" quantitative read-across structure-activity-activity relationship (q-RASAAR) and partial least squares (PLS) regression algorithms. All the models are strictly validated using both internal and external validation tests as suggested in the OECD guidelines. Three different mixing rules have been used in this study for descriptor computations to incorporate the additive and interaction effects among the mixture components. To the best of our knowledge, this is the first report of interspecies mixture toxicity models which can predict the cellular toxicity of binary and ternary mixtures against any of the three above-mentioned organisms.

Prediction-Inspired Intelligent Training for the Development of Classification Read-across Structure-Activity Relationship (c-RASAR) Models for Organic Skin Sensitizers: Assessment of Classification Error Rate from Novel Similarity Coefficients

The advancements in the field of cheminformatics have led to a reduction in animal testing to estimate the activity, property, and toxicity of query chemicals. Read-across structure-activity relationship (RASAR) is an emerging concept that utilizes various similarity functions derived from chemical information to develop highly predictive models. Unlike quantitative structure-activity relationship (QSAR) models, RASAR descriptors of a query compound are computed from its close congeners instead of the compound itself, thus targeting predictions in the model training phase. The objective of the present study is not to propose new QSAR models for skin sensitization but to demonstrate the enhancement in the quality of predictions of the skin-sensitizing potential of organic compounds by developing classification-based RASAR (c-RASAR) models. A diverse, previously curated data set was collected from the literature for which 2D descriptors were computed. The extracted essential features were then used to develop a classification-based linear discriminant analysis (LDA) QSAR model. Furthermore, from the read-across-based predictions, RASAR descriptors were calculated using the basic settings of the hyperparameters for the Laplacian Kernel-based optimum similarity measure. After feature selection, an LDA c-RASAR model was developed, which superseded the prediction quality of the LDA-QSAR model. Various other combinations of RASAR descriptors were also taken to develop additional c-RASAR models, all showing better prediction quality than the LDA QSAR model while using a lower number of descriptors. Various other machine learning c-RASAR models were also developed for comparison purposes. In this work, we have proposed and analyzed three new similarity metrics: gm_class, sm1, and sm2. The first one is an indicator variable used to generate a simple univariate c-RASAR model with good prediction ability, while the remaining two are similarity indices used to analyze possible activity cliffs in the training and test sets and are believed to play an important role in the modelability analysis of data sets.

QSAR and Chemical Read-Across Analysis of 370 Potential MGMT Inactivators to Identify the Structural Features Influencing Inactivation Potency

O6-methylguanine-DNA methyltransferase (MGMT) constitutes an important cellular mechanism for repairing potentially cytotoxic DNA damage induced by guanine O6-alkylating agents and can render cells highly resistant to certain cancer chemotherapeutic drugs. A wide variety of potential MGMT inactivators have been designed and synthesized for the purpose of overcoming MGMT-mediated tumor resistance. We determined the inactivation potency of these compounds against human recombinant MGMT using [3H]-methylated-DNA-based MGMT inactivation assays and calculated the IC50 values. Using the results of 370 compounds, we performed quantitative structure-activity relationship (QSAR) modeling to identify the correlation between the chemical structure and MGMT-inactivating ability. Modeling was based on subdividing the sorted pIC50 values or on chemical structures or was random. A total of nine molecular descriptors were presented in the model equation, in which the mechanistic interpretation indicated that the status of nitrogen atoms, aliphatic primary amino groups, the presence of O-S at topological distance 3, the presence of Al-O-Ar/Ar-O-Ar/R..O..R/R-O-C=X, the ionization potential and hydrogen bond donors are the main factors responsible for inactivation ability. The final model was of high internal robustness, goodness of fit and prediction ability (R2pr = 0.7474, Q2Fn = 0.7375-0.7437, CCCpr = 0.8530). After the best splitting model was decided, we established the full model based on the entire set of compounds using the same descriptor combination. We also used a similarity-based read-across technique to further improve the external predictive ability of the model (R2pr = 0.7528, Q2Fn = 0.7387-0.7449, CCCpr = 0.8560). The prediction quality of 66 true external compounds was checked using the "Prediction Reliability Indicator" tool. In summary, we defined key structural features associated with MGMT inactivation, thus allowing for the design of MGMT inactivators that might improve clinical outcomes in cancer treatment.

In silico assessment of risks associated with pesticides exposure during pregnancy

Novel Quantitative Structure-Activity Relationship (QSAR) models of compounds' placenta (PL) permeability expressed as their log FM (fetus-to-mother blood concentration) values or binary PL1/0 (crossing/non-crossing) score were generated using a number of statistical tools: Multiple Linear Regression, Boosted Trees, Principal Component Analysis and Artificial Neural Networks, on the basis of molecular descriptors calculated by Mordred software and selected using Partial Least Squares (PLS) analysis. It was established that the most important predictor of both log FM and the binary PL1/0 score is Lipinski - a binary variable reflecting the compounds' ability to satisfy the criteria of drug-likeness according to the Lipinski's "Rule of 5". The quantitative (log FM) and qualitative (PL1/0) models of PL permeability were applied to 345 pesticides from different chemical families (triazines, carbamates, pyrethroids, organochlorine, organophosphorus and miscellaneous compounds). The ability of studied pesticides to cross the placenta was assessed; the basic physico-chemical parameters responsible for good or poor placenta transport of pesticides were identified and the relationships between the pesticides' PL permeability, blood-brain barrier (BBB) transfer and gastro-intestinal (GI) absorption were investigated. It was found (on the basis of logistic regression analysis) that the probability of a compound crossing the placenta (PL1) is inversely correlated with its lipophilicity and molar refractivity and positively correlated with the total count of oxygen and nitrogen atoms.

Data driven toxicity assessment of organic chemicals against Gammarus species using QSAR approach

Nowadays, organic chemicals play an essential role in almost all walks of life and have become indispensable to modern society. However, the continually synthesized chemicals and the numerous potential adverse endpoints against living organisms increasingly promote the regulators regarding the computational approach as a crucial supplement and an alternative to the traditional animal tests in chemical risk assessment. In this present research, we evaluated the ecotoxicity of chemicals against four typical Gammarus species, which constituted a critical element in detritus cycle and also the recommended species for water monitoring. We first screened the molecular descriptors based on the Genetic Algorithm and then developed the Quantitative Structure-Activity Relationship models using the Multiple Linear Regression method. The statistical results from various validation metrics suggested that the obtained models were internally robust and externally predictive. The application domain analysis based on the leverage approach and standardized residual method demonstrated the broad application range of each model. The interpretation of molecular descriptors in each model suggested that the chemicals with higher polarity and hydrophilicity tend to be less toxic, whereas the lipophilic moieties would enhance the chemical toxicity. Meanwhile, the other selected descriptors, such as Chi-cluster, heterocyclic, and distance matrix descriptors, manifested that the chemical toxicity was also affected by molecular branching, connectivity, electrotopological state, and other various properties. In summary, the present work proposed well-performed QSAR models and clarified the possible toxic mechanism of chemicals against Gammarus species. The obtained models could help predict the toxicity data and conduct a preliminary risk assessment, thus guiding the subsequent animal tests and reducing the assessment cost.

Ecotoxicological QSAR study of fused/non-fused polycyclic aromatic hydrocarbons (FNFPAHs): Assessment and priority ranking of the acute toxicity to Pimephales promelas by QSAR and consensus modeling methods

Fused/non-fused polycyclic aromatic hydrocarbons (FNFPAHs) have a variety of toxic effects on ecosystems and human body, but the acquisition of their toxicity data is greatly limited by the limited resources available. Here, we followed the EU REACH regulation and used Pimephales promelas as a model organism to investigate the quantitative structure-activity relationship (QSAR) between the FNFPAHs and their toxicity for the aquatic environment for the first time. We developed a single QSAR model (SM1) containing five simple and interpretable 2D molecular descriptors, which met the validation of OECD QSAR-related principles, and analyzed their mechanistic relationships with toxicity in detail. The model had good degree of fitting and robustness, and had better external prediction performance (MAE_test = 0.4219) than ECOSAR model (MAE_test = 0.5614). To further enhance its prediction accuracy, the three qualified single models (SMs) were used for constructing consensus models (CMs), the best one CM2 (MAE_test = 0.3954) had a significantly higher prediction accuracy for test compounds than SM1, and also outperformed the T.E.S.T. consensus model (MAE_test = 0.4233). Subsequently, the toxicity of 252 true external FNFPAHs from Pesticide Properties Database (PPDB) was predicted by SM1, the prediction results showed that 94.84 % compounds were reliably predicted within the model's application domain (AD). We also applied the best CM2 to predict the untested 252 FNFPAHs. Furthermore, we provided a mechanistic analysis and explanation for pesticides ranked as top 10 most toxic FNFPAHs. In summary, all developed QSAR and consensus models can be used as efficient tools for predicting the acute toxicity of unknown FNFPAHs to Pimephales promelas, thus being important for the risk assessment and regulation of FNFPAHs contamination in aquatic environment.

Hi-MGT: A hybrid molecule graph transformer for toxicity identification

Conventional toxicity testing methods that rely on animal experimentation are resource-intensive, time-consuming, and ethically controversial. Therefore, the development of alternative non-animal testing approaches is crucial. This study proposes a novel hybrid graph transformer architecture, termed Hi-MGT, for the toxicity identification. An innovative aggregation strategy, referred to as GNN-GT combination, enables Hi-MGT to simultaneously and comprehensively aggregate local and global structural information of molecules, thus elucidating more informative toxicity information hidden in molecule graphs. The results show that the state-of-the-art model outperforms current baseline CML and DL models on a diverse range of toxicity endpoints and is even comparable to large-scale pretrained GNNs with geometry enhancement. Additionally, the impact of hyperparameters on model performance is investigated, and a systematic ablation study is conducted to demonstrate the effectiveness of the GNN-GT combination. Moreover, this study provides valuable insights into the learning process on molecules and proposes a novel similarity-based method for toxic site detection, which could potentially facilitate toxicity identification and analysis. Overall, the Hi-MGT model represents a significant advancement in the development of alternative non-animal testing approaches for toxicity identification, with promising implications for enhancing human safety in the use of chemical compounds.

Computational modeling of PET imaging agents for vesicular acetylcholine transporter (VAChT) protein binding affinity: application of 2D-QSAR modeling and molecular docking techniques

The neurotransmitter acetylcholine (ACh) plays a ubiquitous role in cognitive functions including learning and memory with widespread innervation in the cortex, subcortical structures, and the cerebellum. Cholinergic receptors, transporters, or enzymes associated with many neurodegenerative diseases, including Alzheimer's disease (AD) and Parkinson's disease (PD), are potential imaging targets. In the present study, we have developed 2D-quantitative structure-activity relationship (2D-QSAR) models for 19 positron emission tomography (PET) imaging agents targeted against presynaptic vesicular acetylcholine transporter (VAChT). VAChT assists in the transport of ACh into the presynaptic storage vesicles, and it becomes one of the main targets for the diagnosis of various neurodegenerative diseases. In our work, we aimed to understand the important structural features of the PET imaging agents required for their binding with VAChT. This was done by feature selection using a Genetic Algorithm followed by the Best Subset Selection method and developing a Partial Least Squares- based 2D-QSAR model using the best feature combination. The developed QSAR model showed significant statistical performance and reliability. Using the features selected in the 2D-QSAR analysis, we have also performed similarity-based chemical read-across predictions and obtained encouraging external validation statistics. Further, we have also performed molecular docking analysis to understand the molecular interactions occurring between the PET imaging agents and the VAChT receptor. The molecular docking results were correlated with the QSAR features for a better understanding of the molecular interactions. This research serves to fulfill the experimental data gap, highlighting the applicability of computational methods in the PET imaging agents' binding affinity prediction.

Graphical abstract

Supplementary Information

The online version contains supplementary material available at 10.1007/s40203-023-00146-4.

Computational modeling of aquatic toxicity of polychlorinated naphthalenes (PCNs) employing 2D-QSAR and chemical read-across

Polychlorinated naphthalenes (PCNs) are produced from a variety of industrial sources, and they reach the aquatic ecosystems by the dry-wet deposition from the atmosphere and also by the drainage from the land surfaces. Then the PCNs can be transmitted through the food chain to humans and show toxic effects on different aquatic animals as well as humans. Considering this scenario, it is an obligatory task to explore the toxicity data of PCNs more deeply for the species of an aquatic ecosystem (green algae-Daphnia magna-fish), and to extrapolate those data for humans. But the toxicity data for different aquatic species are quite limited. The laboratory experimentations are complicated and ethically troublesome to fill toxicity data gaps; therefore, different in silico methods (e.g., QSAR, quantitative read-across predictions) are emerging as crucial ways to fill the data gaps and hazard assessments. In the present study, we developed individual toxicity models as well as interspecies models from the 75 PCN toxicity data against three aquatic species (green algae-Daphnia magna-fish) by employing easily interpretable 2D descriptors; these models were validated rigorously employing different globally accepted internal and external validation metrics. Then we interpreted the modelled descriptors mechanistically with the endpoint values for better understanding. And finally, we endeavored to improve the prediction quality in terms of external validation metrics by employing a novel quantitative read-across approach by pooling the descriptors from the developed individual QSAR models.

On Some Novel Similarity-Based Functions Used in the ML-Based q-RASAR Approach for Efficient Quantitative Predictions of Selected Toxicity End Points

The novel quantitative read-across structure-activity relationship (q-RASAR) approach uses read-across-derived similarity functions in the quantitative structure-activity relationship (QSAR) modeling framework in a unique way for supervised model generation. The aim of this study is to explore how this workflow enhances the external (test set) prediction quality of conventional QSAR models by the incorporation of some novel similarity-based functions as additional descriptors using the same level of chemical information. To establish this, five different toxicity data sets, for which QSAR models were reported previously, have been considered in the q-RASAR modeling exercise, which uses chemical similarity-derived measures. The identical sets of chemical features along with the same compositions of training and test sets as reported previously were used in the present analysis for ease of comparison. The RASAR descriptors were calculated based on a chosen similarity measure with the default setting of relevant hyperparameter(s) and were then clubbed with the original structural and physicochemical descriptors, and the number of selected features was further optimized by employing a grid search technique applied on the respective training sets. These features were then used to develop multiple linear regression (MLR) q-RASAR models that show enhanced predictivity as compared to the QSAR models developed previously. Moreover, various other ML algorithms like support vector machine (SVM), linear SVM, random forest, partial least squares, and ridge regression were also employed using the same feature combinations as used in the MLR models to compare the prediction qualities. The q-RASAR models for five different data sets possess at least one of the RASAR descriptors, RA function, g_m, and average similarity, suggesting that these are important determinants of similarities that contribute to the development of predictive q-RASAR models, as also evident from the SHAP analysis of the models.

Efficient predictions of cytotoxicity of TiO2-based multi-component nanoparticles using a machine learning-based q-RASAR approach

The availability of experimental nanotoxicity data is in general limited which warrants both the use of in silico methods for data gap filling and exploring novel methods for effective modeling. Read-Across Structure-Activity Relationship (RASAR) is an emerging cheminformatic approach that combines the usefulness of a QSAR model and similarity-based Read-Across predictions. In this work, we have generated simple, interpretable, and transferable quantitative-RASAR (q-RASAR) models which can efficiently predict the cytotoxicity of TiO₂-based multi-component nanoparticles. A data set of 29 TiO₂-based nanoparticles with specific amounts of noble metal precursors was rationally divided into training and test sets, and the Read-Across-based predictions for the test set were generated. The optimized hyperparameters and the similarity approach, which yield the best predictions, were used to calculate the similarity and error-based RASAR descriptors. A data fusion of the RASAR descriptors with the chemical descriptors was done followed by the best subset feature selection. The final set of selected descriptors was used to develop the q-RASAR models, which were validated using the stringent OECD criteria. Finally, a random forest model was also developed with the selected descriptors, which could efficiently predict the cytotoxicity of TiO₂-based multi-component nanoparticles superseding previously reported models in the prediction quality thus showing the merits of the q-RASAR approach. To further evaluate the usefulness of the approach, we have applied the q-RASAR approach also to a second cytotoxicity data set of 34 heterogeneous TiO₂-based nanoparticles which further confirmed the enhancement of external prediction quality of QSAR models after incorporation of RASAR descriptors.

Application of Molecular Simulation Methods in Food Science: Status and Prospects

Molecular simulation methods, such as molecular docking, molecular dynamic (MD) simulation, and quantum chemical (QC) calculation, have become popular as characterization and/or virtual screening tools because they can visually display interaction details that in vitro experiments can not capture and quickly screen bioactive compounds from large databases with millions of molecules. Currently, interdisciplinary research has expanded molecular simulation technology from computer aided drug design (CADD) to food science. More food scientists are supporting their hypotheses/results with this technology. To understand better the use of molecular simulation methods, it is necessary to systematically summarize the latest applications and usage trends of molecular simulation methods in the research field of food science. However, this type of review article is rare. To bridge this gap, we have comprehensively summarized the principle, combination usage, and application of molecular simulation methods in food science. We also analyzed the limitations and future trends and offered valuable strategies with the latest technologies to help food scientists use molecular simulation methods.

A machine learning q-RASPR approach for efficient predictions of the specific surface area of perovskites

In this study, the specific surface area of various perovskites was modeled using a novel quantitative read-across structure-property relationship (q-RASPR) approach, which clubs both Read-Across (RA) and quantitative structure-property relationship (QSPR) together. After optimization of the hyper-parameters, certain similarity-based error measures for each query compound were obtained. Clubbing some of these error-based measures with the previously selected features along with the Read-Across prediction function, a number of machine learning models were developed using Partial Least Squares (PLS), Ridge Regression (RR), Linear Support Vector Regression (LSVR), Random Forest (RF) regression, Gradient Boost (GBoost), Adaptive Boosting (Adaboost), Multiple Layer Perceptron (MLP) regression and k-Nearest Neighbor (kNN) regression. Based on the repeated cross-validation as well as external prediction quality and interpretability, the PLS model (n_Training  = 38, n_Test  = 12, R T r a i n 2 ${{R}_{Train}^{2}}$ =0.737, Q L O O 2 = 0 . 637 , R T e s t 2 = 0 . 898 , Q F 1 T e s t 2 = 0 . 901 ) ${{Q}_{LOO}^{2}=0.637,\ {R}_{Test}^{2}=0.898,{\rm \ }\ {Q}_{F1\left(Test\right)}^{2}=0.901)}$ was selected as the best predictor which underscored the previously reported results. The finally selected model should efficiently predict specific surface areas of other perovskites for their use in photocatalysis. The new q-RASPR method also appears promising for the prediction of several other property endpoints of interest in materials science.

First report on soil ecotoxicity prediction against Folsomia candida using intelligent consensus predictions and chemical read-across

Soil invertebrates serve as an outstanding biological indicator of the terrestrial ecosystem and overall soil quality, considering their high sensitivity when compared to other indicators of soil quality. In this study, the available soil ecotoxicity data (pEC50) against the soil invertebrate Folsomia candida (C. name: Springtail) (n = 45) were collated from the database of ECOTOX (cfpub.epa.gov/ecotox) and subjected to QSAR analysis using 2D descriptors. Four partial least squares (PLS) models were built based on the features selected through genertic algorithm followed by the best subset selection. These four models were then used as inputs for Intelligent Consensus Predictor version 1.2 (PLS version) to get the final consensus predictions, using the best selection of predictions (compound-wise) from four "qualified" individual models. Both internal and external validations metrics of the consensus predictions are well- balanced and within the acceptable range as per the OECD criteria. The consensus model was found to be better than the previous developed models for this endpoint. Predictions were also made using the Chemical Read-across approach, which showed even better external validation metric values than the consensus predictions. From the selected features in the QSAR models, it has been found out that molecular weight and presence of a di-thiophosphate group, electron donor groups, and polyhalogen substitutions have a significant impact on the soil ecotoxicity. The soil ecotoxicological risk assessment of organic chemicals can therefore be prioritized by these features. The models developed from diverse structural organic compounds can be applied to any new query compound for data gap filling.

Quick and efficient quantitative predictions of androgen receptor binding affinity for screening Endocrine Disruptor Chemicals using 2D-QSAR and Chemical Read-Across

Endocrine Disruptor Chemicals are synthetic or natural molecules in the environment that promote adverse modifications of endogenous hormone regulation in humans and/or in animals. In the present research, we have applied two-dimensional quantitative structure-activity relationship (2D-QSAR) modeling to analyze the structural features of these chemicals responsible for binding to the androgen receptors (logRBA) in rats. We have collected the receptor binding data from the EDKB database (https://www.fda.gov/science-research/endocrine-disruptor-knowledge-base/accessing-edkb-database) and then employed the DTC-QSAR tool, available from https://dtclab.webs.com/software-tools, for dataset division, feature selection, and model development. The final partial least squares model was evaluated using various stringent validation criteria. From the model, we interpreted that hydrophobicity, steroidal nucleus, bulkiness and a hydrogen bond donor at an appropriate position contribute to the receptor binding affinity, while presence of electron rich features like aromaticity and polar groups decrease the receptor binding affinity. Additionally we have also performed chemical Read-Across predictions using Read-Across-v3.1 available from https://sites.google.com/jadavpuruniversity.in/dtc-lab-software/home, and the results for the external validation metrics were found to be better than the QSAR-derived predictions. The best quality of external predictions emerged from the q-RASAR approach which combines both read-across and QSAR. To explore the essential features responsible for the receptor binding, pharmacophore mapping, molecular docking along with molecular dynamics simulation were also performed, and the results are in accordance with the QSAR/q-RASAR findings.

Intelligent consensus predictions of bioconcentration factor of pharmaceuticals using 2D and fragment-based descriptors

Bioconcentration factors (BCFs) are markers of chemical substance accumulation in organisms, and they play a significant role in determining the environmental risk of various chemicals. Experiments to obtain BCFs are expensive and time-consuming; therefore, it is better to estimate BCF early in the chemical development process. The current research aims to evaluate the ecotoxicity potential of 122 pharmaceuticals and identify possible important structural attributes using BCF as the determining feature against a group of fish species. We have calculated the theoretical 2D descriptors from the OCHEM platform and SiRMS descriptor calculating software. The regression-based quantitative structure-property relationship (QSPR) modeling was used to identify the chemical features responsible for acute fish bioconcentration. Multiple models with the "intelligent consensus" algorithm were employed for the regression-based approach improving the predictive ability of the models. To ensure the robustness and interpretability of the developed models, rigorous validation was performed employing various statistical internal and external validation metrics. From the developed models, it can be specified that the presence of large lipophilic and electronegative moieties greatly enhances the bioaccumulative potential of pharmaceuticals, whereas the hydrophilic characteristics have shown a negative impact on BCF. Furthermore, the developed models were employed to screen the DrugBank database (https://go.drugbank.com/) for assessing the BCF properties of the entire database. The evidence acquired from the modeled descriptors might be used for aquatic risk assessment in the future, with the added benefit of providing an early caution of their probable negative impact on aquatic ecosystems for regulatory purposes.

Chemical similarity and machine learning-based approaches for the prediction of aquatic toxicity of binary and multicomponent pharmaceutical and pesticide mixtures against Aliivibrio fischeri

Different classes of chemicals are present in the environment as mixtures. Among them, pharmaceuticals and pesticides are of major concern due to their improper use and disposal, and subsequent additive and non-additive effects. To assess the environmental risk posed by the mixtures of pharmaceuticals and pesticides, a quantitative structure-activity relationship (QSAR) model has been developed in this study using the pEC₅₀ values of 198 binary and multi-component mixtures against the marine bacterium Aliivibrio fischeri. The developed partial least squares (PLS) model has been rigorously validated and proved to be a robust and extremely predictive one. To address the chances of overestimation of validation metrics, three cross-validation tests (mixtures out, compounds out, and everything out) have been applied, and the results were satisfactory. The use of simple 2-dimensional descriptors makes the prediction much quick, and also makes the model easily interpretable. A machine learning-based chemical read-across prediction has also been performed to justify the effectiveness of selected structural features in this study. In a nutshell, this study proves QSAR and chemical read-across as effective alternative approaches for the toxicity prediction of pharmaceutical and pesticide mixtures and also approves the use of mixture descriptors for modelling mixtures successfully.

Nano-read-across predictions of toxicity of metal oxide engineered nanoparticles (MeOx ENPS) used in nanopesticides to BEAS-2B and RAW 264.7 cells

The demand for nutrients and new technologies has increased with population growth. The agro-technological revolution with metal oxide engineered nanoparticles (MeOx ENPs) has the potential to reform the resilient agricultural system while maintaining the security of food. When utilized extensively, MeOx ENPs may have unintended toxicological effects on both target and non-targeted species. Since limited information about nanopesticides' pernicious effects is available, in silico modeling can be done to explore these issues. Hence, in the present work, we have applied computational modeling to explore the influence of metal oxide nanoparticles on the toxicity of bronchial epithelial (BEAS-2B) and murine myeloid (RAW 264.7) cells to bridge the data gap relating to the toxicity of MeOx NPs. Initially, partial least squares (PLS) regression models were developed applying the Small Dataset Modeler software (http://teqip.jdvu.ac.in/QSAR_Tools/DTCLab/) using four datasets having effective concentration (EC₅₀%) as the endpoints and employing only periodic table descriptors. To further explore the predictions, we applied a read-across approach using the descriptors selected in the QSAR models. Also, the inter-endpoint cytotoxicity relationship modeling (quantitative toxicity-toxicity relationship or QTTR) was conducted. It was found that the result obtained by nano-read-across provided a similar level of accuracy as provided by QSAR. The information derived from the PLS models of both the cell lines suggested that metal cation formation, and bond-forming capacity influence the toxicity whereas the presence of metal has an influential impact on the ecotoxicological effects. Thus, it is feasible to design safe nanopesticides that could be more effective than conventional analogs.