Application of QSAR for the identification of key molecular fragments and reliable predictions of effects of textile dyes on growth rate and biomass values of Raphidocelis subcapitata

Predicting blood-brain barrier permeability of molecules with a large language model and machine learning

Predicting the blood-brain barrier (BBB) permeability of small-molecule compounds using a novel artificial intelligence platform is necessary for drug discovery. Machine learning and a large language model on artificial intelligence (AI) tools improve the accuracy and shorten the time for new drug development. The primary goal of this research is to develop artificial intelligence (AI) computing models and novel deep learning architectures capable of predicting whether molecules can permeate the human blood-brain barrier (BBB). The in silico (computational) and in vitro (experimental) results were validated by the Natural Products Research Laboratories (NPRL) at China Medical University Hospital (CMUH). The transformer-based MegaMolBART was used as the simplified molecular input line entry system (SMILES) encoder with an XGBoost classifier as an in silico method to check if a molecule could cross through the BBB. We used Morgan or Circular fingerprints to apply the Morgan algorithm to a set of atomic invariants as a baseline encoder also with an XGBoost classifier to compare the results. BBB permeability was assessed in vitro using three-dimensional (3D) human BBB spheroids (human brain microvascular endothelial cells, brain vascular pericytes, and astrocytes). Using multiple BBB databases, the results of the final in silico transformer and XGBoost model achieved an area under the receiver operating characteristic curve of 0.88 on the held-out test dataset. Temozolomide (TMZ) and 21 randomly selected BBB permeable compounds (Pred scores = 1, indicating BBB-permeable) from the NPRL penetrated human BBB spheroid cells. No evidence suggests that ferulic acid or five BBB-impermeable compounds (Pred scores < 1.29423E-05, which designate compounds that pass through the human BBB) can pass through the spheroid cells of the BBB. Our validation of in vitro experiments indicated that the in silico prediction of small-molecule permeation in the BBB model is accurate. Transformer-based models like MegaMolBART, leveraging the SMILES representations of molecules, show great promise for applications in new drug discovery. These models have the potential to accelerate the development of novel targeted treatments for disorders of the central nervous system.

Exploring molecular fingerprints of different drugs having bile interaction: a stepping stone towards better drug delivery

Bile acids are amphiphilic substances produced naturally in humans. In the context of drug delivery and dosage form design, it is critical to understand whether a drug interacts with bile inside the gastrointestinal (GI) tract or not. This study focuses on the identification of structural fingerprints/features important for bile interaction. Molecular modelling methods such as Bayesian classification and recursive partitioning (RP) studies are executed to find important fingerprints/features for the bile interaction. For the Bayesian classification study, the ROC score of 0.837 and 0.950 are found for the training set and the test set compounds, respectively. The fluorine-containing aliphatic/aromatic group, the branched chain of the alkyl group containing hydroxyl moiety and the phenothiazine ring etc. are identified as good fingerprints having a positive contribution towards bile interactions, whereas, the bad fingerprints such as free carboxylate group, purine, and pyrimidine ring etc. have a negative contribution towards bile interactions. The best tree (tree ID: 1) from the RP study classifies the bile interacting or non-interacting compounds with a ROC score of 0.941 for the training and 0.875 for the test set. Additionally, SARpy and QSAR-Co analyses are also been performed to classify compounds as bile interacting/non-interacting. Moreover, forty-six recently FDA-approved drugs have been screened by the developed SARpy and QSAR-Co models to assess their bile interaction properties. Overall, this attempt may facilitate the researchers to identify bile interacting/non-interacting molecules in a faster way and help in the design of formulations and target-specific drug development.

Theoretical Studies on the Quantitative Structure-Toxicity Relationship of Polychlorinated Biphenyl Congeners Reveal High Affinity Binding to Multiple Human Nuclear Receptors

Polychlorinated biphenyls (PCBs) are organic chemicals consisting of a biphenyl structure substituted with one to ten chlorine atoms, with 209 congeners depending on the number and position of the chlorine atoms. PCBs are widely known to be endocrine-disrupting chemicals (EDCs) and have been found to be involved in several diseases/disorders. This study takes various molecular descriptors of these PCBs (e.g., molecular weight) and toxicity endpoints as molecular activities, investigating the possibility of correlations via the quantitative structure-toxicity relationship (QSTR). This study then focuses on molecular docking and dynamics to investigate the docking behavior of the strongest-binding PCBs to nuclear receptors and compares these to the docking behavior of their natural ligands. Nuclear receptors are a family of transcription factors activated by steroid hormones, and they have been investigated to consider the impact of PCBs on humans in this context. It has been observed that the docking affinity of PCBs is comparable to that of the natural ligands, but they are inferior in terms of stability and interacting forces, as shown by the RMSD and total energy values. However, it is noted that most nuclear receptors respond to PCBs similarly to how they respond to their natural ligands-as shown in the RMSF plots-the most similar of which are seen in the ER, THR-β, and RAR-α. However, this study is performed purely in silico and will need experimental verification for validation.

Thiazolidinedione-triazole conjugates: design, synthesis and probing of the α-amylase inhibitory potential

Aim: The primary objective of this investigation was the synthesis, spectral interpretation and evaluation of the α-amylase inhibition of rationally designed thiazolidinedione-triazole conjugates (7a-7aa). Materials & methods: The designed compounds were synthesized by stirring a mixture of thiazolidine-2,4-dione, propargyl bromide, cinnamaldehyde and azide derivatives in polyethylene glycol-400. The α-amylase inhibitory activity of the synthesized conjugates was examined by integrating in vitro and in silico studies. Results: The investigated derivatives exhibited promising α-amylase inhibitory activity, with IC50 values ranging between 0.028 and 0.088 μmol ml-1. Various computational approaches were employed to get detailed information about the inhibition mechanism. Conclusion: The thiazolidinedione-triazole conjugate 7p, with IC50 = 0.028 μmol ml-1, was identified as the best hit for inhibiting α-amylase.

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.

Impact of Pollutant Load from Textile Dyeing Industry Wastewater on Biometric Growth Profile of Vigna radiata

The phytoremediation of wastewater has certain advantages, but the interactions of soil and crop properties have not been systematically studied. This study aimed to analyze how different concentrations of textile dyeing industry wastewater (25%, 50%, 75%, and 100%) affected soil qualities, growth, and yield attributes (Vigna radiata). In reaction to dyeing effluent at varying concentrations, the seed germination percentage, growth metrics such as tolerance index, phytotoxicity percentage, relative toxicity, extreme and plumule length were calculated. With increasing effluent concentrations, a gradual decrease in the germination of seed and seedling growth was observed. The maximal relative toxicity and percentage of phytotoxicity was 100%. Interaction of biometric growth profile relative seed germination, relative root growth, relative shoot growth, growth index, and seedling vigor index of V. radiata and physicochemical parameter of textile dyeing industry wastewater were also investigated by using the Pearson correlation co-efficient. Principal component analysis (PCA) is helped to obtain and recognize the factors/sources accountability of different concentrations of textile dyeing industry wastewater. The results of the PCA revealed that four components (PC1 to PC4) out of total principal components retained PC1, PC2, with values of 69.25% and 28.85%, respectively.

QSAR modelling of organic dyes for their acute toxicity in Daphnia magna using 2D-descriptors

The present study reports quantitative structure-activity relationship (QSAR) models for 22 organic dyes spanning a broad chemical domain to predict their toxicity in Daphnia magna [log (1/EC₅₀)]. Only two-dimensional descriptors with clear physicochemical meaning were used to construct the QSAR models. The process of development, validation, and interpretation of models adheres to the stringent recommendations of the Organization for Economic Cooperation and Development (OECD) guidelines. In this study, the multi-layered stepwise regression method and linear discriminant analysis (LDA) method were employed for the deployment of regression - and classification-based models respectively; however, the final regression-based QSAR models were obtained through the partial least squares (PLS) regression. Additionally, the applicability domain of the developed models was verified. The constructed models should be applicable in the absence of toxicity data of new or untested dye structures, particularly when the compounds fall within the developed models' scope, and also implementable to develop more environmentally friendly alternatives.

Unswerving modeling of hepatotoxicity of cadmium containing quantum dots using amalgamation of quasiSMILES, index of ideality of correlation, and consensus modeling

Liver toxicity of quantum dots varies with size, concentration, and other structural as well as experimental parameters. For modeling hepatotoxicity, the eclectic data associated with cadmium containing quantum dots have been used in the creation of quasiSMILES for their representation. The core diameter is normalized for wider applicability and the index of the ideality of correlation is applied to construct the better quantitative features toxicity relationship models. Total eight splits are created and the best model is obtained through split 1 with better prediction criteria of validation set objects. The values of all statistical criteria used in the quality determination of a QSAR model are within the specified range for all the eight toxicity models developed here. Factors like TGA ligand and 0.6-0.7 nm diameter are favorable for liver toxicity while L-cysteine ligand and 0.5-0.6 nm core diameter are helpful in the reduction of toxicity. Further, the intelligent consensus modeling process forms a total of 40 individual and 20 consensus models and the best individual and consensus models are 'Good' in MAE-based criteria. The consensus modeling enhances the prediction ability as well as the accuracy of the developed models and increases the applicability space of the built models for hepatotoxicity prediction of quantum dots.