Quantitative Structure-activity Relationship Methods Research Articles

Hybrid 2D/3D-quantitative structure–activity relationship studies on the bioactivities and molecular mechanism of antibacterial peptides

Antimicrobial peptide (AMP) is the polypeptide, which protects the organism avoiding attack from pathogenic bacteria. Studies have shown that there were some antimicrobial peptides with molecular action mechanism involved in crossing the cell membrane without inducing severe membrane collapse, then interacting with cytoplasmic target-nucleic acid, and exerting antibacterial activity by interfacing the transmission of genetic information of pathogenic microorganisms. However, the relationship between the antibacterial activities and peptide structures was still unclear. Therefore, in the present work, a series of AMPs with a sequence of 20 amino acids was extracted from DBAASP database, then, quantitative structure–activity relationship (QSAR) methods were conducted on these peptides. In addition, novel antimicrobial peptides with  stronger antimicrobial activities were designed according to the information originated from the constructed models. Hence, the outcome of this study would lay a solid foundation for the in-silico design and exploration of novel antibacterial peptides with improved activity activities.

3D and 2D-QSAR Studies on Natural Flavonoids for Nitric Oxide Production Inhibitory Activity

Background: Nitric oxide (NO), an important second messenger molecule, regulates numerous physiological responses, while excessive NO generates negative effects on the circulatory, nervous and immune systems. Recently, some natural flavonoids were reported to possess the capability of inhibiting LPS-induced NO production. To fully understand the nature of their own NO inhibitory activity, it is necessary to address the structural requirements of flavonoids as NO inhibitors. Objective: The objective of this work was to develop efficient QSAR models for predicting the NOinhibitory activity of new flavonoids and improving insights into the critical properties of the chemical structures that were required for the ideal NO production inhibitory activities. Methods: To provide insights into the structural basis of flavonoids as NO inhibitors, 3D quantitative structure-activity relationship (3D-QSAR) and 2D-QSAR models were developed on a dataset of 55 flavonoids using comparative molecular field analysis (CoMFA), comparative molecular similarity indices analysis (CoMSIA) and hologram quantitative structure-activity relationship (HQSAR) approaches. method: CoMFA, CoMSIA combining HQSAR methods were employed on a series of flavonoids to generate 3D and 2D-QSAR models. Results: The statistically significant models for CoMFA, CoMSIA and HQSAR resulted in crossvalidated coefficient (q2) values of 0.523, 0.572 and 0.639, non-cross-validated coefficient (r2) values of 0.793, 0.828 and 0.852, respectively. The robustness of these models was further affirmed using a test set of 18 compounds, which resulted in predictive correlation coefficients (r2 pred) of 0.968, 0.954 and 0.906. Furthermore, the models-derived contour maps were appraised for activity trends for the molecules analyzed. result: Comparative Molecular Field Analysis (CoMFA), Comparative Molecular Similarity Indices Analysis (CoMSIA) combining hologram quantitative structure-activity relationship (HQSAR) methods were employed on a series of flavonoids to generate 3D and 2D-QSAR models. Result: The obtained models can be used to predict the activities of new flavonoids and identify the key structural features affecting the NO inhibitory activities. Conclusion: The 3D and 2D-QSAR models constructed in this paper were efficient in estimating the NO inhibitory activities of flavonoids and facilitating the design of flavonoid-derived NO production inhibitors. other: none

Metamorphosis of human health risk assessment with artificial intelligence (AI) - a new paradigm in pharmaco-toxicological sciences

Toxicological Science, especially in the last five decades, has witnessed rapid evolution of different tools and techniques developed to address diverse issues related to studies dealing with adverse health effects of a variety of poisons, drugs, chemicals,ever-growing list of xenobiotics and human diseases. Traditionally these studies are performed using suitable animal (in vivo) models. There was a time when toxicologists/pharmacologists were searching models alternate to animal toxicity testing (Doke and Dhawale, 2015). Improved cell culture techniques, knowledge on stem cells and other microbiological systems led to the development of in vitro toxicology. It was soon followed by DNA chips, micro fluidics, in silico toxicology , toxicogenomics and computational toxicology. Several platforms are now discussing machine learning (ML) and artificial intelligence (AI) together as future tools of computational toxicology. For decades, quantitative structure-activity relationship (QSAR) methods have been employed to study the effects of drugs/chemicals (Cai et al., 2022). However, AI methods for toxicity assessment ranging from ADMEtox to AI4TOX provide evidence to the immense potential of AI. Intriguingly, a few problems between theoretical developments and practice of AI by end users have been recognized. AI is now being employed in cancer care. According to WHO (2022), cancer is responsible for 9.3 million deaths per year. AI is being used for cancer grading, classification, follow up services and diagnostic accuracy. However, certain limitations viz. testing, validation, certification and auditing need to be addressed (Cabral et al., (2023). Potential of AI in diabetic care and management has recently been recognized. The huge burden of diabetic patients in India can be managed through AI tools. Diabetic risk can be predicted using genomic data, to diagnose diabetes using EHR data and to identify diabetes related complications i.e. retinopathy and nephropathy (Singhla et al., 2019). Application of AI in the management of cardiovascular diseases like myocardial infarction has been highlighted with special reference to Chinese medicine (Chen et al., 2022). There exists experimental evidence that AI tools can be used to assess, monitor and manage Parkinsons' disease (Bounsall et al., 2023). Perspectives of the application of AI in complimentary and alternative medicine were reviewed by Chu et al. (2022). Several regulatory agencies are now adopting the concept of 3R ie., replacement, reduction and refinement of animal testing (EU REACH/3R principles; Toxicology 21 of U.S. Government) ( Maestri, 2021).The application of AI in clinical toxicology through converging data resources, algorithms, real world information from sensors and health records has also been discussed (Sinha et al. 2021). Plausibility of toxicity prediction using AI tools was recently reviewed by Santin et al. (2021). Application of AI in recently emerged science of nanotoxicology is also being sought. The need for nanotoxicity databases, powerful nano descriptors, new modeling approaches, molecular mechanism analyses and designing of next generation nanomaterials are being debated ( Jha et al., 2014; Yan et al., 2023). U.S. Food and Drug Administration (USFDA) has recently initiated and AI program in Toxicology known as AI4TOX. This program mainly consists of four initiatives- AnimalGAN- to predict animal toxicology data for untested chemicals ( /about –fda/nctr-research-focus-areas/animalgan-initiative); SafetAI- to develop novel deep learning methods for toxicological endpoints (/about-fda/nctr-research-focus-areas/safetai-initiative); BERTox- to develop the most advanced AI powered Natural Language Processing (NPL) (/about-fda/nctr-research-focus-areas/bertox initiative) and PathologAI- to develop an effective and accurate framework for analysis of histopathological data from animal studies (/ about-fda/nctr-research-focus-areas/pathologai-initiative). Recently, Society of Toxicology (SOT) annual meeting held at Nashville from March 19-23, focused on a question-“How could AI be used for risk assessment?” There exists some skepticism weather AI may be used in human health risk assessment? How AI could be applied -to prioritize pharmaceutical/environmental chemicals, to identify potential off targets and decipher the mechanisms of toxicity and detect pathological effects? A Symposium session devoted to AI summarized international collaborative computational projects like CERaPP, COmPara and CATOMOS that have been designed to streamline the regulatory and safety assessments (Hartung, 2023). High throughput screening data (HTS) to predict drug induced liver injury (DILI) using AI is also being generated. AI can identify mechanisms for off target effects in drug development. AI can also be plausibly used to predict genotoxicity. Can AI tools automate the analysis of developmental or physiologically based assays? These discussions held during the symposium indicate exciting potential of AI in health risk assessment. It is speculated that, tools of nanotechnology hybridized with AI can metamorphose human health risk assessment to an extent that has never been achieved before.

Machine learning-based q-RASAR approach for the in silico identification of novel multi-target inhibitors against Alzheimer's disease

In the present research, we present the application of a novel approach, termed the Machine Learning (ML)-Based q-RASAR (quantitative read-across structure-activity relationship) method, for the identification of potential multi-target inhibitors against Alzheimer's disease (AD). The q-RASAR effectively combines the principles of both read-across and 2D QSAR approaches. As a result, it is imperative to take into account similarity-related aspects in the process of developing q-RASAR models. In this investigation, we have implemented ML-based q-RASAR modeling against seven major targets (AChE, BuChE, BACE1, 5-HT6, CDK-5 enzymes, Amyloid precursor protein, and Tau aggregation) of AD using the initially selected features in 2D-QSAR models for the identifications of novel multitarget inhibitors. The models were individually used to check the applicability domain of a pool of 407270 natural products (NPs) obtained from the COCONUT database (https://coconut.naturalproducts.net/download) and provided prioritized compounds for experimental detection of their performance as anti-Alzheimer's drugs. Furthermore, we have also developed the q-RASAAR (quantitative read-across structure-activity-activity relationship) and selectivity-based q-RASAR models to explore the most important features contributing to the dual inhibition against the respective targets. Furthermore, we have applied seven distinct machine learning algorithms to check their influence on the predictive abilities of q-RASAR and q-RASAAR models. Moreover, we have also developed the univariate q-RASAR model, with the RA function as the primary independent variable. Additionally, molecular docking experiments have been conducted to gain insights into the atomic-level molecular interactions between ligands and enzymes. These observations are then juxtaposed with the structural characteristics obtained from models that elucidate the mechanistic aspects of binding events. These proposed models may serve as valuable tools for pinpointing crucial molecular attributes when designing potential drugs for Alzheimer's therapy through the rational design of multi-target inhibitors.

Quantifying the Benefits of Imputation over QSAR Methods in Toxicology Data Modeling.

Imputation machine learning (ML) surpasses traditional approaches in modeling toxicity data. The method was tested on an open-source data set comprising approximately 2500 ingredients with limited in vitro and in vivo data obtained from the OECD QSAR Toolbox. By leveraging the relationships between different toxicological end points, imputation extracts more valuable information from each data point compared to well-established single end point methods, such as ML-based Quantitative Structure Activity Relationship (QSAR) approaches, providing a final improvement of up to around 0.2 in the coefficient of determination. A significant aspect of this methodology is its resilience to the inclusion of extraneous chemical or experimental data. While additional data typically introduces a considerable level of noise and can hinder performance of single end point QSAR modeling, imputation models remain unaffected. This implies a reduction in the need for laborious manual preprocessing tasks such as feature selection, thereby making data preparation for ML analysis more efficient. This successful test, conducted on open-source data, validates the efficacy of imputation approaches in toxicity data analysis. This work opens the way for applying similar methods to other types of sparse toxicological data matrices, and so we discuss the development of regulatory authority guidelines to accept imputation models, a key aspect for the wider adoption of these methods.

Construction of novel dual Z-scheme g-C3N4/ZnFe2O4/Ag2CO3 heterojunction with enhanced visible-light-driven performance for tetracycline degradation and bacterial inactivation

Dual Z-scheme heterojunction with highly separation efficiency of photoinduced carriers and outstanding redox capability has received considerable attention and hold great promise for environmental remediation. Herein, magnetic dual Z-scheme g-C3N4/ZnFe2O4/Ag2CO3 heterojunction was successfully constructed, and applied as a dual-function photocatalyst for tetracycline (TC) degradation and bacterial inactivation. The optimized g-C3N4/ZnFe2O4/Ag2CO3 heterojunction exhibited excellent photocatalytic capacity for TC degradation with a rate constant of 0.0154 min−1, which is 2.73, 3.08, and 2.92 times higher compared to that of the g-C3N4, ZnFe2O4, and g-C3N4/ZnFe2O4, respectively. Impressively, the g-C3N4/ZnFe2O4/Ag2CO3-1.25 heterojunction displayed satisfying activity within a wider range of pH (3.0–9.0), TC concentration (5–25 mg L−1), and with the addition of various anions including Cl-, HCO3-, SO42-, and NO3-. Meanwhile, 6.5-log of Escherichia coli cells could also be inactivated by g-C3N4/ZnFe2O4/Ag2CO3-1.25 heterojunction under visible light. Mechanism exploration based on trapping experiment, electron paramagnetic resonance (EPR) analysis, and liquid chromatography-mass spectrometer (LC-MS) indicated that the improved photocatalytic performance resulted from the synergetic effect of semiconductors and constructed dual Z-scheme heterojunction, which significantly promotes charge separation rate and endows strong redox capacity. As expected, the toxicity of TC degradation intermediates was reduced according to the Toxicity estimation analysis based on a quantitative structure activity relationships (QSAR) method. Further, the g-C3N4/ZnFe2O4/Ag2CO3 heterojunction also demonstrated magnetic separation property and satisfactory photocatalytic stability even after 4 cycles. This work offers a new perspective on constructing novel magnetic dual Z-scheme heterojunction with dual-function photocatalytic properties and is of significance for water remediation.

Environmentally friendly PAEs alternatives with desired synthesizability by in silico methods

Phthalate esters (PAEs) as plasticizers posed significant environmental and human health risks during the production and application processes. This study aims to investigate the synthesizability of previously designed environmentally friendly PAEs alternatives by in silico approaches and explore the key factors influencing their synthesizability. Firstly, a novel approach combining positive and unlabeled (PU) machine learning (ML) and k-Nearest Neighbor (kNN) models was utilized to screen three environmentally friendly PAEs alternatives with high synthesizability. Subsequently, the synthesizability and environmental friendliness of the selected PAEs alternatives were evaluated and confirmed using ADMETlab 2.0. Results demonstrated that all three PAEs alternatives exhibited high synthesizability and environmental friendliness. Finally, the SHapley Additive exPlanation (SHAP) method was employed to analyze the synthesis mechanisms of PAEs alternatives, revealing that the VE2_DzZ (molecular substitutability) and maxHCsats (molecular structure) could influence their synthesizability. Based on these findings, the synthesizability of the three PAEs alternatives was validated using density functional theory (DFT) and three-dimensional quantitative structure-activity relationship (3D-QSAR) methods. This cost-effective study provided the first evaluation of the synthesizability of PAEs alternatives and reduced the number of unlabeled samples by 95.83%. Moreover, these findings offered a theoretical foundation for developing environmentally friendly PAEs alternatives and provided new insights for substituting emerging pollutants and developing new materials.

QSAR modeling using the Gaussian process applied for a series of flavonoids as potential antioxidants

Flavonoids have been the subject of several studies for many years, particularly due to their high antioxidant activity. However, understanding the structure–activity relationships (SAR) of flavonoids is crucial for optimizing their properties and designing new derivatives with enhanced activities. In this study, we employed Quantitative Structure-Activity Relationship (QSAR) methods to analyze a group of 31 flavonoids with known biological activity. The Gaussian program was used to calculate the molecular descriptors. Using statistical modeling techniques, such as multiple linear regression, we developed QSAR models to correlate the molecular descriptors with the activity values. The models were rigorously validated using appropriate procedures to ensure their reliability and predictive power with a correlation coefficient R2pred = 0.86, and an absolute average relative error (AARE pred) of 0.06 for the test set.

The importance of good practices and false hits for QSAR-driven virtual screening real application: a SARS-CoV-2 main protease (Mpro) case study

Computer-Aided Drug Design (CADD) approaches, such as those employing quantitative structure-activity relationship (QSAR) methods, are known for their ability to uncover novel data from large databases. These approaches can help alleviate the lack of biological and chemical data, but some predictions do not generate sufficient positive information to be useful for biological screenings. QSAR models are often employed to explain biological data of chemicals and to design new chemicals based on their predictions. In this review, we discuss the importance of data set size with a focus on false hits for QSAR approaches. We assess the challenges and reliability of an initial in silico strategy for the virtual screening of bioactive molecules. Lastly, we present a case study reporting a combination approach of hologram-based quantitative structure-activity relationship (HQSAR) models and random forest-based QSAR (RF-QSAR), based on the 3D structures of 25 synthetic SARS-CoV-2 Mpro inhibitors, to virtually screen new compounds for potential inhibitors of enzyme activity. In this study, optimal models were selected and employed to predict Mpro inhibitors from the database Brazilian Compound Library (BraCoLi). Twenty-four compounds were then assessed against SARS-CoV-2 Mpro at 10 µM. At the time of this study (March 2021), the availability of varied and different Mpro inhibitors that were reported definitely affected the reliability of our work. Since no hits were obtained, the data set size, parameters employed, external validations, as well as the applicability domain (AD) could be considered regarding false hits data contribution, aiming to enhance the design and discovery of new bioactive molecules.

Potencial of Aloin B Compound and its Derivatives as Type-2 Antidiabetic

Type-2 diabetes mellitus occurs due to suboptimal insulin function (insulin resistance) or decreased insulin function. Type-2 diabetes mellitus treatment is chronic and lifelong. One of the treatments is the use of insulin and oral anti-diabetic drugs. This treatment requires a long period of time and can cause unwanted side effects. Therefore, alternative treatments are needed with minimal side effects by utilizing herbal plants containing Aloin B compounds because they have been proven to be used as antidiabetic agents. These compounds can be found from the Aloe Vera plant (Aloe Vera l.). The aim of this study was to find compounds derived from Aloin B compounds that have the most potential as anti-diabetic type-2 by inhibiting the pancreatic α-amylase enzyme (code: 1B2Y) in breaking down starch in the body. The certainty of the presence of the compound Aloin B in the flesh of the aloe plant was confirmed by the LC-MS test. This research was conducted using the Quantitative Structure-Activity Relationship (QSAR) and Molecular Bonding method. The results showed that the ID S22 compound with the IUPAC name (S)-10-amino-1,2,8-trihydroxy-6- (hydroxymethyl) -10- ((2R,3R,4S,5S,6R) -2,3, 4,5-tetrahydroxy–6(hydroxymethyl) tetrahydro-2H-piran-2-yl) anthracene-9(10H)-one is the most potent compound from Aloin B derivatives as a type-2 antidiabetic agent in the mechanism of inhibiting α-enzyme action pancreatic amylase, based on the value of R2 = 0.980, the PRESS value of the compound was 0.0004, the binding energy value was -7.07 kcal/mol, the inhibition constant was 6.58 uM and the formation of hydrogen bonds between the compound and the amino acid residues aspirin, glycine, threonine and arginine.

Automated machine learning approach for developing a quantitative structure-activity relationship model for cardiac steroid inhibition of Na+/K+-ATPase.

Quantitative structure-activity relationship (QSAR) modeling is a method of characterizing the relationship between chemical structures and biological activity. Automated machine learning enables computers to learn from large datasets and can be used for chemoinformatics. Cardiac steroids (CSs) inhibit the activity of Na+/K+-ATPase (NKA) in several species, including humans, since the binding pocket in which NKA binds to CSs is highly conserved. CSs are used to treat heart disease and have been developed into anticancer drugs for use in clinical trials. Novel CSs are, therefore, frequently synthesized and their activities evaluated. The purpose of this study is to develop a QSAR model via automated machine learning to predict the potential inhibitory activity of compounds without performing experiments. The chemical structures and inhibitory activities of 215 CS derivatives were obtained from the scientific literature. Predictive QSAR models were constructed using molecular descriptors, fingerprints, and biological activities. The best predictive QSAR models were selected based on the LogLoss value. Using these models, the Matthews correlation coefficient, F1 score, and area under the curve of the test dataset were 0.6729, 0.8813, and 0.8812, respectively. Next, we showed automated construction of the predictive models for CS derivatives, which may be useful for identifying novel CSs suitable for candidate drug development. The automated machine learning-based QSAR method developed here should be applicable for the time-efficient construction of predictive models using only a small number of compounds.

Deep electron cloud‐activity and field‐activity relationships

AbstractChemists have been pursuing general mathematical laws to explain and predict molecular properties for a long time. However, most of the traditional quantitative structure‐activity relationship (QSAR) models have limited application domains; for example, they tend to have poor generalization performance when applied to molecules with parent structures different from those of the trained molecules. This paper attempts to develop a new QSAR method that is theoretically possible to predict various properties of molecules with diverse structures. The proposed deep electron cloud‐activity relationships (DECAR) and deep field‐activity relationships (DFAR) methods consist of three essentials: (1) a large number of molecule entities with activity data as training objects and responses; (2) three‐dimensional electron cloud density (ECD) or related field data by the accurate density functional theory methods as input descriptors; and (3) a deep learning model that is sufficiently flexible and powerful to learn the large data described above. DECAR and DFAR are used to distinguish 977 sweet and 1965 non‐sweet molecules (with 6‐fold data augmentation), and the classification performance is demonstrated to be significantly better than the traditional least squares support vector machine (LS‐SVM) models using traditional descriptors. DECAR and DFAR would provide a possible way to establish a widely applicable, cumulative, and shareable artificial intelligence‐driven QSAR system. They are likely to promote the development of an interactive platform to collect and share the accurate ECD and field data of millions of molecules with annotated activities. With enough input data, we envision the appearance of several deep networks trained for various molecular activities. Finally, we could anticipate a single DECAR or DFAR network to learn and infer various properties of interest for chemical molecules, which will become an open and shared learning and inference tool for chemists.

Mechanism analysis of synthetic musks-induced miscarriage risk in pregnant women: Molecular and adverse outcome pathways perspectives

Synthetic musks (SMs) with pleasant odor have been extensively used in personal care products. SMs are emerging contaminants due to the wide detection in human breast milk and blood, and even caused miscarriage to pregnant women. Aiming at this, the two-dimensional quantitative structure-activity relationship (2D-QSAR) coupling with three-dimensional quantitative structure-activity relationship (3D-QSAR) methods were first used to analyze the SM-induced risk of miscarriage in pregnant women, and the above two models were used to mutually authenticate each other. It was found that the critical volume, melting point and heat of formation of SMs were the key descriptors leading to miscarriage. In addition, the adverse outcome pathway (AOP) was used to assess the miscarriage risk of selected SMs and the environment-friendly SM derivatives with desired functional properties designed in previous studies. Results showed that the miscarriage risk of musk ketone derivative (M27) was reduced by approximately three times than that of MK. The miscarriage risk of galaxolide derivatives (i.e., D6 and D24) were about six times lower than that of galaxolide, which further confirmed that the theoretically designed SMs derivatives can effectively reduce the risk of miscarriage in pregnant women. This study provides a comprehensive analysis of the mechanism of SM-induced miscarriage, and provides an accurate and rapid theoretical evaluation method for the future risk assessment of SM-induced miscarriage.

Molecular Modeling, Docking, and QSAR Studies on A Series of N-arylsulfonyl-N-2-pyridinyl-piperazines Analogs Acting as Anti-Diabetic Agents

The Molecular structure of compounds contains a lot of information that can be used further. A classical Quantitative Structure Activity Relationship (QSAR) method was used to decode that information based on the descriptors. The study was performed on a mono-substituted series of Glucokinase-Glucokinase regulatory protein inhibitors (GK-GKRP/GCKR). A sequential application of the statistical method, both linear and nonlinear, has been used in the study which includes Multiple Linear Regression (MLR), Partial Least Square (PLS), and Artificial Neural Network (ANN). The developed model was validated using various statistical methods to evidently prove its reliability and precision. This knowledge will be used to design a new compound. Docking studies will be performed to establish the binding pattern of the designed compound. The prophetic power and robustness of the model containing 26 compounds in the training set were proven by the various statistical parameter s value: 0.30, F-value: 41.8, r: 0.94, r2: 0.88, r2CV: 0.77. The model gives insight into the various descriptors that are selected for the present study. The present study not only shows the contribution of various substituents in the biological activity but also indicates the changes that can be done to design the new potent molecules with more selectivity and less toxicity. HIGHLIGHTS The enzyme glucokinase (GCK) is responsible for maintaining the body's normal glucose homeostasis. Hypertriglyceridemia, hyperinsulinemia, and T2D are all triggered by GCK dysfunction or dysregulation An inhibitor of the glucokinase enzyme (GCK), which is only present in hepatocytes and is in charge of glucose metabolism, is encoded by the glucokinase regulator (GCKR) gene The designed model gives insight into the various descriptors that are selected for the present study. The present study not only shows the contribution of various substituents in the biological activity but also indicates the changes that can be done to design the new potent molecules with more selectivity and less toxicity Small compounds have been discovered that specifically bind to GKRP and lower blood sugar levels GRAPHICAL ABSTRACT

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 (MAEtest = 0.4219) than ECOSAR model (MAEtest = 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 (MAEtest = 0.3954) had a significantly higher prediction accuracy for test compounds than SM1, and also outperformed the T.E.S.T. consensus model (MAEtest = 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.

Metal-Free Activation of Sulfite by Benzoquinone-Derived Carbon for Efficient Organic Contaminant Degradation: Identification and Regulation of Active Sites

Sulfite [S(IV)]-based advanced oxidation processes (AOPs) driven by carbon materials provide an environmentally friendly, economical route for environmental remediation. However, the active sites and involved radical mechanisms are still controversial due to the complexity of the carbon structure, thus greatly hampering the development of highly efficient systems. Herein, a benzoquinone-derived carbon (BQC)/S(IV) system was developed for organic pollutant degradation in an acidic medium for the first time. The quinone groups and conductivity were confirmed as the key factors for S(IV) activation through cyclic voltammetry, antitheses, specific site masking, and quantitative structure–activity relationship methods. Benefiting from the high content of quinone groups and excellent conductivity, the BQC catalyst exhibited much more effective in activating Na2SO3 for organic pollutant degradation compared to most traditional carbon or even metal catalysts. The chronopotentiometry and in situ Raman results revealed that the formation of C–S(IV)* complexes was the primary step to produce SO3·–, followed by the production of SO4·– via a chain reaction, in which the SO4·– radical was responsible for pollutant degradation. The findings may provide novel insights into reaction mechanisms in S(IV)-AOP systems and pave the way toward highly efficient carbon catalysts to activate S(IV) for the elimination of organic contaminants.

Dimensional Reduction of QSAR Features Using a Machine Learning Approach on the SARS-Cov-2 Inhibitor Database

Quantitative Structure-Activity Relationship (QSAR) is a method that relates the chemical composition of a molecule to its biochemical, pharmaceutical and biological activities. The characteristics of a molecule's chemical constituents, such as chemical descriptors and fingerprints, are necessary to create a good QSAR model. Dimensionality reduction can alleviate the issue of several unnecessary and redundant chemical descriptors and chemical fingerprints in a high-dimensional feature-number data set by shrinking the high-dimensional original space to a low-dimensional intrinsic space. There are two categories of dimensional reduction techniques: feature extraction and feature selection. The dimension reduction approach can be utilized as a starting step in running a QSAR Virtual Screening Model on a dataset of SARS-CoV-2 inhibitor medications to create novel treatments for Covid-19 cases based on machine learning (ML) and the idea of medicinal repurposing. Fe extraction and feature selection are crucial to determining which feature sets should be applied to a specific classification process in QSAR modeling to produce reliable virtual screening results. The SARS-Cov-2 inhibitor drug database's chemical descriptor and chemical fingerprint were extracted using a simple, quick, and accurate method in this work. The total number of selected features is 12122 features. PCA, Missing values, and Random Forest are the techniques employed. The Xgboost Tree Ensemble, Naive Bayes, Support Vector Machine, Random Forest, and Deep Learning (Artificial Neural Network/Multilayer Perceptron) were used to classify the QSAR modeling on the training and test data. The Random Forest approach, when applied to all chemical descriptors and chemical fingerprint features, along with the XGBoost algorithm, yields the best feature selection results (accuracy value of 0.845 and AUC of 0.904). There are 233 characteristics for the regression QSAR approach and 273 features for the feature selection-based QSAR method of classification. Next, virtual screening of QSAR modeling of prospective drugs for Covid-19 therapy can be done utilizing the outcomes of the characteristics that have been chosen using the Random Forest approach

From Molecular Descriptors to Intrinsic Fish Toxicity of Chemicals: An Alternative Approach to Chemical Prioritization.

The European and U.S. chemical agencies have listed approximately 800k chemicals about which knowledge of potential risks to human health and the environment is lacking. Filling these data gaps experimentally is impossible, so in silico approaches and prediction are essential. Many existing models are however limited by assumptions (e.g., linearity and continuity) and small training sets. In this study, we present a supervised direct classification model that connects molecular descriptors to toxicity. Categories can be driven by either data (using k-means clustering) or defined by regulation. This was tested via 907 experimentally defined 96 h LC50 values for acute fish toxicity. Our classification model explained ≈90% of the variance in our data for the training set and ≈80% for the test set. This strategy gave a 5-fold decrease in the frequency of incorrect categorization compared to a quantitative structure-activity relationship (QSAR) regression model. Our model was subsequently employed to predict the toxicity categories of ≈32k chemicals. A comparison between the model-based applicability domain (AD) and the training set AD was performed, suggesting that the training set-based AD is a more adequate way to avoid extrapolation when using such models. The better performance of our direct classification model compared to that of QSAR methods makes this approach a viable tool for assessing the hazards and risks of chemicals.

Deep Learning and Antibiotic Resistance.

Antibiotic resistance (AR) is a naturally occurring phenomenon with the capacity to render useless all known antibiotics in the fight against bacterial infections. Although bacterial resistance appeared before any human life form, this process has accelerated in the past years. Important causes of AR in modern times could be the over-prescription of antibiotics, the presence of faulty infection-prevention strategies, pollution in overcrowded areas, or the use of antibiotics in agriculture and farming, together with a decreased interest from the pharmaceutical industry in researching and testing new antibiotics. The last cause is primarily due to the high costs of developing antibiotics. The aim of the present review is to highlight the techniques that are being developed for the identification of new antibiotics to assist this lengthy process, using artificial intelligence (AI). AI can shorten the preclinical phase by rapidly generating many substances based on algorithms created by machine learning (ML) through techniques such as neural networks (NN) or deep learning (DL). Recently, a text mining system that incorporates DL algorithms was used to help and speed up the data curation process. Moreover, new and old methods are being used to identify new antibiotics, such as the combination of quantitative structure-activity relationship (QSAR) methods with ML or Raman spectroscopy and MALDI-TOF MS combined with NN, offering faster and easier interpretation of results. Thus, AI techniques are important additional tools for researchers and clinicians in the race for new methods of overcoming bacterial resistance.

Identifying Sequential Residue Patterns in Bitter and Umami Peptides

The primary structures of peptides, originating from food proteins, affect their taste. Connecting primary structure to taste, however, is difficult because the size of the peptide sequence space increases exponentially with increasing peptide length, while experimentally-labeled data on peptides' tastes remain scarce. We propose a method that coarse-grains the sequence space to reduce its size and systematically identifies the most common coarse-grained residue patterns found in known bitter and umami peptides. We select the optimal patterns by performing extensive out-of-sample tests. The optimal patterns better represent the bitter and umami peptides when compared against baseline peptides, bitter peptides with all hydrophobic residues and umami peptides with all negatively charged residues, and peptides with randomly-chosen residues. Our method complements quantitative structure--activity relationship methods by offering generic, coarse-grained bitter and umami residue patterns that can aid in locating short bitter or umami segments in a protein and in designing new umami peptides.