ErbB2 kinase is a key target in approximately 20% of breast cancer cases; however, ErbB2-positive cells may shift their dependence to ErbB4 upon developing resistance to ErbB2 inhibitors. Targeting ErbB4 presents a viable strategy to address this challenge. This study employs a comprehensive approach combining structure-based pharmacophore modelling, molecular docking, and MM-GBSA calculations to identify novel ErbB4 kinase inhibitors. Critical pharmacophoric features were extracted from the crystal structures of ErbB4-lapatinib, followed by virtual screening of the Chembl database to discover potential small molecule candidates. Furthermore, the ADMET profiles of 11 shortlisted candidates were assessed to verify their pharmacokinetic and toxicity properties, identifying Chembl310724, Chembl521284, and Chembl4168686 as promising inhibitors of ErbB4 kinase activity with the binding free energy (ΔG) values of -99.84, -89.42 and -86.06 kcal/mol, respectively. This integrated methodology not only enhances our understanding of ErbB4 inhibition but also sets a foundation for the rational design of targeted therapies addressing breast cancer with ErbB4 dependency.

CDK/Cyclins are dysregulated in several human cancers. Recent studies showed inhibition of CDK4/6 was responsible for controlling cell cycle progression and cancer cell growth. In the present study, atom-based and field-based 3D-QSAR, virtual screening, molecular docking and molecular dynamics studies were done for the development of novel pyrrolo[2,3-d]pyrimidine (P2P) derivatives as anticancer agents. The developed models showed good and values for atom-based 3D-QSAR, which were equal to 0.7327 and 0.8939, whereas for field-based 3D-QSAR the values were 0.8552 and 0.6255, respectively. Molecular docking study showed good-binding interactions with amino acid residues such as VAL-101, HIE-100, ASP-104, ILE-19, LYS-147 and GLU-99, important for CDK4/6 inhibitory activity by using PDB ID: 5L2S. Pharmacophore hypothesis (HHHRR_1) was used in the screening of ZINC database. The top scored ZINC compound ZINC91325512 showed binding interactions with amino acid residues VAL-101, ILE-19, and LYS-147. Enumeration study revealed that the screened compound R1 showed binding interactions with VAL 101 and GLN 149 residues. Furthermore, the Molecular dynamic study showed compound R1, ZINC91325512 and ZINC04000264 having RMSD values of 1.649, 1.733 and 1.610 Å, respectively. These ZINC and enumerated compounds may be used for the development of novel pyrrolo[2,3-d]pyrimidine derivatives as anticancer agent.

Identifying new compounds with minimal side effects to enhance patients' quality of life is the ultimate goal of drug discovery. Due to the expensive and time-consuming nature of experimental investigations and the scarcity of data in traditional QSAR studies, deep transfer learning models, such as the BERT model, have recently been suggested. This study evaluated the model's performance in predicting the anti-proliferative activity of five cancer cell lines (HeLa, MCF7, MDA-MB231, PC3, and MDA-MB) using over 3,000 synthesized molecules from PubChem. The results indicated that the model could predict the class of designed small molecules with acceptable accuracy for most cell lines, except for PC3 and MDA-MB. The model's performance was further tested on an in-house dataset of approximately 25 small molecules per cell line, based on IC50 values. The model accurately predicted the biological activity class for HeLa with an accuracy of and demonstrated acceptable performance for MCF7 and MDA-MB231, with accuracy between 0.56 and 0.66. However, the results were less reliable for PC3 and HepG2. In conclusion, the ChemBERTa fine-tuned model shows potential for predicting outcomes on in-house datasets.

Per- and polyfluoroalkylated organic compounds (PFAs) are versatile compounds extensively used in global industries. However, they are also persistent organic pollutants (POPs). This study aimed to develop new models for assessing oral and inhalation toxicity in rat and mice models. A set of 407 PFAs from the literature was divided into four groups based on the endpoints of interest. The models were constructed using only 2D structure descriptors derived from SMILES strings. The resulting models showed a strong statistical quality for all endpoints. They present an applicability domain (AD) that ensures good reliability, and provided meaningful interpretation, which are partially supported by existing literature. Consequently, these models are valuable for understanding how PFAs exert their toxic effect on mammals and for predicting the risk associated with these significant industrial chemical agents.

Dithiocarbamate fungicides have been widely used in agricultural practices due to their effective control of fungal diseases, thereby contributing to global food security and agricultural productivity. In this study, the inhibitory potency of eight compounds on human and rat aromatase (CYP19A1) activity was evaluated. The results revealed that zineb exhibited the highest inhibitory potency on human CYP19A1 (IC, 2.79 μM). Maneb (IC, 3.09 μM), thiram (IC, 4.76 μM), and ferbam (IC, 6.04 μM) also demonstrated potent inhibition on human CYP19A1. For the rat CYP19A1, disulfiram (IC, 1.90 μM) displayed the strongest inhibition followed by maneb (2.16 μM), zineb (2.54 μM), and thiram (6.99 μM). These dithiocarbamates acted as mixed/non-competitive inhibitors of human and rat CYP19A1. Dithiothreitol (DTT), a reducing agent, partially rescued thiram-mediated inhibition when incubated at the same. Moreover, positive correlations were observed between log , topological polar surface area, molecular weight, and heavy atoms and IC values. 3D-QSAR analysis revealed the hydrogen bond acceptor and donor play critical roles in the binding of dithiocarbamates to human CYP19A1. In silico analysis showed that dithiocarbamates bind to the haem binding site, containing Cys437 residues. In conclusion, some dithiocarbamates potently inhibit human and rat CYP19A1 via interacting with haem-binding Cys437 residues.

Human aldehyde dehydrogenases (ALDHs) are a group of 19 isoforms often overexpressed in cancer stem cells (CSCs). These enzymes play critical roles in CSC protection, maintenance, cancer progression, therapeutic resistance, and poor prognosis. Thus, targeting ALDH isoforms offers potential for innovative cancer treatments. Flavonoids, known for their ability to affect multiple cancer-related pathways, have shown anticancer activity by downregulating specific ALDH isoforms. This study aimed to evaluate 830 flavonoids from the PubChem database against five ALDH isoforms (ALDH1A1, ALDH1A2, ALDH1A3, ALDH2, ALDH3A1) using computational methods to identify potent inhibitors. Extra precision (XP) Glide docking and MM-GBSA free binding energy calculations identified several flavonoids with high binding affinities. MD simulation highlighted flavonoids 1, 2, 18, 27, and 42 as potential specific inhibitors for each isoform, respectively. Flavonoid 10 showed high binding affinities for ALDH1A2, ALDH1A3, and ALDH3A1, emerging as a potential multi-ALDH inhibitor. ADMET property evaluation indicated that the promising hits have acceptable drug-like profiles, but further optimization is needed to enhance their therapeutic efficacy and reduce toxicity, making them more effective ALDH inhibitors for future cancer treatment.

BACE1 has been regarded as an essential drug design target for treating Alzheimer's disease (AD). Multiple independent Gaussian accelerated molecular dynamics simulations (GaMD), deep learning (DL), and molecular mechanics general Born surface area (MM-GBSA) method are integrated to elucidate the molecular mechanism underlying the effect of D93 and D289 protonation on binding of inhibitors OV6 and 4B2 to BACE1. The GaMD trajectory-based DL successfully identifies significant function domains. Dynamic analysis shows that the protonation of D93 and D289 strongly affects the structural flexibility and dynamic behaviour of BACE1. Free energy landscapes indicate that inhibitor-bound BACE1s have more conformational states in the protonated states than the wild-type (WT) BACE1, and show more binding poses of inhibitors. Binding affinities calculated using the MM-GBSA method indicate that the protonation of D93 and D289 highly disturbs the binding ability of inhibitors to BACE1. In addition, the protonation of two residues significantly affects the hydrogen bonding interactions (HBIs) of OV6 and 4B2 with BACE1, altering their binding activity to BACE1. The binding hot spots of BACE1 recognized by residue-based free energy estimations provide rational targeting sites for drug design towards BACE1. This study is anticipated to provide theoretical aids for drug development towards treatment of AD.

Human polo-like kinase 1 (PLK1) has been recognized as an attractive therapeutic target against nasopharyngeal carcinoma (NPC). The kinase contains a conserved polo-box domain (PBD) that exhibits a wide specificity across various substrates. Previously, we explored natural amino acid preference in PLK1 PBD-binding phosphopeptides. However, limited to the short sequence only natural amino acids cannot guarantee the sufficient exploitation of chemical and structural diversity of the phosphopeptides. Here, we described a genetic optimization (GO) strategy to systematically optimize a 10-sized 6-mer phosphopeptide array towards increasing affinity to PLK1 PBD domain by using 20 natural plus 34 unnatural amino acids as basic building blocks. A QSAR predictor was created to guide the GO optimization and then evaluated rigorously at molecular and cellular levels. Three unnatural phosphopeptides uPP8, uPP15 and uPP20 were designed as potent binders with = 0.18, 0.42 and 0.08 μM, respectively, in which the uPP20 also possessed a good anti-tumor activity against human NPC cells when fused with cell permeation sequence. In addition, we defined a relaxed 6-mer motif for the preferential PLK1 PBD-binding phosphosites, namely [Φ/П]-3-[ζ]-2-[ζ]-1-[pT/pS]0-[Φ/П]+1-[Φ]+2, where the symbols Φ, ζ and П represent hydrophobic, polar and aromatic amino acid types, respectively.  .

Cathepsin K (CatK), a lysosomal cysteine protease, contributes to skeletal abnormalities, heart diseases, lung inflammation, and central nervous system and immune disorders. Currently, CatK inhibitors are associated with severe adverse effects, therefore limiting their clinical utility. This study focuses on exploring quantitative structure-activity relationships (QSAR) on a dataset of CatK inhibitors (1804) compiled from the ChEMBL database to predict the inhibitory activities. After data cleaning and pre-processing, a total of 1568 structures were selected for exploratory data analysis which revealed physicochemical properties, distributions and statistical significance between the two groups of inhibitors. PubChem fingerprinting with 11 different machine-learning classification models was computed. The comparative analysis showed the ET model performed well with accuracy values for the training set (0.999), cross-validation (0.970) and test set (0.977) in line with OECD guidelines. Moreover, to gain structural insights on the origin of CatK inhibition, 15 diverse molecules were selected for molecular docking. The CatK inhibitors (1 and 2) exhibited strong binding energies of -8.3 and -7.2 kcal/mol, respectively. MD simulation (300 ns) showed strong structural stability, flexibility and interactions in selected complexes. This synergy between QSAR, docking, MD simulation and machine learning models strengthen our evidence for developing novel and resilient CatK inhibitors.

Fraction unbound in plasma () of drugs is an significant factor for drug delivery and other biological incidences related to the pharmacokinetic behaviours of drugs. Exploration of different molecular fragments for of different small molecules/agents can facilitate in identification of suitable candidates in the preliminary stage of drug discovery. Different researchers have implemented strategies to build several prediction models for of different drugs. However, these studies did not focus on the identification of responsible molecular fragments to determine the fraction unbound in plasma. In the current work, we tried to focus on the development of robust classification-based QSAR models and evaluated these models with multiple statistical metrics to identify essential molecular fragments/structural attributes for fractions unbound in plasma. The study unequivocally suggests various -containing aromatic rings and aliphatic groups have positive influences and sulphur-containing thiadiazole rings have negative influences for the values. The molecular fragments may help for the assessment of the values of different small molecules/drugs in a speedy way in comparison to experiment-based in vivo and in vitro studies.

Ionic liquids (ILs) have attracted considerable interest due to their unique properties and prospective uses in various industries. However, their potential toxicity, particularly regarding enzyme inhibition, has become a growing concern. In this study, a QSAR model was proposed to predict the enzyme inhibition toxicity of ILs. A dataset of diverse ILs with corresponding toxicity data against three enzymes was compiled. Molecular descriptors that capture the physicochemical, structural, and topological properties of the ILs were calculated. To optimize the selection of descriptors and develop a robust QSAR model, the chaotic spotted hyena optimization algorithm, a novel nature-inspired metaheuristic, was employed. The proposed algorithm efficiently searches for an optimal subset of descriptors and model parameters, enhancing the predictive performance and interpretability of the QSAR model. The developed model exhibits excellent predictive capability, with high classification accuracy and low computation time. Sensitivity analysis and molecular interpretation of the selected descriptors provide insights into the critical structural features influencing the toxicity of ILs. This study showcases the successful application of the chaotic spotted hyena optimization algorithm in QSAR modelling and contributes to a better understanding of the toxicity mechanisms of ILs, aiding in the design of safer alternatives for industrial applications.

Anaplastic Lymphoma Kinase (ALK) is a receptor tyrosine kinase within the insulin receptor superfamily. Alterations in ALK, such as rearrangements, mutations, or amplifications, have been detected in various tumours, including lymphoma, neuroblastoma, and non-small cell lung cancer. In this study, we outline a computational workflow designed to uncover new inhibitors of ALK. This process starts with a ligand-based exploration of the pharmacophoric space using 13 diverse sets of ALK inhibitors. Subsequently, quantitative structure-activity relationship (QSAR) modelling is employed in combination with a genetic function algorithm to identify the optimal combination of pharmacophores and molecular descriptors capable of elucidating variations in anti-ALK bioactivities within a compiled list of inhibitors. The successful QSAR model revealed three pharmacophores, two of which share three similar features, prompting their merger into a single pharmacophore model. The merged pharmacophore was used as a 3D search query to mine the National Cancer Institute (NCI) database for novel anti-ALK leads. Subsequent in vitro bioassay of the top 40 hits identified two compounds with low micromolar IC values. Remarkably, one of the identified leads possesses a novel chemotype compared to known ALK inhibitors.

MMP-2 overexpression is strongly related to several diseases including cancer. However, none of the MMP-2 inhibitors have been marketed as drug candidates due to various adverse effects. Here, a set of sulphonyl pyrrolidines was subjected to validation of molecular modelling followed by binding mode analysis to explore the crucial structural features required for the discovery of promising MMP-2 inhibitors. This study revealed the importance of hydroxamate as a potential zinc-binding group compared to the esters. Importantly, hydrophobic and sterical substituents were found favourable at the terminal aryl moiety attached to the sulphonyl group. The binding interaction study revealed that the S1' pocket of MMP-2 similar to '' allows the aryl moiety for proper fitting and interaction at the active site to execute potential MMP-2 inhibition. Again, the sulphonyl pyrrolidine moiety can be a good fragment necessary for MMP-2 inhibition. Moreover, some novel MMP-2 inhibitors were also reported. They showed the significance of the 3 position substitution of the pyrrolidine ring to produce interaction inside S2' pocket. The current study can assist in the design and development of potential MMP-2 inhibitors as effective drug candidates for the management of several diseases including cancers in the future.

Dengue fever, prevalent in Southeast Asian countries, currently lacks effective pharmaceutical interventions for virus replication control. This study employs a strategy that combines machine learning (ML)-based quantitative-structure-activity relationship (QSAR), molecular docking, and molecular dynamics simulations to discover potential inhibitors of the NS3 protease of the dengue virus. We used nine molecular fingerprints from PaDEL to extract features from the NS3 protease dataset of dengue virus type 2 in the ChEMBL database. Feature selection was achieved through the low variance threshold, F-Score, and recursive feature elimination (RFE) methods. Our investigation employed three ML models - support vector machine (SVM), random forest (RF), and extreme gradient boosting (XGBoost) - for classifier development. Our SVM model, combined with SVM-RFE, had the best accuracy (0.866) and ROC_AUC (0.964) in the testing set. We identified potent inhibitors on the basis of the optimal classifier probabilities and docking binding affinities. SHAP and LIME analyses highlighted the significant molecular fingerprints (e.g. ExtFP69, ExtFP362, ExtFP576) involved in NS3 protease inhibitory activity. Molecular dynamics simulations indicated that amphotericin B exhibited the highest binding energy of -212 kJ/mol and formed a hydrogen bond with the critical residue Ser196. This approach enhances NS3 protease inhibitor identification and expedites the discovery of dengue therapeutics.

In the search for natural and non-toxic products alternatives to synthetic pesticides, the fumigant and repellent activities of 35 essential oils are predicted in the human head louse () through the Quantitative Structure-Activity Relationships (QSAR) theory. The number of constituents of essential oils with weight percentage composition greater than 1% varies from 1 to 15, encompassing up to 213 structurally diverse compounds in the entire dataset. The 27,976 structural descriptors used to characterizing these complex mixtures are calculated as linear combinations of non-conformational descriptors for the components. This approach is considered simple enough to evaluate the effects that changes in the composition of each component could have on the studied bioactivities. The best linear regression models found, obtained through the Replacement Method variable subset selection method, are applied to predict 13 essential oils from a previous study with unknown property data. The results show that the simple methodology applied here could be useful for predicting properties of interest in complex mixtures such as essential oils.

Human neutrophil elastase (HNE) plays a key role in initiating inflammation in the cardiopulmonary and systemic contexts. Pathological auto-proteolysed two-chain (tc) HNE exhibits reduced binding affinity with inhibitors. Using AutoDock Vina v1.2.0, 66 flavonoid inhibitors, sivelestat and alvelestat were docked with single-chain (sc) HNE and tcHNE. Schrodinger PHASE v13.4.132 was used to generate a 3D-QSAR model. Molecular dynamics (MD) simulations were conducted with AMBER v18. The 3D-QSAR model for flavonoids with scHNE showed  = 0.95 and  = 0.91. High-activity compounds had hydrophobic A/A2 and C/C2 rings in the S1 subsite, with hydrogen bond donors at C5 and C7 positions of the A/A2 ring, and the C4' position of the B/B1 ring. All flavonoids except robustaflavone occupied the S1'-S2' subsites of tcHNE with decreased AutoDock binding affinities. During MD simulations, robustaflavone remained highly stable with both HNE forms. Principal Component Analysis suggested that robustaflavone binding induced structural stability in both HNE forms. Cluster analysis and free energy landscape plots showed that robustaflavone remained within the sc and tcHNE binding site throughout the 100 ns MD simulation. The robustaflavone scaffold likely inhibits both tcHNE and scHNE. It is potentially superior to sivelestat and alvelestat and can aid in developing therapeutics targeting both forms of HNE.

The 3C-like Proteinase (3CLpro) of novel coronaviruses is intricately linked to viral replication, making it a crucial target for antiviral agents. In this study, we employed two fingerprint descriptors (ECFP_4 and MACCS) to comprehensively characterize 889 compounds in our dataset. We constructed 24 classification models using machine learning algorithms, including Support Vector Machine (SVM), Random Forest (RF), extreme Gradient Boosting (XGBoost), and Deep Neural Networks (DNN). Among these models, the DNN- and ECFP_4-based Model 1D_2 achieved the most promising results, with a remarkable Matthews correlation coefficient (MCC) value of 0.796 in the 5-fold cross-validation and 0.722 on the test set. The application domains of the models were analysed using d calculations. The collected 889 compounds were clustered by K-means algorithm, and the relationships between structural fragments and inhibitory activities of the highly active compounds were analysed for the 10 obtained subsets. In addition, based on 464 3CLpro inhibitors, 27 QSAR models were constructed using three machine learning algorithms with a minimum root mean square error (RMSE) of 0.509 on the test set. The applicability domains of the models and the structure-activity relationships responded from the descriptors were also analysed.

Essential oils (EOs) are natural products currently used to control arthropods, and their interaction with insect odorant-binding proteins (OBPs) is fundamental for the discovery of new repellents. This in silico study aimed to predict the potential of EO components to interact with odorant proteins. A total of 684 EO components from PubChem were docked against 23 odorant binding proteins from Protein Data Bank using AutoDock Vina. The ligands and proteins were optimized using Gaussian 09 and Sybyl-X 2.0, respectively. The nature of the protein-ligand interactions was characterized using LigandScout 4.0, and visualization of the binding mode in selected complexes was carried out by Pymol. Additionally, complexes with the best binding energy in molecular docking were subjected to 500 ns molecular dynamics simulations using Gromacs. The best binding affinity values were obtained for the 1DQE-ferutidine (-11 kcal/mol) and 2WCH-kaurene (-11.2 kcal/mol) complexes. Both are natural ligands that dock onto those proteins at the same binding site as DEET, a well-known insect repellent. This study identifies kaurene and ferutidine as possible candidates for natural insect repellents, offering a potential alternative to synthetic chemicals like DEET.

Resveratrol is converted to various metabolites by gut microbiota. Human and rat liver 11β-hydroxysteroid dehydrogenase 1 (11β-HSD1) are critical for glucocorticoid activation, while 11β-HSD2 in the kidney does the opposite reaction. It is still uncertain whether resveratrol and its analogues selectively inhibit 11β-HSD1. In this study, the inhibitory strength, mode of action, structure-activity relationship (SAR), and docking analysis of resveratrol analogues on human, rat, and mouse 11β-HSD1 and 11β-HSD2 were performed. The inhibitory strength of these chemicals on human 11β-HSD1 was dihydropinosylvin (6.91 μM) > lunularin (45.44 μM) > pinostilbene (46.82 μM) > resveratrol (171.1 μM) > pinosylvin (193.8 μM) > others. The inhibitory strength of inhibiting rat 11β-HSD1 was pinostilbene (9.67 μM) > lunularin (17.39 μM) > dihydropinosylvin (19.83 μM) > dihydroresveratrol (23.07 μM) > dihydroxystilbene (27.84 μM) > others and dihydropinosylvin (85.09 μM) and pinostilbene (>100 μM) inhibited mouse 11β-HSD1. All chemicals did not inhibit human, rat, and mouse 11β-HSD2. It was found that dihydropinosylvin, lunularin, and pinostilbene were competitive inhibitors of human 11β-HSD1 and that pinostilbene, lunularin, dihydropinosylvin, dihydropinosylvin and dihydroxystilbene were mixed inhibitors of rat 11β-HSD1. Docking analysis showed that they bind to the steroid-binding site of human and rat 11β-HSD1. In conclusion, resveratrol and its analogues can selectively inhibit human and rat 11β-HSD1, and mouse 11β-HSD1 is insensitive to the inhibition of resveratrol analogues.

The widespread use of pyrethroid and organophosphate pesticides necessitates accurate toxicity predictions for regulatory compliance. In this study QSAR and SSD models for six pyrethroid and four organophosphate compounds using QSAR Toolbox and SSD Toolbox have been developed. The QSAR models, described by the formula 48 h-EC50 or 96 h-LC50 = x + y * log Kow, were validated for predicting 48 h-EC50 values for acute toxicity and 96 h-LC50 values for acute fish toxicity, meeting criteria of ≥10, ≥0.7, and >0.5. Predicted 48 h-EC50 values for pyrethroids ranged from 3.95 × 10 mg/L (permethrin) to 8.21 × 10 mg/L (fenpropathrin), and 96 h-LC50 values from 3.89 × 10 mg/L (permethrin) to 1.68 × 10 mg/L (metofluthrin). For organophosphates, 48 h-EC50 values ranged from 2.00 × 10 mg/L (carbophenothion) to 3.76 × 10 mg/L (crufomate) and 96 h-LC50 values from 3.81 × 10 mg/L (carbophenothion) to 12.3 mg/L (crufomate). These values show a good agreement with experimental data, though some, like Carbophenothion, overestimated toxicity. HC05 values, indicating hazardous concentrations for 5% of species, range from 0.029 to 0.061 µg/L for pyrethroids and 0.030 to 0.072 µg/L for organophosphates. These values aid in establishing environmental quality standards (EQS). Compared to existing EQS, HC05 values for pyrethroids were less conservative, while those for organophosphates were comparable.