Molecular Descriptors Research Articles

Predicting viral proteins that evade the innate immune system: a machine learning-based immunoinformatics tool

Viral proteins that evade the host’s innate immune response play a crucial role in pathogenesis, significantly impacting viral infections and potential therapeutic strategies. Identifying these proteins through traditional methods is challenging and time-consuming due to the complexity of virus-host interactions. Leveraging advancements in computational biology, we present VirusHound-II, a novel tool that utilizes machine learning techniques to predict viral proteins evading the innate immune response with high accuracy. We evaluated a comprehensive range of machine learning models, including ensemble methods, neural networks, and support vector machines. Using a dataset of 1337 viral proteins known to evade the innate immune response (VPEINRs) and an equal number of non-VPEINRs, we employed pseudo amino acid composition as the molecular descriptor. Our methodology involved a tenfold cross-validation strategy on 80% of the data for training, followed by testing on an independent dataset comprising the remaining 20%. The random forest model demonstrated superior performance metrics, achieving 0.9290 accuracy, 0.9283 F1 score, 0.9354 precision, and 0.9213 sensitivity in the independent testing phase. These results establish VirusHound-II as an advancement in computational virology, accessible via a user-friendly web application. We anticipate that VirusHound-II will be a crucial resource for researchers, enabling the rapid and reliable prediction of viral proteins evading the innate immune response. This tool has the potential to accelerate the identification of therapeutic targets and enhance our understanding of viral evasion mechanisms, contributing to the development of more effective antiviral strategies and advancing our knowledge of virus-host interactions.

Prediction of potential antitumor components in Ganoderma lucidum: A combined approach using machine learning and molecular docking

The objective of this study is to develop a reliable predictive model for antitumour activity and to identify potential antitumour components in Ganoderma lucidum. Four machine learning models, including Random Forest, were employed to train predictive models for antitumour activity, utilising Morgan fingerprints as molecular descriptors. The most effective model was then employed to predict the chemical composition of Ganoderma lucidum, identifying the four most probable compounds for molecular docking with known TNF-α-related targets. The findings of the study indicate that a Support Vector Machine (SVM) model exhibits an accuracy, F1 score, AUC, and sensitivity of 0.7638, 0.7638, 0.8332, and 0.7621, respectively. The model demonstrated an 80 % accuracy rate in predicting the antitumour activity of 10 FDA-approved drugs. Besides, the model identified 11 components in Ganoderma lucidum, including 3-nitroanisole, with a probability of antitumour activity exceeding 0.5, indicating their potential as antitumour agents. The results of the molecular docking procedure indicated that the four most promising antitumour compounds derived from Ganoderma lucidum exhibited a favourable binding affinity with the TNF-α target. In conclusion, this study incorporated a machine learning prediction step prior to molecular docking, thereby enhancing the reliability of the latter. Furthermore, it identified previously unreported compounds in Ganoderma lucidum with potential antitumour activity, such as 3-nitroanisole.

Computer prediction of acute toxicity of thioderivatives of 3,5-bis(5-mercapto-4-R-4H-1,2,4-triazol-3-yl)phenol

This study presents the computational prediction of acute toxicity for new derivatives of 3,5-bis(5-mercapto-4-R-4H-1,2,4-triazol-3-yl)phenols and their alkylated analogues. The aim of the work was to evaluate the impact of structural changes, particularly alkylation and chain length extension, on the toxicity levels of new derivatives of 3,5-bis(5-mercapto-4-R-4H-1,2,4-triazol-3-yl)phenols and their alkylated analogues. Materials and methods. QSAR methodologies were used to predict toxicity, allowing the evaluation of toxicological properties based on molecular descriptors. Toxicity modeling was performed using computer software, enabling the estimation of toxicity without the need for in vivo experimental studies. Results. The results showed, that alkylation of 3,5-bis(5-mercapto-4-R-4H-1,2,4-triazol-3-yl)phenol derivatives does not lead to a significant toxicity reduction. Moreover, an increase in toxicity was observed with the prolongation of the carbon chain in the synthesized compounds. It was also found, that acid derivatives, particularly 2,2’-(((5-hydroxy-1,3-phenylene)bis(4-R-4H-1,2,4-triazol-5,3-diyl))bis(sulfanyl))diacetate acids and 3,3’-((((5-hydroxy-1,3-phenylene)bis(4-R-4H-1,2,4-triazol-5,3-diyl))bis(sulfanyl))bis(methylene))dibenzoyl acids, exhibit toxicity ranging from 521.9 mg/kg to 2232.2 mg/kg, corresponding to toxicity class IV (low toxicity) on the OECD scale. It was found, that molecular structure and hydrophobic properties play a crucial role in determining compound toxicity. This study helps to establish a structure-toxicity relationship and to optimize compound structures for reduced toxicity. Conclusions. These results provide a foundation for further development of potential drug candidates with predicted toxicological properties.

Exponential Wiener index of some silicate networks

Any graph that depicts a particular molecular structure can be given a topological graph index, also known as a molecular descriptor. This index can be used to examine numerical data and discover more about specific physical properties of molecules. Silicates are hard crystals that can be sliced into little pieces. Therefore, silicon play a vital role in many real time applications. It is the authentic material to make microchips which run in PDA, PC and other game tools. In the current study, we have determined the new topological index for a few silicate structures, including cyclic, single-chain, and Pyro silicates by grouping the vertices of the corresponding molecular graphs based on their distance sums. This index is known as the exponential Wiener index. It is expanded to include their line graphs as well. The numerical values of exponential Wiener index and multiplicative exponential Wiener index for the molecular graphs of cyclic silicates, single chain silicates and their line graphs with n vertices where n = 3, 4, 5 are explored.

Learning glass transition temperatures via dimensionality reduction with data from computer simulations: Polymers as the pilot case.

Machine learning methods provide an advanced means for understanding inherent patterns within large and complex datasets. Here, we employ the principal component analysis (PCA) and the diffusion map (DM) techniques to evaluate the glass transition temperature (Tg) from low-dimensional representations of all-atom molecular dynamic simulations of polylactide (PLA) and poly(3-hydroxybutyrate) (PHB). Four molecular descriptors were considered: radial distribution functions (RDFs), mean square displacements (MSDs), relative square displacements (RSDs), and dihedral angles (DAs). By applying Gaussian Mixture Models (GMMs) to analyze the PCA and DM projections and by quantifying their log-likelihoods as a density-based metric, a distinct separation into two populations corresponding to melt and glass states was revealed. This separation enabled the Tg evaluation from a cooling-induced sharp increase in the overlap between log-likelihood distributions at different temperatures. Tg values derived from the RDF and MSD descriptors using DM closely matched the standard computer simulation-based dilatometric and dynamic Tg values for both PLA and PHB models. This was not the case for PCA. The DM-transformed DA and RSDdata resulted in Tg values in agreement with experimental ones. Overall, the fusion of atomistic simulations and DMs complemented with the GMMs presents a promising framework for computing Tg and studying the glass transition in a unified way across various molecular descriptors for glass-forming materials.

Neighbourhood Sum-Based Structural Analysis for Sodalite System

Background: Sodalite is a type of zeolite with an intricate structure comprising a system of interrelated cages and tunnels. It is extensively used in sieving applications due to its unique structure and properties. As a result, it finds several uses in water and air purifica-tion, radioactive decontamination, detergents, and so on. Due to the potential positive envi-ronmental impact of sodalite materials, analysing the molecule at a structural level becomes the need of the hour. Methods: Molecular descriptors form the basis of many convenient and cost-effective tech-niques for studying molecular structures. In this article, the neighbourhood sum-based de-scriptors are computed edge-partition techniques, simplifying the intricate structures with cages and tunnels into more simple graphs for mathematical convenience. Results: This article presents the calculated analytical expressions for molecular descriptors, specifically focusing on various neighbourhood sum degree-based indices in sodalite struc-tures. Conclusion: The results presented in this article establish the dependence of the physical properties of a molecule on its underlying structure using the computed molecular de-scriptors. The graphical comparison of the results provides a visual representation of the be-haviour of indices with respect to the molecular structure.

Analysis of octane isomer properties via topological descriptors of line graphs

Currently, graph-based molecular structure descriptors are frequently used in QSAR (Quantitative Structure-Activity Relationship) and QSPR (Quantitative Structure-Property Relationship) modeling of the properties of chemical compounds. Octane isomers have an important role in the testing of such relationships. In this study, we compute some well-known topological descriptors of the molecular graphs of isomeric octanes and their line graphs, both indices and coindices. Then we analyze the correlation coefficients between the physicochemical properties of the octane isomers and some well-known topological indices and their coindices of the molecular graphs and the line molecular graphs of the octane isomers, to find the particular topological descriptors that have the best predictive values.

A simple approach to rotationally invariant machine learning of a vector quantity.

Unlike with the energy, which is a scalar property, machine learning (ML) prediction of vector or tensor properties poses the additional challenge of achieving proper invariance (covariance) with respect to molecular rotation. For the energy gradients needed in molecular dynamics (MD), this symmetry is automatically fulfilled when taking analytic derivative of the energy, which is a scalar invariant (using properly invariant molecular descriptors). However, if the properties cannot be obtained by differentiation, other appropriate methods should be applied to retain the covariance. Several approaches have been suggested to properly treat this issue. For nonadiabatic couplings and polarizabilities, for example, it was possible to construct virtual quantities from which the above tensorial properties are obtained by differentiation and thus guarantee the covariance. Another possible solution is to build the rotational equivariance into the design of a neural network employed in the model. Here, we propose a simpler alternative technique, which does not require construction of auxiliary properties or application of special equivariant ML techniques. We suggest a three-step approach, using the molecular tensor of inertia. In the first step, the molecule is rotated using the eigenvectors of this tensor to its principal axes. In the second step, the ML procedure predicts the vector property relative to this orientation, based on a training set where all vector properties were in this same coordinate system. As the third step, it remains to transform the ML estimate of the vector property back to the original orientation. This rotate-predict-rotate (RPR) procedure should thus guarantee proper covariance of a vector property and is trivially extensible also to tensors such as polarizability. The RPR procedure has an advantage that the accurate models can be trained very fast for thousands of molecular configurations, which might be beneficial where many training sets are required (e.g., in active learning). We have implemented the RPR technique, using the MLatom and Newton-X programs for ML and MD, and performed its assessment on the dipole moment along MD trajectories of 1,2-dichloroethane.

Theoretical investigation of Diels–Alder reaction mechanism and regioselectivity with functionalized fullerene derivatives

AbstractDensity functional theory (DFT) was used to study the mechanism of Diels–Alder (DA) reaction between 2‐Methyl‐ 1,3 Butadiene (MBD) with B12N12, B6N6C12 and B6C12N6 fullerens in the gas phase. In this mechanism, B‐N bonds of four‐membered and six‐membered rings of nanocages were chosen to react with MBD. Due to the existence of two types of B‐N bonds, two competitive pathways (a and b) were investigated. The effect of the substitution of carbon atoms instead of boron and nitrogen atoms on the stability of the structures and the regioselectivity of the DA reaction was examined. The potential energy of all structures and the activation barrier for all reaction pathways was evaluated. The chemical reactivity of fullerens using the molecular quantum descriptors was assessed. To evaluate the global electron density transfer in transition states (TS) structures, the natural bond orbital (NBO) analysis was performed.

Ready-to-use Models Built Using a Diverse Set of 266 Aroma Compounds for the Estimation of Gas Chromatographic Retention Indices for the 50%-Cyanopropylphenyl-50%-Dimethylpolysiloxane Stationary Phase.

Retention index prediction based on the molecule structure is not often used in practice due to low accuracy, the need to use paid software to calculate molecular descriptors (MD), and the narrow applicability domain of many models. In recent years, relatively accurate and versatile deep learning (DL)-based models have emerged. These models are now used in practice as an additional criterion in gas chromatography-mass spectrometry identification. The DB-225ms stationary phase (usually described as 50%-cyanopropylphenyl-50%-dimethylpolysiloxane in available sources) is widely used, but ready-to-use retention index estimation models are not available for it. This study presents such models. The models are linear and use simple constitutional MD and retention indices predicted by DL for the DB-WAX and DB-624 stationary phases as MD (we show that it is their use that allows us to achieve satisfactory accuracy). The accuracy obtained for a completely unseen hold-out test set: root mean square error 73.2; mean absolute error 45.7; median absolute error 22.0. The models were trained using a retention data set of 266 volatile compounds. All calculations can be performed using the convenient open-source software CHERESHNYA. The final equations are implemented as a spreadsheet and a code snippet and are available online: https://doi.org/10.6084/m9.figshare.26800789.

A machine learning and DFT assisted analysis of benzodithiophene based organic dyes for possible photovoltaic applications

We present a synergistic approach to combine Machine Learning (ML), Density Functional Theory (DFT), and molecular descriptor analysis for designing high-performance benzodithiophene (BDT) based chromophores. A dataset of 366 BDT incorporated moieties is compiled from literature while their molecular descriptors are designed by using Python programming language. Linear and Random Forest Regression models produces best results to predict their exciton binding energy (Eb) with their R-Squared (R2) value 0.87 and 0.94 respectively. Their DFT calculations provides additional features, including molecular charges. Their ML models also reveals that their Eb values are a crucial predictor for their photovoltaic (PV) performance as its lower value could facilitate efficient charge carrier separation. For this, their hydrogen bond acceptors (HBA) and topological polar surface area (TPSA) emerges as key descriptors during their regression analysis. Their DFT validation shows negligible differences in their molecular charges to suggest their electron donor/acceptor moieties can significantly impact their chromophore nature. The current research work is helpful for efficiently screening the suitability of organic chromophores for their PV applications through advanced computational tools.

Facile Synthesis of (S)‐2‐Aryl‐N‐(1‐phenylethylisonicotinamides) Derivatives via SMC Reaction: Their Thermodynamic and Spectroscopic Features via DFT Approach

AbstractIn this study, 2‐bromoisonicotinic acid and (S)‐1‐phenylethaneamine were reacted in the presence of pyridine and TiCl4 to produce 2‐bromo‐N‐(1‐phenylethyl)isonicotinamide (3). The derivatives (5a–f) of (S)‐2‐bromo‐N‐(1‐phenylethyl)isonicotinamide were then created by a Suzuki–Miyaura cross‐coupling process catalyzed by palladium (0) and K3PO4 as a base. The reactivity and electronic features of the compounds were investigated using DFT molecular electrostatic potential investigations, reactivity descriptors, and frontier molecular orbital analysis.

Prediction of Melting and Solid Phase Transitions Temperatures and Enthalpies for Triacylglycerols using Artificial Neural Networks

Triacylglycerols (TAG) are the main components of vegetable oils and any attempt to simulate vegetable oils processes will demand knowledge of their properties. However, experimental values are scarce, considering that several TAG in their pure forms are unavailable or too expensive for experimental measurements. On the other hand, correlating physical properties with TAG molecular structure is not simple. TAG is a molecule composed of 3 fatty acids (FA) esterified to a glycerol (GL) backbone, making properties dependent on carbon number (CN) of each FA, number of unsaturations (UN) of each FA, and position of the FA in the GL backbone. Few models are available in literature for prediction of TAG melting properties, with a special attention to melting temperature (Tfus) and enthalpy (ΔHfus) and solid-solid transition properties of the TAG polymorphic forms. Wesdorp's, Moorthy's et al. and Zeberg-Mikkelsen and Stenby's works present models based on the Group-Contribution theory nowadays used, despite some flaws, particularly considering the polymorphic transitions. Therefore, this work was aimed at evaluating Artificial Neural Network (ANN) models for prediction of TAG's Tfus and ΔHfus (β-form) as well as temperature and enthalpy transitions of molecule polymorphic forms (α and β’). Database was composed of temperature and enthalpy experimental data from literature. For each TAG, 7 input data were provided: total CN, as well as CN and UN at sn-1, 2 and 3 TAG position. The Multilayer Perceptron Feed Forward (MPL) model was used, and the topology was evaluated for number of hidden layers (HL), number of neurons (NN) and activation function at each hidden layer, and convergence algorithm. Number of HL and NN was screened by using a Central Composite Rotatable Design (CCRD). Models were further evaluated by Explainable Artificial Intelligence (XAI) and feature evaluation strategies. Architectures showed a significant higher accuracy for calculation and prediction of TAG's melting properties of the 3 polymorphic forms, with R2 higher to 0.91 for all databases when compared to literatures’ models (excepted for the prediction of the melting temperature of the β form, where Wesdorp's model presented a better predictive ability, despite great similarity). Good results were probably related to the well-defined physicochemical relationship between input (molecular structure descriptors) and output (melting properties), that could be described by XAI evaluation. This is an important advantage considering the improvement of the performance of process and products design including TAG molecules.

Prediction of acetylene solubility by a mechanism-data hybrid-driven machine learning model constructed based on COSMO-RS theory

In this work, a mechanism-data hybrid-driven machine learning model is built to predict acetylene solubility from COSMO-derived molecular descriptors, overcoming the challenge of limited training samples. We have successfully enhanced the data-learning capabilities of the model by mechanism-level modelling, and generated new mechanism-level understanding from the results of data learning. On the basis of the mechanism-level understanding of molecular interactions, this model gives more accurate predictions than the baseline models, even though it contains only 37 model parameters. Meanwhile, it presents better generalization to predict solubility in solvents beyond the training sample. Furthermore, by analyzing the implication of the parameters of this model, we find a charged segment interaction energy form different from that described by the classic COSMO-RS theory. This work not only produces available acetylene solubility data, but also presents a proposed framework to build a mechanism-data hybrid-driven model for thermodynamic properties and discovering interpretable chemical laws from small training samples.

Exploring blood-brain barrier passage using atomic weighted vector and machine learning.

This study investigates the potential of leveraging molecular properties, as determined by MD-LOVIs software and machine learning techniques, to predict the ability of compounds to cross the blood-brain barrier (BBB). Accurate prediction of BBB permeation is critical for the development of central nervous system (CNS) drugs. The study applies various machine learning models, including both classification and regression techniques, to predict BBB passage and molecular activity. Notably, classification models such as GBM-AWV (accuracy = 0.801), GLM-CN (accuracy = 0.808), SVMPoly-means (accuracy = 0.980), SVMPoly-AC (accuracy = 0.980), SVMPoly-MI_TI_SI (accuracy = 0.900), SVMPoly-GI (accuracy = 0.900), RF-means (accuracy = 0.870), and GLM-means (accuracy = 0.818) demonstrate high accuracy in predicting BBB passage. In contrast, regression models like ES-RLM-AG (R2 = 0.902), IB-IBK (R2 = 0.82), IB-Kstar (R2 = 0.834), IB-MLP (R2 = 0.843), and DRF-AWV (R2 = 0.810) exhibit strong performance in predicting molecular activity. The results show that classification models like GBM-AWV, GLM-CN, and SVMPoly variants, as well as regression models like ES-RLM-AG and IB-MLP, achieve high performance, demonstrating the effectiveness of machine learning in predicting BBB permeability. The computational methods employed in this study include the MD-LOVIs software for generating molecular descriptors and several machine learning algorithms, including gradient boosting machines (GBM), generalized linear models (GLM), support vector machines (SVM) with polynomial kernels, random forests (RF), ensemble regression models, and instance-based learning algorithms. These models were trained and validated using various datasets to predict BBB passage and molecular activity, with the performance metrics reported for each model. Standard computational techniques were employed throughout, ensuring the reliability of the predictions.

New insight into molecular interactions of surface-active ionic liquid (SAIL) with some biomolecules: Experimental and computational approaches

Understanding the influence of ionic liquids (ILs) on the solubility of biomolecules in aqueous solutions is crucial for designing and optimizing novel biotechnological processes. However, the molecular-level mechanisms underlying this influence remain inconclusive and not fully elucidated. To contribute toward the understanding of molecular interactions between amino acids and ionic liquid in aqueous media, measurements of the densities and speeds of sound for L-serine and glycyl-L-serine in (0.00, 0.005, 0.01, 0.03, and 0.05) mol·kg−1 aqueous solutions of 1-octyl-3-methylimidazolium bromide were conducted at T = (288.15, 298.15, 308.15, and 318.15) K. From experimental data various thermodynamics paramours such as apparent molar volume (Vϕ), the partial molar volume (Vϕ0), standard partial molar volumes of transfer (ΔVϕ0) partial molar isentropic compression (Kϕ,s) and partial molar isentropic compression of transfer (ΔKϕ,S0) have been examined. Along with experiment results, computational tools were also utilized for a deeper understanding of the molecular changes. From density functional theory (DFT), the highest occupied molecular orbital (HOMO) and lowest unoccupied molecular orbital (LUMO) were calculated and utilized to obtain molecular descriptors such as ionization energy (I), electron affinity (A), hardness (η), softness (S), chemical potential (μ), and electronegativity (χ). Thermochemical properties, including change in enthalpy (ΔH), and change in Gibbs free energy (ΔG), were predicted. Molecular docking studies were used to analysis the molecular interaction of ionic liquid with I-Motif structure and structural changes.

Beyond the Arbitrariness of Drug-Likeness Rules: Rough Set Theory and Decision Rules in the Service of Drug Design

Lipinski’s Rule of Five and Ghose filter are empirical guidelines for evaluating the drug-likeness of a compound, suggesting that orally active drugs typically fall within specific ranges for molecular descriptors such as hydrogen bond donors and acceptors, weight, and lipophilicity. We revisit these practices and offer a more analytical perspective using the Dominance-based Rough Set Approach (DRSA). By analyzing representative samples of drug and non-drug molecules and focusing on the same molecular descriptors, we derived decision rules capable of distinguishing between these two classes systematically and reproducibly. This way, we reduced human bias and enabled efficient knowledge extraction from available data. The performance of the DRSA model was rigorously validated against traditional rules and available machine learning (ML) approaches, showing a significant improvement over empirical rules while achieving comparable predictive accuracy to more complex ML methods. Our rules remain simple and interpretable while being characterized by high sensitivity and specificity.

Predicting variable-length ACE inhibitory peptides based on graph convolutional network

Traditional molecular descriptors have contributed to the prediction of angiotensin I-converting enzyme (ACE) inhibitory peptides, but they often fall short in capturing the complex structure of the molecule. To address these limitations, this study introduces molecular graphs as an advanced method for peptide characterization. Peptides containing 2–10 amino acids were represented using molecular graphs, and a graph convolutional network (GCN) model was constructed to predict variable-length peptides. This model was compared with machine learning (ML) models based on molecular descriptors, including Random Forest (RF), Support Vector Machine (SVM), and k-Nearest Neighbor (kNN), under the same benchmark. Notably, the GCN model outperformed the other models with an accuracy of 0.78, effectively identifying ACE inhibitory potential. Furthermore, the GCN model also demonstrated superior performance, exceeding existing methods with an accuracy rate of over 98 % on an independent test set. To validate our predictions, we synthesized peptides VAPE and AQQKEP with high predicted probabilities, and their IC50 values of 2.25 ± 0.11 and 3.75 ± 0.17 μM, respectively, indicating potent ACE inhibitory activity. The developed GCN model presents a powerful tool for the rapid screening and identification of ACE inhibitory peptides, offering promising opportunities for developing antihypertensive components in functional foods.

Exploring the Influence of Ionic Liquid Anion Structure on Gas-Ionic Liquid Partition Coefficients of Organic Solutes Using Machine Learning.

This article presents an in-depth investigation into the influence of anionic structures of ionic liquids (ILs) on gas-ionic liquid partition coefficients (log K) of organic solutes in three ILs. While the primary objective was to examine whether there is a relationship between the molecular structure of the IL anion component and log K, additionally it was looked at whether the molecular descriptors of the anion in the relationships encode possible molecular interactions during the miscibility and partitioning in the IL. The research involves the compilation of data series of experimental log K values, where the cation component is constant. Such representative data series were obtained for three solutes─benzene, cyclohexane, and methanol─in three ILs with a uniform cationic component of methylimidazoliums. Using multiple linear regression models enhanced with machine learning techniques, the relationship between anionic structures and log K values was successfully quantified and modeled. Systematically selected molecular descriptors describing the anion structure show that in the case of methanol log K is strongly dependent on hydrogen bonds and Coulomb-dipolar interactions with the anion component, while in the case of benzene and cyclohexane the dispersion forces of the anion component are dominant. The outlier analysis and data interpretation highlight the need for extensive experimental data. The results confirm the initial hypothesis and provide valuable information on the role of the structure of the anionic component in determining the partitioning behavior of organic solutes. This knowledge is important for the design and optimization of ILs for specific applications, particularly as solvents in various industrial processes. The research also provides useful information about molecular interactions taking place in the interfaces of IL and organic additives in complex liquid media such as multicomponent electrolyte solutions, for example in energy storage applications.

Synthesis, Characterization, and Molecular Docking of Novel Isatin-thiosemicarbazone containing 1,2,3-triazole Derivatives as Potential Anti-cancer Agents

Abstract: To improve the activity of isatin-1,2,3-triazole hybrids as anticancer agents, new derivatives of isatin- thiosemicarbazone-1,2,3-triazoles 9-16 were designed and synthesized via the condensation of isatin-1,2,3- triazole hybrids 1-8 with thiosemicarbazide. Spectral and elemental analysis confirmed the structure of the prepared derivatives 9-16. Also, as anticancer agents, the latter derivatives were screened against three human cancerous cell lines: human lung fibroblast (WI38), colorectal carcinoma colon cancer (HCT-116), and mammary gland breast cancer (MCF-7). Doxorubicin, a standard control, was used to compare viable cell percentages and IC50 values. In general, derivatives 12 and 16 revealed a higher potency against the three human cancerous cell lines. Finally, the molecular descriptors of compounds 12 and 16 were correlated with their observed cytotoxicity. result: In general, derivatives 12 and 16 revealed a higher potency against the three human cancerous cell lines.