Molecular Descriptors Research Articles

Explainable Machine Learning Models to Predict Gibbs-Donnan Effect During Ultrafiltration and Diafiltration of High-Concentration Monoclonal Antibody Formulations.

Evaluating the Gibbs-Donnan and volume exclusion effects during protein ultrafiltration and diafiltration (UF/DF) is crucial in biopharmaceutical process development to precisely control the concentration of the drug substance in the final formulation. Understanding the interactions between formulation excipients and proteins under these conditions requires a domain-specific knowledge of molecular-level phenomena. This study developed gradient boosted tree models to predict the Gibbs-Donnan and volume exclusion effects for amino acids and therapeutic monoclonal antibodies using simple molecular descriptors. The models' predictions were interpreted by information gain and Shapley additive explanation (SHAP) values to understand the modes of action of the antibodies and excipients and to validate the models. The results translated feature effects in machine learning to real-world molecular interactions, which were cross-referenced with existing scientific literature for verification. The models were validated in pilot-scale manufacturing runs of two antibody products requiring high levels of concentration. By only requiring a molecule's biophysicochemical descriptors and process conditions, the proposed models provide an in silico alternative to conventional UF/DF experiments to accelerate process development and boost process understanding of the underlying molecular mechanisms through rational interpretation of the models.

Improvement of biodegradability prediction QSAR system through optimal combination of algorithms

Abstract This paper introduces a novel quantitative structure-activity relationship (QSAR) system for predicting the biodegradability of chemicals. The system focuses on evaluating the ratio of biochemical oxygen demand (BOD) to theoretical oxygen demand (ThOD) (%BOD/ThOD) and the presence of degradation products to categorize chemicals as “readily biodegradable” (RB) or “not readily biodegradable” (NB). The conventional laboratory testing method for biodegradability assessment is time-consuming and expensive, thus highlighting the need for a quantitative structure-activity relationship (QSAR) system that can predict biodegradability without the need for extensive testing. The authors acquired a dataset of 3948 chemicals from the National Institute of Technology and Evaluation (NITE) database, which included information on molecular structure, %BOD/ThOD values, and the presence of degradation products. The QSAR system consists of two prediction models: a %BOD/ThOD prediction model and a discrimination prediction model. Multiple algorithms were employed in each model to enhance prediction accuracy. The %BOD/ThOD prediction model utilizes eight algorithms, including linear, nonlinear, and tree-structured models. The discrimination prediction model employs ten algorithms, and the outcomes are combined using majority voting. Explanatory variables for both models include molecular descriptors and spatial structure derived from the chemical’s molecular representation. The performance of the QSAR system was evaluated based on training and validation datasets. Results showed that the combination of three %BOD/ThOD prediction algorithms improved the average correction rate compared to using all algorithms. Similarly, selecting the discrimination prediction algorithm further enhanced the average correction rate.

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.

In-Silico exploration: Unraveling the anti-cancer potential of 8-Nitroquinoline hydrazides

The proliferation of cancer has propelled the scientific community to expedite drug discovery to nullify the threats of cancers on health segment. Addressing the adverse impact of cancer on health necessitates the effective solution. The profound activity of quinoline core appended heterocyclic derivatives against cancer proliferation impacts their significance in cancer pathology. The hydrazides of 8-nitroquinoline derivatives were synthesised, and their structures were characterized through IR, NMR and single crystal XRD techniques. Initially, the theoretical calculations like green chemistry metrics and DFT studies of the 6a-c have successfully elucidated through FMO, molecular reactivity descriptors, MEP mapping approaches. Outcomes of the studies clearly indicate that the synthesised quinoline hydrazides were highly capable to interact with the biological target entities. In-Silico studies explicit the role of organic small molecules and its efficiency to be the potent drug candidates in therapeutics. Computationally the ADMET studies and the molecular docking performance of 6a-c demonstrated to expose the pharmacokinetic behaviour and binding probability assessments with 11 different oncogenic receptors. The compound 6c could be found as favourable drug molecule adhered from its drug likeness, which comply with most of the ADMET properties. Moreover notably, methyl substituted 6c exhibits its potential binding efficiency with CDK2 and PI3K macromolecules bearing -8.7 & -7.5 kcal/mol binding affinity over 6a & 6b among the selected oncogenic proteins. The insights of in-silico studies could be validated further through in-vitro and in-vivo wet lab research works.

Synthesis, DFT calculation, molecular docking and in vitro anticancer activities of sulphanilamide incorporated Schiff base metal complexes

Transition metal complexes of the type [M(L2)Cl2] (M= Co, Cu, Ni and Zn) were synthesized from Sulphanilamide based novel Schiff base ligand (4-[(oxo-1,2-diphenylethylidene)amino]-benzene-1-sulphonamide) utilizing micro-wave helped blending and characterized by expository and spectroscopic strategies. Based on the findings of the NMR and FT-IR spectroscopy, it was determined that the azomethine nitrogen and oxygen atoms from the ligand are coordinated with the metal ions that correspond to them. The physicochemical properties, drug-like nature and pharmacokinetic properties were predicted by the SwissADME Online webserver. The compound's geometry, electrostatic potential, frontier molecular orbital and quantum chemical descriptors were determined. The most appropriate drug targets were identified by using a polypharmacology approach. To evaluate the ligand and its metal complexes' interactions with the chosen breast cancer targets, molecular docking was used. The findings demonstrated that the metal complexes had good interaction profiles and binding energies. Examining the compounds' in vitro anticancer potential, the results revealed that the biological ability of each molecule to prevent breast cancer varied. In light of this, each of the compounds that were synthesized has the potential to be utilized as possible candidates for breast cancer treatment.

Predicting biological activity and design of 5-HT6 antagonists through assessment of ANN-QSAR models in the context of Alzheimer's disease.

Alzheimer's disease (AD) is the leading cause of dementia around the world, totaling about 55 million cases, with an estimated growth to 74.7 million cases in 2030, which makes its treatment widely desired. Several studies and strategies are being developed considering the main theories regarding its origin since it is not yet fully understood. Among these strategies, the 5-HT6 receptor antagonism emerges as an auspicious and viable symptomatic treatment approach for AD. The 5-HT6 receptor belongs to the G protein-coupled receptor (GPCR) family and is closely implicated in memory loss processes. As a serotonin receptor, it plays an important role in cognitive function. Consequently, targeting this receptor presents a compelling therapeutic opportunity. By employing antagonists to block its activity, the 5-HT6 receptor's functions can be effectively modulated, leading to potential improvements in cognition and memory. Addressing this challenge, our research explored a promising avenue in drug discovery for AD, employing Artificial Neural Networks-Quantitative Structure-Activity Relationship (ANN-QSAR) models. These models have demonstrated great potential in predicting the biological activity of compounds based on their molecular structures. By harnessing the capabilities of machine learning and computational chemistry, we aimed to create a systematic approach for analyzing and forecasting the activity of potential drug candidates, thus streamlining the drug discovery process. We assembled a diverse set of compounds targeting this receptor and utilized density functional theory (DFT) calculations to extract essential molecular descriptors, effectively representing the structural features of the compounds. Subsequently, these molecular descriptors served as input for training the ANN-QSAR models alongside corresponding biological activity data, enabling us to predict the potential efficacy of novel compounds as 5-hydroxytryptamine receptor 6 (5-HT6) antagonists. Through extensive analysis and validation of ANN-QSAR models, we identified eight new promising compounds with therapeutic potential against AD.

Unsupervised Machine Learning in the Analysis of Nonadiabatic Molecular Dynamics Simulation.

The all-atomic full-dimensional-level simulations of nonadiabatic molecular dynamics (NAMD) in large realistic systems has received high research interest in recent years. However, such NAMD simulations normally generate an enormous amount of time-dependent high-dimensional data, leading to a significant challenge in result analyses. Based on unsupervised machine learning (ML) methods, considerable efforts were devoted to developing novel and easy-to-use analysis tools for the identification of photoinduced reaction channels and the comprehensive understanding of complicated molecular motions in NAMD simulations. Here, we tried to survey recent advances in this field, particularly to focus on how to use unsupervised ML methods to analyze the trajectory-based NAMD simulation results. Our purpose is to offer a comprehensive discussion on several essential components of this analysis protocol, including the selection of ML methods, the construction of molecular descriptors, the establishment of analytical frameworks, their advantages and limitations, and persistent challenges.

Development of a robust Machine learning model for Ames test outcome prediction

The mutagenicity is an essential parameter for evaluating the safety of pharmaceuticals, chemicals, consumer products, environmentally related compounds and the Ames assay is a significant test for predicting the mutagenicity of chemical compounds. In the data-driven era, developing robust models for efficient mutagenicity prediction before synthesizing and testing in vitro has gained increasing attention. In this study, a machine learning model that could predict Ames mutagenicity based on 2D molecular descriptors was developed. A multistep filtering process that adequately helps in identifying significant descriptors was adopted in this study. Three different sets of descriptors, namely, RDKit, Mordred and CDK were used to train three machine learning algorithms, viz., random forest, xgboost and catboost. The datasets were collected from different resources to develop a robust machine learning model. The robustness of this model was further validated by comparing different available ML and DL models for Ames genotoxicity. Specifically, 12 models, including our xgboost model, were used to validate an external dataset, and our model exhibited excellent performance, with an impressive AUC of 0.97. The codes to predict the genotoxicity of a molecule is available at https://github.com/Naga270588/Genotoxicity.

Hybrid Class Balancing Approach for Chemical Compound Toxicity Prediction.

Computational methods are crucial for efficient and cost-effective drug toxicity prediction. Unfortunately, the data used for prediction is often imbalanced, resulting in biased models that favor the majority class. This paper proposes an approach to apply a hybrid class balancing technique and evaluate its performance on computational models for toxicity prediction in Tox21 datasets. The process begins by converting chemical compound data structures (SMILES strings) from various bioassay datasets into molecular descriptors that can be processed by algorithms. Subsequently, Undersampling and Oversampling techniques are applied in two different schemes on the training data. In the first scheme (Individual), only one balancing technique (Oversampling or Undersampling) is used. In the second scheme (Hybrid), the training data is divided according to a ratio (e.g., 90-10), applying a different balancing technique to each proportion. We considered eight resampling techniques (four Oversampling and four Undersampling), six molecular descriptors (based on MACCS, ECFP, and Mordred), and five classification models (KNN, MLP, RF, XGB and SVM) over 10 bioassay datasets to determine the configurations that yield the best performance. We defined three testing scenarios: without balancing techniques (baseline), Individual, and Hybrid. We found that using the ENN technique in the MACCS-MLP combination resulted in a 10.01% improvement in performance. The increase for ECFP6-2048 was 16.47% after incorporating a combination of the SMOTE (10%) and RUS (90%) techniques. Meanwhile, using the same combination of techniques, MORDRED-XGB showed the most significant increase in performance, achieving a 22.62% improvement. Integrating any of the class balancing schemes resulted in a minimum of 10.01% improvement in prediction performance compared to the best baseline configuration. In this study, Undersampling techniques were more appropriate due to the significant overlap among samples. By eliminating specific samples from the predominant class that are close to the minority class, this overlap is greatly reduced.

Role of GA, AG and R in Structure-Property Modelling

Molecular descriptors are essential tools that bridge the gap between chemical structures and their properties. Among these, the Randić index (R), geometric-arithmetic index (GA), and arithmetic-geometric index (AG) are well-established. However, the relationships between these descriptors, particularly the gaps GA − R and AG − R , have not been extensively explored. This study investigates the impact of these descriptor gaps on chemical graph theory. Our findings reveal that the differences GA − R and AG − R provide unique insights into the structure-property modeling of molecules, particularly outperforming AG, GA, R, and other known descriptors for alkanes. Polycyclic aromatic compounds (PACs) are organic molecules composed of multiple fused aromatic rings. PACs are of significant environmental concern due to their persistence and potential toxicity, including carcinogenic properties. The effectiveness of GA − R and AG − R is observed to be strong in structure-property modeling for some polycyclic aromatic compounds. Additionally, GA − R and AG − R are recognized as important descriptors for various pharmaceutical compounds employed in treating malignant diseases.

Advanced High-Throughput Rational Design of Porphyrin-Sensitized Solar Cells Using Interpretable Machine Learning.

Accurately predicting the power conversion efficiency (PCE) in dye-sensitized solar cells (DSSCs) represents a crucial challenge, one that is pivotal for the high throughput rational design and screening of promising dye sensitizers. This study presents precise, predictive, and interpretable machine learning (ML) models specifically designed for Zn-porphyrin-sensitized solar cells. The model leverages theoretically computable, effective, and reusable molecular descriptors (MDs) to address this challenge. The models achieve excellent performance on a "blind test" of 17 newly designed cells, with a mean absolute error (MAE) of 1.02%. Notably, 10 dyes are predicted within a 1% error margin. These results validate the ML models and their importance in exploring uncharted chemical spaces of Zn-porphyrins. SHAP analysis identifies crucial MDs that align well with experimental observations, providing valuable chemical guidelines for the rational design of dyes in DSSCs. These predictive ML models enable efficient in silico screening, significantly reducing analysis time for photovoltaic cells. Promising Zn-porphyrin-based dyes with exceptional PCE are identified, facilitating high-throughput virtual screening. The prediction tool is publicly accessible at https://ai-meta.chem.ncu.edu.tw/dsc-meta.

Analysis of Lipophilicity and Pharmacokinetic Parameters of Dipyridothiazine Dimers with Anticancer Potency.

Lipophilicity is an essential parameter of a compound that determines the solubility and pharmacokinetic properties that determine the transport of the drug to the molecular target. Dimers of dipyridothiazines are diazaphenothiazine derivatives exhibiting diverse anticancer potential in vitro, which is related to their affinity for histone deacetylase. In this study, the lipophilicity of 16 isomeric dipyridothiazine dimers was investigated theoretically and experimentally by reversed-phase thin-layer chromatography (RP-TLC) in an acetone-TRIS buffer (pH = 7.4). The relative lipophilicity parameter RM0 and specific hydrophobic surface area b were significantly intercorrelated, showing congeneric classes of dimers. The parameter RM0 was transformed into parameter logPTLC by use of the calibration curve. Molecular descriptors, ADMET parameters and probable molecular targets were determined in silico for analysis of the pharmacokinetic profile of the tested compounds showing anticancer activity. The analyzed compounds were tested in the context of Lipinski's rule of five, Ghose's rule and Veber's rule, confirming their bioavailability.

A multichannel graph neural network based on multisimilarity modality hypergraph contrastive learning for predicting unknown types of cancer biomarkers.

Identifying potential cancer biomarkers is a key task in biomedical research, providing a promising avenue for the diagnosis and treatment of human tumors and cancers. In recent years, several machine learning-based RNA-disease association prediction techniques have emerged. However, they primarily focus on modeling relationships of a single type, overlooking the importance of gaining insights into molecular behaviors from a complete regulatory network perspective and discovering biomarkers of unknown types. Furthermore, effectively handling local and global topological structural information of nodes in biological molecular regulatory graphs remains a challenge to improving biomarker prediction performance. To address these limitations, we propose a multichannel graph neural network based on multisimilarity modality hypergraph contrastive learning (MML-MGNN) for predicting unknown types of cancer biomarkers. MML-MGNN leverages multisimilarity modality hypergraph contrastive learning to delve into local associations in the regulatory network, learning diverse insights into the topological structures of multiple types of similarities, and then globally modeling the multisimilarity modalities through a multichannel graph autoencoder. By combining representations obtained from local-level associations and global-level regulatory graphs, MML-MGNN can acquire molecular feature descriptors benefiting from multitype association properties and the complete regulatory network. Experimental results on predicting three different types of cancer biomarkers demonstrate the outstanding performance of MML-MGNN. Furthermore, a case study on gastric cancer underscores the outstanding ability of MML-MGNN to gain deeper insights into molecular mechanisms in regulatory networks and prominent potential in cancer biomarker prediction.

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.

Calculated hydration free energies become less accurate with increases in molecular weight

In order for computer-aided drug design to fulfil its long held promise of delivering new medicines faster and cheaper, extensive development and validation work must be done first. This pertains particularly to molecular dynamics force fields where one important aspect–the hydration free energy (HFE) of small molecules–is often insufficiently analyzed. While most benchmarking studies report excellent accuracies of calculated hydration free energies–usually within 2 kcal/mol of experimental values–we find that deeper analysis reveals significant shortcomings. Herein, we report a dependence of HFE prediction errors on ligand molecular weight–the higher the weight, the bigger the prediction error and the higher the probability the calculated result is erroneous by a large amount. We show that in the drug-like molecular weight region, HFE predictions can easily be off by 5 kcal/mol or more. This is likely to be highly problematic in a drug discovery and development setting. We make our HFE results and molecular descriptors freely and fully available in order to encourage deeper analysis of future molecular dynamics results and facilitate development of the next generation of force fields.

Force Fields, Quantum-Mechanical- and Molecular-Dynamics-Based Descriptors of Radiometal-Chelator Complexes.

Radiopharmaceuticals are currently a key tool in cancer diagnosis and therapy. Metal-based radiopharmaceuticals are characterized by a radiometal-chelator moiety linked to a bio-vector that binds the biological target (e.g., a protein overexpressed in a particular tumor). The right match between radiometal and chelator influences the stability of the complex and the drug's efficacy. Therefore, the coupling of the radioactive element to the correct chelator requires consideration of several features of the radiometal, such as its oxidation state, ionic radius, and coordination geometry. In this work, we systematically investigated about 120 radiometal-chelator complexes taken from the Cambridge Structural Database. We considered 25 radiometals and about 30 chelators, featuring both cyclic and acyclic geometries. We used quantum mechanics methods at the density functional theoretical level to generate the general AMBER force field parameters and to perform 1 µs-long all-atom molecular dynamics simulations in explicit water solution. From these calculations, we extracted several key molecular descriptors accounting for both electronic- and dynamical-based properties. The whole workflow was carefully validated, and selected test-cases were investigated in detail. Molecular descriptors and force field parameters for the complexes considered in this study are made freely available, thus enabling their use in predictive models, molecular modelling, and molecular dynamics investigations of the interaction of compounds with macromolecular targets. Our work provides new insights in understanding the properties of radiometal-chelator complexes, with a direct impact for rational drug design of this important class of drugs.

In Silico Insights: QSAR Modeling of TBK1 Kinase Inhibitors for Enhanced Drug Discovery.

TBK1, or TANK-binding kinase 1, is an enzyme that functions as a serine/threonine protein kinase. It plays a crucial role in various cellular processes, including the innate immune response to viruses, cell proliferation, apoptosis, autophagy, and antitumor immunity. Dysregulation of TBK1 activity can lead to autoimmune diseases, neurodegenerative disorders, and cancer. Due to its central role in these critical pathways, TBK1 is a significant focus of research for therapeutic drug development. In this paper, we explore data from the CAS Content Collection regarding TBK1 and its implication in a large assortment of diseases and disorders. With the demand for developing efficient TBK1 inhibitors being outlined, we focus on utilizing a machine learning approach for developing predictive models for TBK1 inhibition, derived from the fragment-functional analysis descriptors. Using the extensive CAS Content Collection, we assembled a training set of TBK1 inhibitors with experimentally measured IC50 values. We explored several machine learning techniques combined with various molecular descriptors to derive and select the best TBK1 inhibitor QSAR models. Certain significant structural alerts that potentially contribute to inhibition of TBK1 are outlined and discussed. The merit of the article stems from identifying the most adequate TBK1 QSAR models and subsequent successful development of advanced positive training data to facilitate and enhance drug discovery for an important therapeutic target such as TBK1 inhibitors, based on an extensive, wide-ranging set of scientific information provided by the CAS Content Collection.

MolPipeline: A Python Package for Processing Molecules with RDKit in Scikit-learn.

The open-source package scikit-learn provides various machine learning algorithms and data processing tools, including the Pipeline class, which allows users to prepend custom data transformation steps to the machine learning model. We introduce the MolPipeline package, which extends this concept to cheminformatics by wrapping standard RDKit functionality, such as reading and writing SMILES strings or calculating molecular descriptors from a molecule object. We aimed to build an easy-to-use Python package to create completely automated end-to-end pipelines that scale to large data sets. Particular emphasis was put on handling erroneous instances, where resolution would require manual intervention in default pipelines. MolPipeline provides the building blocks to enable seamless integration of common cheminformatics tasks within scikit-learn's pipeline framework, such as scaffold splits and molecular standardization, making pipeline building easily adaptable to diverse project requirements.

Predicting CO2 equilibrium solubility in various amine-CO2 systems using an artificial neural network model

Three proposed reaction mechanisms can occur in an amine-CO2 system: either zwitterionic or termolecular mechanisms for primary/secondary amines and base-catalyzed hydration for tertiary amines. The intricacy of this system hinders the construction of a general model for all types of amines. This study attempts to build an artificial neural network model that predicts the equilibrium solubility of any nonblended aqueous amine-CO2 system under given operating conditions, regardless of the reaction mechanism. This is a novel approach that has not yet been reported. The amines were characterized using molecular descriptors derived from COSMO theory through density functional theory calculations to incorporate molecular structures as model features. Our model achieved performance metrics (R2) of 0.9645 and 0.9481 for the training and validation sets, respectively. For unfamiliar amines that were absent in both the training and validation sets, our model achieved an R2 of 0.8601. Model benchmarking was performed using a previously established thermodynamic model. Interpretations of the model are also provided based on the chosen features. This study also offers exploratory insight into how the molecular structure and operating conditions affect the CO2 equilibrium solubility in amines. The model developed in this study has the potential to reduce the solvent screening time in determining appropriate amines for larger-scale applications.