QSAR Research Articles

In Silico Prediction of EGFR Inhibitors from Thiophene Derivatives

Abstract: Cancer is one of the biggest global health problems and is the second leading cause of death worldwide. Cancer also causes great damage to economy. Unfortunately, there is still no effective treatment method against this disease today, and the mortality rates in certain types are still very high. Medical research can now be done faster and safer with the aid of in silico studies. These studies save time for researchers and accelerate new drug discoveries. In our study, thiophene derivatives with important efficacy in cancer treatment were focused on and the affinity of the small molecule structures determined as candidates to the Epidermal Growth Factor Receptor (EGFR), known to be the key receptor in cancer, was examined. First, molecular docking studies were performed, and then long-term molecular dynamics (MD) simulations were carried out. Finally, anti-cancer activity predictions based on Quantitative Structure-Activity Relationship (QSAR) were performed. Co-crystallized ligand Erlotinib, taken from the Protein Data Bank (PDB), was used as a positive control and compared with candidate drugs using the same procedures. In light of the analysis of virtual screening, MD, MM/GBSA, and QSAR predictions, the top three molecules and their MM/GBSA scores were identified as follows: OSI 930 (-65.81 kcal/mol), Neltenexine (-49.53 kcal/mol), and Tenonitrozole (-41.95 kcal/mol). As a result, in this study, candidate molecules that inhibit EGFR and have the highest potential as anti-cancer drugs among thiophene-derived compounds were determined and detailed in silico analyzes were performed. This study holds importance as it may guide future anti-cancer drug discovery studies.

QSAR Modeling for Predicting Beta-Secretase 1 Inhibitory Activity in Alzheimer's Disease with Support Vector Regression

Alzheimer's disease (AD) is a neurodegenerative disorder characterized by cognitive decline, with the accumulation of β-amyloid (Aβ) plaques playing a key role in its progression. Beta-Secretase 1 (BACE1) is a crucial enzyme in Aβ production, making it a prime therapeutic target for AD treatment. However, designing effective BACE1 inhibitors has been challenging due to poor selectivity and limited blood-brain barrier permeability. To address these challenges, we employed a machine learning approach using Support Vector Regression (SVR) in a Quantitative Structure-Activity Relationship (QSAR) model to predict the inhibitory activity of potential BACE1 inhibitors. Our model, trained on a dataset of 7,298 compounds from the ChEMBL database, accurately predicted pIC50 values using molecular descriptors, achieving an R² of 0.690 on the testing set. The model's performance demonstrates its utility in prioritizing drug candidates, potentially accelerating drug discovery. This study highlights the effectiveness of computational approaches in optimizing drug discovery and suggests that further refinement could enhance the model’s predictive power for AD therapeutics.

From protein structure to an optimized chromatographic capture step using multiscale modeling.

Optimizing a biopharmaceutical chromatographic purification process is currently the greatest challenge during process development. A lack of process understanding calls for extensive experimental efforts in pursuit of an optimal process. In silico techniques, such as mechanistic or data driven modeling, enhance the understanding, allowing more cost-effective and time efficient process optimization. This work presents a modeling strategy integrating quantitative structure property relationship (QSPR) models and chromatographic mechanistic models (MM) to optimize a cation exchange (CEX) capture step, limiting experiments. In QSPR, structural characteristics obtained from the protein structure are used to describe physicochemical behavior. This QSPR information can be applied in MM to predict the chromatogram and optimize the entire process. To validate this approach, retention profiles of six proteins were determined experimentally from mixtures, at different pH (3.5, 4.3, 5.0, and 7.0). Four proteins at different pH's were used to train QSPR models predicting the retention volumes and characteristic charge, subsequently the equilibrium constant was determined. For an unseen protein knowing only the protein structure, the retention peak difference between the modeled and experimental peaks was 0.2% relative to the gradient length (60 column volume). Next, the CEX capture step was optimized, demonstrating a consistent result in both the experimental and QSPR-based methods. The impact of model parameter confidence on the final optimization revealed two viable process conditions, one of which is similar to the optimization achieved using experimentally obtained parameters. The multiscale modeling approach reduces the required experimental effort by identification of initial process conditions, which can be optimized.

Photodegradation of polychlorinated biphenyls (PCBs) on suspended particles from the Yellow River under sunlight irradiation: QSAR model and mechanism analysis

Polychlorinated biphenyls (PCBs), as a class of hydrophobic organic pollutants, are widely found in river sediments and suspended particles, and the environmental fate of different PCBs can be better understood by investigating their photochemical transformation process. In this study, the quantitative structure-activity relationship (QSAR) model between the photodegradation rate constants of 17 PCBs adsorbed on Yellow River suspended particles in water (as a typical heterogeneous photodegradation system) and the physicochemical parameters of PCBs was constructed by SPSS and machine learning. The model showed that the more hydrophobicity of the molecule, the more positive charge carried by the aromatic C atoms, and the presence of chlorine atoms adjacent to the carbon bridge could all enhance the photochemical activity of PCBs. From the combined analysis of rate constants, quenching experiments and theoretical calculations, it was revealed for the first time that in natural suspended particle containing organic matter, the higher concentration of •O2- and 1O2 in the hydrophobic zone contributed more to the more hydrophobic PCBs, while •OH in the hydrophilic zone played a major role in the degradation of the less hydrophobic PCBs. Findings of this study would deepen the understanding of the degradation mechanism of hydrophobic pollutants by active species in complex environments.

Computing Zagreb Connection Indices for the Cartesian Product of Path and Cycle Graphs

Topological indices ( ) are a class of graph-based descriptors widely used in chemiformatics and Quantitative Structure-Activity Relationship (QSAR) studies. TIs capture key structural features of molecules by encoding graph-theoretic information, offering a quantitative representation of molecular topology. Each mathematical network is associated with a specific numerical value determined by a function. Among the Zagreb connection indices have been extensively researched. This study delves into the Cartesian product of cycle graphs with path graphs, elucidating the comprehensive implications of . These indices encompass the first , second , and third . Furthermore, comprehensive results of the modified first , modified second , and modified third are presented, along with the first multiplicative ZCI second multiplicative ZCI (SMZCI), and third multiplicative ZCI Moreover, modified first multiplicative ZCI ), modified second multiplicative ZCI and modified third multiplicative ZCI ( are also calculated. To provide precision, both the graphical and numerical analyses of the computed findings are aligned for the two Cartesian products. The foundation for mathematically modeling complex networks and chemical structures lies in graph theory. Topological indices ( ) are a class of graph-based descriptors widely used in chemiformatics and quantitative structure-activity relationship (QSAR) studies. TIs capture key structural features of molecules by encoding graph-theoretic information, offering a quantitative representation of molecular topology. Each mathematical network is associated with a specific numerical value determined by a topological index function. Among these the Zagreb connection indices are extensively researched TIs. This article delves into the Cartesian product of cycle graphs with path graphs, elucidating the comprehensive implications of . These indices encompass the first, Second and third. Furthermore, we present the comprehensive results of the modified , modified and modified , and also present the first multiplicative Zagreb connection index , and , along with their modified counterparts like modified first multiplicative Zagreb connection index , and . These analysis encompass two distinct types of graphs: cycle graphs and path graphs. To provide further precision, we align both the graphical and numerical analysis of our computed findings for these two Cartesian products.

Prediction of Dielectric Constant in Series of Polymers by Quantitative Structure-Property Relationship (QSPR)

This work is devoted to the investigation of dielectric permittivity which is influenced by electronic, ionic, and dipolar polarization mechanisms, contributing to the material’s capacity to store electrical energy. In this study, an extended dataset of 86 polymers was analyzed, and two quantitative structure–property relationship (QSPR) models were developed to predict dielectric permittivity. From an initial set of 1273 descriptors, the most relevant ones were selected using a genetic algorithm, and machine learning models were built using the Gradient Boosting Regressor (GBR). In contrast to Multiple Linear Regression (MLR)- and Partial Least Squares (PLS)-based models, the gradient boosting models excel in handling nonlinear relationships and multicollinearity, iteratively optimizing decision trees to improve accuracy without overfitting. The developed GBR models showed high R2 coefficients of 0.938 and 0.822, for the training and test sets, respectively. An Accumulated Local Effect (ALE) technique was applied to assess the relationship between the selected descriptors—eight for the GB_A model and six for the GB_B model, and their impact on target property. ALE analysis revealed that descriptors such as TDB09m had a strong positive effect on permittivity, while MLOGP2 showed a negative effect. These results highlight the effectiveness of the GBR approach in predicting the dielectric properties of polymers, offering improved accuracy and interpretability.

Selective elimination of organic pollutants and analysis of effects and novel mechanisms of aged microplastics on wavelength-dependent UV-LED/H2O2 system

The selective removal of organic pollutants and potential impact of aged microplastics (MPs) as emerging pollutants in wavelength-dependent UV-LED/H2O2 system are not fully understood. This study found that cefalexin (CFX) degradation efficiency in UV-LED alone system was highly correlated with its UV molar absorbance (R2=0.994), while in UV-LED/H2O2 system, it was correlated with ·OH yield (R2=0.991) across various wavelengths. Quantitative structure-activity relationship (QSAR) analysis showed selective degradation of six pollutants based on their e--donating capabilities (R2=0.748–0.916). The coexistence of aged MPs, introducing C-O/C=O groups and rearranging their surface e-, potentially affected the elimination efficiency of CFX. Aged polystyrene (PS) decreased the degradation efficiency of CFX by shorting the O-O bond length (lO-O) in H2O2 and capturing e- from H2O2, whereas aged polyethylene (PE), polypropylene (PP) and polyvinyl chloride (PVC) had negligible effects as the lO-O elongation balanced the e--donating effect of H2O2. Additionally, phenol released from aged PS, with strong nucleophilicity, competing with CFX for ·OH, further decreasing CFX degradation efficiency. This study provides valuable insights into organic pollutant selective removal and reveals a novel inhibitory mechanism of aged PS on the performance of UV-LED/H2O2 technology.

Highly Cytotoxic Cryptophycin Derivatives with Modification in Unit D for Conjugation.

Cytotoxic payloads for drug conjugates suitable for directed tumor therapy need to be highly potent and require a functional group for conjugation with the homing device (antibody, peptide, or small molecule). Cryptophycins are cyclodepsipeptides that stand out from the realm of natural products due to their extraordinarily high cytotoxicity. However, the installation of a suitable conjugation handle without compromising the toxicity is highly challenging. The unit D, natively 2-hydroxyisocaproic acid (leucic acid), was envisaged as a promising attachment site based on structural information from X-ray analysis. A versatile, scalable and efficient synthetic route towards conjugable cryptophycins with modification in unit D was developed and an array of new cryptophycin analogues was synthesized. Several derivatives, especially those containing lipophilic groups with low steric demand such as alkylated amino groups, exhibit low picomolar cytotoxicity often combined with efficacy against multidrug-resistant tumor cells. The newly established cryptophycin analogues comprise a broad range of relevant functional groups used as conjugation handles, among them amino, hydroxy, carboxy, as well as sulfur-containing derivatives. X-ray crystallographic analysis of a tubulin-bound cryptophycin together with quantitative structure activity relationship manifested rationales for the synthesis of most potent cryptophycin derivatives and further confirmed the suitability of modifications in unit D.

Development of Novel ROCK Inhibitors via 3D-QSAR and Molecular Docking Studies: A Framework for Multi-Target Drug Design.

Background/Objectives: Alterations in the actin cytoskeleton correlates to tumor progression and affect critical cellular processes such as adhesion, migration and invasion. Rho-associated coiled-coil-containing protein kinases (ROCK1 and ROCK2), important regulators of the actin cytoskeleton, are frequently overexpressed in various malignancies. The aim of this study was therefore to identify the key structural features of ROCK1/ROCK2 inhibitors using computer-aided drug design (CADD) approaches. In addition, new developed ROCK inhibitors provided a significant framework for the development of multitarget therapeutics-ROCK/HDAC (histone deacetylases) multitarget inhibitors. Methods: 3D-QSAR (Quantitative structure-activity relationship study) and molecular docking study were employed in order to identify key structural features that positively correlate with ROCK inhibition. MDA-MB-231, HCC1937, Panc-1 and Mia PaCa-2 cells were used for evaluation of anticancer properties of synthesized compounds. Results: C-19 showed potent anti-cancer properties, especially enhancement of apoptosis and cell cycle modulation in pancreatic cancer cell lines. In addition, C-19 and C-22 showed potent anti-migratory and anti-invasive effects comparable to the well-known ROCK inhibitor fasudil. Conclusions: In light of the results of this study, we propose a novel multi-target approach focusing on developing dual HDAC/ROCK inhibitors based on the structure of both C-19 and C-22, exploiting the synergistic potential of these two signaling pathways to improve therapeutic efficacy in metastatic tumors. Our results emphasize the potential of multi-target ROCK inhibitors as a basis for future cancer therapies.

Machine learning approaches in designing anti-HIV nitroimidazoles: 2D/3D QSAR, kNN-MFA, docking, dynamics, PCA analysis and MMGBSA studies

In this study, newly synthesized 20 nitroimidazole derivatives were subjected to 2D and 3D Quantitative Structure-Activity Relationship (QSAR) study to investigate their anti-HIV activity against both ROD and IIIB strains. Later, proposed hypothesis was virtually proved by further in-silico studies. In statistically significant 2D-QSAR models r2 values for IIIB strains 0.9241 and for ROD strains 0.9412 with corresponding q2 values of 0.7706 and 0.8299, were obtained, respectively. Different models were constructed using three different kNN-MFA 3D QSAR approaches such as SW-FB, SA score, and GA. By using the generated hypothesis, newer analogues of nitroimidazole derivatives was designed and molecular modelling studies were conducted to prove the hypothesis. The three molecules were displayed the good docking scores compared to the reference molecule. The stabilities of docked complexes were analyzed by MD simulations and MMGB/SA calculations. These results offer insightful design guidance for novel anti-HIV compounds synthesis and suggest interesting directions for future pharmaceutical research.

QSAR: Using the Past to Study the Present.

Quantitative structure-activity relationships (QSAR) is a method for predicting the physical and biological properties of small molecules; it is in use in industry and public services. However, as any scientific method, it is challenged by more and more requests, especially considering its possible role in assessing the safety of new chemicals. To answer the question whether QSAR, by exploiting available knowledge, can build new knowledge, the chapter reviews QSAR methods in search of a QSAR epistemology. QSAR stands on tree pillars, i.e., biological data, chemical knowledge, and modeling algorithms. Usually the biological data, resulting from good experimental practice, are taken as a true picture of the world; chemical knowledge has scientific bases; so if a QSAR model is not working, blame modeling. The role of modeling in developing scientific theories, and in producing knowledge, is so analyzed. QSAR is a mature technology and is part of a large body of in silico methods and other computational methods. The active debate about the acceptability of the QSAR models, about the way to communicate them, and the explanation to provide accompanies the development of today QSAR models. An example about predicting possible endocrine-disrupting chemicals (EDC) shows the many faces of modern QSAR methods.

Mn and P co-doped biochar catalyst for persulfate efficient degradation of tetracycline hydrochloride：Process and mechanism

A novel manganese/phosphorus-doped biochar (Mn/P-C) catalyst was prepared for the degradation of tetracycline hydrochloride (TCH) by activating peroxymonosulfate (PMS). Characterization of the catalyst revealed that Mn/P-C possessed stacked, complex pleated sheets and surface oxygen-containing functional groups, providing abundant active sites. Mn/P-C exhibited superior adsorption and catalytic properties. Nearly complete removal of TCH was achieved under optimal conditions: a PMS concentration of 2 mM, pH 6.51, and catalyst dosage of 0.5 g/L within 120 minutes of reaction time. The reaction rate constant of the system was 0.060 min−1, which was 13.79 times higher than that of pure biochar. XPS characterization before and after the reaction, quenching experiment, and electron paramagnetic resonance (EPR) experiment comprehensively verified the reaction pathway mechanisms. The primary radicals involved were SO4•- and O2•-, while the 1O2 non-radical transfer pathway was also generated on the catalyst surface, enhancing electron transfer and accelerating catalytic degradation. UPLC-MS/MS was used to investigate the main degradation intermediates and the possible transformation pathways were proposed. The toxicity of TCH and its intermediates was evaluated by the quantitative structure-activity relationship (QSAR) method. Theoretical calculations provided deeper insights into TCH degradation pathways through DFT computational analysis. This study confirms that doping biochar with transition metals and nonmetals can synergistically enhance the degradation efficacy of PMS-activated biochar catalysts, providing a novel approach for the application of carbon-based material catalysts in persulfate activation.

Discovery of Small Molecule Inhibitors Targeting CTNNB1 (β-catenin) for Endometrial cancer: Employing 3D QSAR, Drug-Likeness Assessment, ADMET Predictions, Molecular Docking and Simulation.

Endometrial carcinoma (EC) is a type of cancer that originates in the lining of the uterus, known as the endometrium. It is associated with various treatment options such as surgery, radiation therapy, chemotherapy, and hormone therapy, each presenting unique challenges and limitations. Beta-catenin, a protein involved in the development and progression of several cancers, including EC, plays a crucial role. Abnormal beta-catenin signaling is often linked to the emergence of specific EC subtypes, affecting tumor growth and invasion. The study's objective is to identify compounds targeting the beta-catenin protein for treating endometrial cancer (EC) using in silico drug design. Our approach includes molecular docking to evaluate binding affinities, ADME profiling for pharmacokinetic properties, toxicity assessments, and molecular dynamics simulations to assess compound stability and interactions. Approximately one thousand anti-cancer phytochemicals were sourced from PubChem and subjected to molecular docking simulations against the beta-catenin protein. The compounds were evaluated based on their binding affinities, with the top five selected for further analysis. These five molecules underwent toxicity and ADME profiling. The Prediction of Activity Spectra for Substances (PASS) tool was used to identify compounds targeting CTNNB1. Comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) were employed to establish quantitative structure-activity relationship (QSAR) models for the five CTNNB1 antagonist molecules. The selected five compounds, namely Pazopanib, Binimetinib, Telatinib, 4-(2,3-Dihydrobenzo[ b][1,4]dioxin-6-yl)-3-((5-nitrothiazol-2-yl)thio)-1H-1,2,4-triazol-5(4H)-one, and Ribavirin, demonstrated efficacy against CTNN1. MD simulations of the docked complexes confirmed the stability of these drugs in binding to the target protein. All five molecules showed promising safety and effectiveness profiles according to their ADME and toxicity evaluations. Through a comprehensive screening process employing in silico drug design methods, this study successfully identified five potential human anticancer drug candidates targeting the beta-catenin protein. These findings offer a foundation for further experimental validation and development towards the treatment of EC.

Application of Ensemble Machine Learning Methods for QSAR Classification of Leukotriene A4 Hydrolase Inhibitors in Drug Discovery

Inflammatory diseases such as asthma, rheumatoid arthritis, and cardiovascular conditions are driven by overproduction of leukotriene B4 (LTB4), a potent inflammatory mediator. Leukotriene A4 hydrolase (LTA4H) plays a critical role in converting leukotriene A4 into LTB4, making it a prime target for drug discovery. Despite ongoing efforts, developing effective LTA4H inhibitors has been challenging due to the complex binding properties of the enzyme and the structural diversity of potential inhibitors. Traditional drug discovery methods, like high-throughput screening (HTS), are often time-consuming and inefficient, prompting the need for more advanced approaches. Quantitative Structure-Activity Relationship (QSAR) modeling, enhanced by ensemble machine learning techniques, provides a promising solution by enabling accurate prediction of compound bioactivity based on molecular descriptors. In this study, six ensemble machine learning methods—AdaBoost, Extra Trees, Gradient Boosting, LightGBM, Random Forest, and XGBoost—were employed to classify LTA4H inhibitors. The dataset, comprising 636 compounds labeled as active or inactive based on pIC50 values, was processed to extract 450 molecular descriptors after feature engineering. The results show that the LightGBM model achieved the highest classification accuracy (83.59%) and Area Under the Curve (AUC) value (0.901), outperforming other models. XGBoost and Random Forest also demonstrated strong performance, with AUC values of 0.890 and 0.895, respectively. The high sensitivity (95.24%) of the XGBoost model highlights its ability to accurately identify active compounds, though it exhibited slightly lower specificity (61.36%), indicating a higher false-positive rate. These findings suggest that ensemble machine learning models, particularly LightGBM, are highly effective in predicting bioactivity, offering valuable tools for early-stage drug discovery. The results indicate that ensemble methods significantly enhance QSAR model accuracy, making them viable for identifying promising LTA4H inhibitors, potentially accelerating the development of anti-inflammatory therapies.

Development of machine learning-based quantitative structure–activity relationship models for predicting plasma half-lives of drugs in six common food animal species

Abstract Plasma half-life is a crucial pharmacokinetic parameter for estimating extralabel withdrawal intervals of drugs to ensure the safety of food products derived from animals. This study focuses on developing a quantitative structure–activity relationship (QSAR) model incorporating multiple machine learning and artificial intelligence algorithms, and aims to predict the plasma half-lives of drugs in 6 food animals, including cattle, chickens, goats, sheep, swine, and turkeys. By integrating 4 machine learning algorithms with 5 molecular descriptor types, 20 QSAR models were developed using data from the Food Animal Residue Avoidance Databank (FARAD) Comparative Pharmacokinetic Database. The deep neural network (DNN) algorithm demonstrated the best prediction ability of plasma half-lives. The DNN model with all descriptors achieved superior performance with a high coefficient of determination (R2) of 0.82 ± 0.19 in 5-fold cross-validation on the training sets and an R2 of 0.67 on the independent test set, indicating accurate predictions and good generalizability. The final model was converted to a user-friendly web dashboard to facilitate its wide application by the scientific community. This machine learning-based QSAR model serves as a valuable tool for predicting drug plasma half-lives and extralabel withdrawal intervals in 6 common food animals based on physicochemical properties. It also provides a foundation to develop more advanced models to predict the tissue half-life of drugs in food animals.

Investigating the electronic properties of edge glycine/biopolymer/graphene quantum dots

This study systematically investigated four types of graphene quantum dots (GQDs) AHEX, ZTRI, ZHEX, and ATRI, and their interactions with glycine to form GQD-glycine complexes. Utilizing density functional theory (DFT) and the PM6 semiempirical method, the study analyzed electronic properties and structure-activity relationships. Global reactivity indices were calculated using Koopmans’ theorem, and quantitative structure-activity relationship (QSAR) parameters were assessed via SCIGRESS 0.3. The study further explored interactions using density of states (DOS) and quantum theory of atoms in molecules (QTAIM) analyses. Key findings revealed that glycine interaction significantly increased the total dipole moment (TDM) and decreased the HOMO/LUMO energy gap (ΔE) for the GQD-glycine complexes. Notably, ZTRI/glycine showed a TDM of 4.535 Debye and a reduced ΔE of 0.323 eV, indicating enhanced reactivity. Further interactions with cellulose, chitosan, and sodium alginate identified the ZTRI/glycine/sodium alginate composite as the most reactive, with a TDM of 8.020 Debye and the lowest ΔE of 0.200 eV. This composite also exhibited the highest electrophilicity index (56.421) and lowest chemical hardness (0.145 eV), underscoring its superior reactivity and stability. DOS analysis revealed that biomolecules contributed the most to molecular orbitals, with carbon atoms contributing the least. QTAIM analysis confirmed the greater stability of the ZTRI/glycine/sodium alginate complex compared to other studied composites. These results highlight the enhanced reactivity and stability of GQDs when interacting with glycine and sodium alginate.

Mechanistic and thermodynamic studies on directing group-assisted copper-catalyzed α-C(sp2)−H amination of aryl amines with alkylamines

In mechanistic, three possible pathways of α-C(sp[2])−H functionalization amination of picolinamide with morpholine catalyzed by Cu(OAc)2/O2 in p-xylene were studied. Through theoretical calculations, it was found that the pathway involving the radical-cation intermediates produced by the subsequent single electron transfer (SET) was proved to be the most favorable. Then, we also discussed the solvation and substituent effects of three key steps, including N − H bond deprotonation, C − N bond formation rate-determining step and C − H bond deprotonation step, so as to fully understand the mechanism of directing group-assisted copper-catalyzed α-C − H functionalization of aniline and the screening of substrates and solvents. In thermodynamic, the SET process plays a crucial role in the entire catalytic cycle. Therefore, we further studied the reduction potential (Ered) values of Cu(II) radical-cation intermediates involved in the SET process, reflecting the occurrence trend of SET from the thermodynamic point of view, and studied the substituent effects and structure-activity relationships on Ered values of Cu(II) complexes derivatives formed by different Cu(Ⅱ) catalysts, different directing groups, different alkylamines and aromatic amines substrates. It is also found that there are many quantitative structure-activity relationships between Ered and the singly occupied molecular orbital (SOMO) energy, Hammett substituent constant σ+ and other structural parameters.

Extension of scoring-assisted generative exploration for ionic liquids (SAGE-IL) and its application to ionic liquid design for CO2 capture

Greenhouse gas emissions, particularly carbon dioxide (CO2), pose a significant threat to the climate, driving global warming and numerous environmental issues. Ionic liquids (ILs) have gained attention for CO2 capture due to their tunable properties, which can be optimized through the selection of specific cation-anion combinations. Traditional methods for discovering effective ILs are time-consuming and costly, necessitating the development of innovative computational approaches. This study extended the Scoring-Assisted Generative Exploration for Ionic Liquids (SAGE-IL) to generate novel ILs tailored for CO2 capture. SAGE-IL combines deep learning-based generative models with quantitative structure-property relationship models to design ILs with desired properties. Notably, SAGE-IL successfully performed single property optimization, either increasing or decreasing viscosity and melting point, through iterative interactions between generative and evaluation models. Furthermore, it achieved multiple property optimization, improving either CO2 solubility or activity coefficient while simultaneously optimizing density, melting point, viscosity, and synthetic accessibility. This approach not only accelerates the discovery of high-performance ILs but also highlights the potential of generative models in de novo molecular design, paving the way for future advancements in materials discovery.

AI-assisted models to predict chemotherapy drugs modified with C60 fullerene derivatives.

Employing quantitative structure-activity relationship (QSAR)/ quantitative structure-property relationship (QSPR) models, this study explores the application of fullerene derivatives as nanocarriers for breast cancer chemotherapy drugs. Isolated drugs and two drug-fullerene complexes (i.e., drug-pristine C60 fullerene and drug-carboxyfullerene C60-COOH) were investigated with the protein CXCR7 as the molecular docking target. The research involved over 30 drugs and employed Pearson's hard-soft acid-base theory and common QSAR/QSPR descriptors to build predictive models for the docking scores. Energetic descriptors were computed using quantum chemistry at the density functional-based tight binding DFTB3 level. The results indicate that drug-fullerene complexes interact more with CXCR7 than isolated drugs. Specific binding sites were identified, with varying locations for each drug complex. Predictive models, developed using multiple linear regression and IBM Watson artificial intelligence (AI), achieved mean absolute percentage errors below 12%, driven by AI-identified key variables. The predictive models included mainly quantitative descriptors collected from datasets as well as computed ones. In addition, a water-soluble fullerene was used to compare results obtained by DFTB3 with a conventional density functional theory approach. These findings promise to enhance breast cancer chemotherapy by leveraging fullerene-based drug nanocarriers.

Design, synthesis, evaluation, pharmacophore modeling, and 3D-QSAR of lappaconitine analogs as potential analgesic agents.

Alleviating pain is crucial for patients with various diseases. This study aimed to enhance the analgesic properties of lappaconitine, a natural drug, through structural modifications. Specifically, carbamate analgesic active fragments were innovatively introduced at multiple sites on the benzene ring of lappaconitine. A total of 53 lappaconitine analogs were synthesized and evaluated. Compounds 5a, 5c, 5e, 6, and 15j addressed the narrow therapeutic window of lappaconitine, enhancing drug safety. Notably, carbamate analogs exhibited significantly enhanced analgesic activity, with compounds 5a and 5c having ED50 values of 1.2 and 1.6 mg/kg, respectively, indicating higher potency than lappaconitine (3.5 mg/kg). A metabolic analysis of compound 5e was conducted in mice, revealing its primary metabolic processes and metabolites, and providing preliminary exploration for the druggability. Given the multiple analgesic targets of lappaconitine, its analgesic mechanism remains inconclusive. This study, for the first time, analyzed the pharmacological activity characteristics of the lappaconitine analogs using a pharmacophore model and established a three-dimensional quantitative structure-activity relationship (3D-QSAR) to elucidate the quantitative relationship between the structures of the synthesized compounds and their analgesic activities. These findings provide valuable guidance for future structural modification and optimization of analgesic drugs.