Computer-aided Molecular Design Research Articles

Integrating machine learning model and computer-aided molecular design toward rational ionic liquid selection for separating fluorinated refrigerants

To reduce emissions and facilitate the reclamation of hydrofluorocarbons (HFCs), which are potent greenhouse gases, ionic liquid (ILs)-based absorption separation of HFCs from fluorinated refrigerant (FR) blends is considered as an effective approach. In this work, computer-aided ionic liquid design (CAILD) is carried out to rationally design ILs for separating FR blends, with the near-azeotropic R-32/R-125 system as an illustrative case study. Firstly, the machine learning algorithm, namely artificial neural network (ANN), is employed to develop a group contribution (GC) model for accurate prediction of FR-in-IL solubility. Then, a mixed-integer nonlinear programming (MINLP) problem is formulated and solved by integrating the ANN-GC model with two available GC models for predicting the melting point and viscosity of ILs. Consequently, the optimal three ILs including [C1C10Im][dca], [C1C11Im][dca], and [C1C12Im][dca] are identified. Finally, quantum chemistry calculations are conducted to disclose the separation mechanism from molecular perspective, further validating the reliability of the CAILD results.

Computer-aided molecular modeling and design studies of some N-(4-(dimethyl amino) phenyl)-4-methoxy-3-propionamidobenzamide derivatives as NS5B polymerase inhibitors

Hepatitis C Virus (HCV) is a stealthy ailment, and most people are unaware they have it in the early stages. Insilco modeling was used to develop a model with high predictive power for N-(4-(dimethyl amino) phenyl)-4-methoxy-3-propionamidobenzamide inhibitors. The inhibitors were optimized using Density Functional Theory (DFT) based on a notion of B3LYP/6–31 G* levels. The genetic function algorithm (GFA) was utilized to create the QSAR models. The best model was chosen based on its statistical fitness using the subsequent measurement parameters: R2trng = 0.828181, R2adj = 0.800468, Q2cv = 0.734795, R2test = 0.7028, and LOF = 0.613985, led to its selection and publication. The model underwent additional assessments, including the Y-scrambling test, and applicability domain, and was considered to have statistical significance. The docking results demonstrate that compound 17 has the greatest inhibitory effect on NS5B-polymerase of any drug in the dataset. Using this compound as a template, novel anti-hepatitis C compounds were developed by including a few beneficial substituents. The docking results of the proposed compounds yielded a high MolDock score (−152.12), indicating effective binding to the target's active sites. Two comparable compounds (SBD4 and SBD3) were shown to be superior to the food drug administration (FDA) approved medication, revibirin/pegylated interferon. The chemicals were determined to be orally accessible after passing Lipinski's criteria, a drug similarity test. Furthermore, the synthesized compounds were found to have favorable pharmacokinetic (absorption, distribution, metabolism, excretion, and toxicity) properties. In conclusion, using the QSAR model, a model with excellent prediction power for certain Biaryl amide inhibitors was constructed. The model was validated and determined to have good internal and external assessment criteria. According to the docking results, compound 17 inhibits NS5B-polymerase the most out of the compounds in the dataset. Furthermore, the compounds' oral bioavailability met drug-like standards. The ultimate goal of this research is to contribute to the development of new therapeutic agents for the treatment of HCV infections, addressing the pressing need for effective antiviral medications.

High-temperature heat pump working fluid development based on molecular design and the group contribution method

High-temperature heat pumps are widely recognized as a critical approach for decarbonizing energy-intensive industries, and their performance is significantly influenced by the working fluids. However, conventional working fluid selection methods primarily focus on existing fluids, limiting the selection scale. Therefore, a group-contribution-based computer-aided molecular design method has been proposed to design and select working fluids for high-temperature heat pumps on a large scale, comprehensively considering environmental impact, safety, operating reliability and performance. A total of 9771 working fluids were generated by the computer-aided process based on the backtracking searching algorithm. Under different working conditions, 208, 232, 243, 228, and 208 working fluids have passed the multi-level screening process based on the group contribution method, respectively, with an overall passing rate below 2.49 %. The H(C)FOs among the top 50 working fluids, ranked by the coefficient of performance, were further analyzed. Statistically, 92.4 % and 98.73 % of the total top 50 working fluids had their Tbr and Tc values within the ranges of 0.640–0.675 and 432–480 K, respectively. Among the 18 HCFOs within the top 50 list, 83.3 % were non-existent. R1251zd and R1344zf demonstrated the best ranking results, while R1242zf, R1233yf, and R1233xf exhibited superior working condition flexibility. As an existing HCFO, R1251yd outperformed other working fluids. Of the 35 HFOs in the top 50 list, 88.57 % were non-existent, and R1381myz, R1371my and R1381mzz showed both exceptional performance and superior working condition adaptability. In conclusion, there remain promising prospects and a notable necessity for the new H(C)FO development, and guidance can be expected from this study in this regard.

Estimating the synthetic accessibility of molecules with building block and reaction-aware SAScore

Synthetic accessibility prediction is a task to estimate how easily a given molecule might be synthesizable in the laboratory, playing a crucial role in computer-aided molecular design. Although synthesis planning programs can determine synthesis routes, their slow processing times make them impractical for large-scale molecule screening. On the other hand, existing rapid synthesis accessibility estimation methods offer speed but typically lack integration with actual synthesis routes and building block information. In this work, we introduce BR-SAScore, an enhanced version of SAScore that integrates the available building block information (B) and reaction knowledge (R) from synthesis planning programs into the scoring process. In particular, we differentiate fragments inherent in building blocks and fragments to be derived from synthesis (reactions) when scoring synthetic accessibility. Compared to existing methods, our experimental findings demonstrate that BR-SAScore offers more accurate and precise identification of a molecule's synthetic accessibility by the synthesis planning program with a fast calculation time. Moreover, we illustrate how BR-SAScore provides chemically interpretable results, aligning with the capability of the synthesis planning program embedded with the same reaction knowledge and available building blocks.Scientific contributionWe introduce BR-SAScore, an extension of SAScore, to estimate the synthetic accessibility of molecules by leveraging known building-block and reactivity information. In our experiments, BR-SAScore shows superior prediction performance on predicting molecule synthetic accessibility compared to previous methods, including SAScore and deep-learning models, while requiring significantly less computation time. In addition, we show that BR-SAScore is able to precisely identify the chemical fragment contributing to the synthetic infeasibility, holding great potential for future molecule synthesizability optimization.

Stable diesel and bio-oil emulsion through solvent extraction and ultrasonic aided emulsification

Fast pyrolysis bio-oil is considered a highly preferable supplement to conventional fossil fuels. However, the direct application of pyrolysis bio-oil is limited due to poor fuel properties. Emulsification of bio-oil with diesel using a solvent is considered a relatively straightforward technique for utilizing bio-oil directly with diesel. In this study, new solvents were studied with the aid of sonication to produce a stable emulsion of diesel and bio-oil. 2-Octanol, 2-Heptanol and 2-Octanone were selected as solvents based on computer-aided molecular design (CAMD) method. Bio-oil was blended with 5, 10, 15 and 20 wt% of 2-Octanol using sonication. The solvent-oil blends with different solvents (2-Heptanol and 2-Octanone) at 20 wt% were also investigated for extracting organics from bio-oil. Subsequently, sonication-assisted emulsification of the solvent-extracted bio-oil with diesel was conducted with the aid of a composite surfactant with a hydrophilic-lipophilic balance (HLB) value of 7. Extraction of bio-oil with 20 wt% of 2-Octanol provided a maximum organic yield of 37.9 ± 1.3 % with a Higher Heating Value (HHV) of 31.9 MJ/kg. The desired ratio for emulsification was found to be 80: 15: 5 (diesel: surfactant: solvent-extracted bio-oil). The emulsion samples showed promising quality with an HHV of 44.0 MJ/kg and a pH of 8.0 ± 0.1. Moreover, no phase separation was observed even after the accelerated ageing process at 80 ºC for 24 hours. The results showed that all three solvents had an overall positive effect on the extraction of organics from bio-oil and aided in emulsification with diesel in which 2-Octanol was the most effective solvent for achieving a stable diesel – bio-oil emulsion.

Task-Similarity is a Crucial Factor for Few-Shot Meta-Learning of Structure-Activity Relationships.

Machine learning models support computer-aided molecular design and compound optimization. However, the initial phases of drug discovery often face a scarcity of training data for these models. Meta-learning has emerged as a potentially promising strategy, harnessing the wealth of structure-activity data available for known targets to facilitate efficient few-shot model training for the specific target of interest. In this study, we assessed the effectiveness of two different meta-learning methods, namely model-agnostic meta-learning (MAML) and adaptive deep kernel fitting (ADKF), specifically in the regression setting. We investigated how factors such as dataset size and the similarity of training tasks impact predictability. The results indicate that ADKF significantly outperformed both MAML and a single-task baseline model on the inhibition data. However, the performance of ADKF varied across different test tasks. Our findings suggest that considerable enhancements in performance can be anticipated primarily when the task of interest is similar to the tasks incorporated in the meta-learning process.

Computer-aided framework for the design of entrainer for palm process residue recovery

Oleochemical industry produced substantial palm process residue during hydrogenation process of fatty acid/methyl ester, containing medium- to long-chain fatty alcohols and alkanes with overlapping boiling points. Recovering fatty alcohols for sale and utilizing alkanes for jet fuel, lubricants, and gasoline is beneficial. However, separating them through conventional distillation (CD) is challenging and expensive due to the difficulty in achieving the necessary temperature differentials for phase changes, especially with high reflux ratio required. To address this challenge, extractive distillation (ED) with an entrainer, designed through computer-aided molecular design (CAMD), was employed. However, implementing ED with a high boiling entrainer is challenging due to the high boiling point of the target components. Therefore, the design of the entrainer focused on the relative volatilities (α) of involved components and associated utility costs. Then, Aspen HYSYS was employed to evaluate the performance of the designed entrainer through product purity and economic analysis. For the separation of C10OH and n-C12, octane and dimethylcyclohexane were generated as entrainers. Dimethylcyclohexane entrainer demonstrated superior separation and generated higher profit than octane entrainer. For the separation of C12OH and n-C14, n-C15 was generated as entrainer to replace the infeasible CD. Therefore, the study concluded that ED with tailored entrainer can effectively replace CD for separating fatty alcohols from alkanes, demonstrating significant industrial feasibility.

Computer-Aided Design of Novel Active Components in Plant Protection

The production demands highly specific environmentally and toxicologically acceptable plant protection products are increasing. Computer-aided molecular design of new active components has a great deal in developing plant protection products to avoid that long-lasting and expensive process. Computational design of future compounds and their synthesis, evaluation of their effectiveness on harmful and beneficial organisms in the soil, as well as detailed research mechanism of action at the molecular level, represents an initial stage in the long-lasting and expensive process of plant protection products. In this paper, the recent advances in quantitative structure-activity relationship (QSAR) studies, molecular docking, and calculation of “Pesticidelikeness properties “, as well, have been reviewed. QSAR models for antifungal activities against phytopathological fungi were obtained for the thiazoline and coumarine derivatives, coumarinyl Schiff bases, and coumarin-1,2,4-triazoles. A molecular docking study revealed that antifungal activities of fluorinated pyrazole aldehydes are related to the inhibition of proteinase K, coumarinyl Schiff bases with endoglucanase and pectinase, hybrids of coumarins and 1,2,4-triazoles with sterol 14α-demethylase inhibition, 3-gydroxycoumarin chitin synthase, while γ-thionins strongly binds to fungal membrane moieties.

Molecular structure characterization of coal with different coalification degrees: a combined study from FT-IR, Raman, 13C NMR spectroscopy and modelling

While extensive research has been conducted on the evolution of coal's molecular structure during coalification, the heterogeneity and complexity of coal continue to impede the accurate and precise quantitative characterization of its structural evolution mechanism. In this study, the organic matter source, coal quality, carbon skeleton structure, and functional group occurrence of coal at different coalification stages have been determined using maceral identification, vitrinite reflectance, industrial quality, elemental analysis, and spectral analysis. Four molecular models have been created through computer-aided molecular design. The results indicate that the hydrogen atoms in the benzene ring structure reduce as coalification increases. The content of aliphatic compounds gradually decreases, while the length of aliphatic side chains first decreases (Ro,max<1.81%) and then increases (Ro,max>1.81%). Moreover, the chemical structure of coal tends to mature and stabilize, as the degree of coal crystallization increases, and the oxygen-containing groups gradually decrease. Therefore, aromatization increases with the condensation reaction dominating in the late stage of coalification. After intensive attempts to determine the chemical bond mode between aromatic carbon, aliphatic side chain, and oxygen functional group, the final molecular structure models are presented as C188H165O13N3 (F-1), C182H161O9N5 (J-1), C195H153O10N3 (T-1), and C193H129O7N3 (W-1). Furthermore, the mechanism of coalification is discussed based on the variation of the side chains and chemical bonds. According to the adsorption heat of functional groups, the methane adsorption capacity increases as the degree of coalification increases. The inflection point of methane adsorption capacity is at Ro,max>1.80%. This study provides a theoretical framework to understand the molecular structure evolution of coals at varying degrees of metamorphism. Additionally, it proposes a reliable method for characterizing coal molecular structures with high accuracy.

Augmenting optimization-based molecular design with graph neural networks

Computer-aided molecular design (CAMD) studies quantitative structure–property relationships and discovers desired molecules using optimization algorithms. With the emergence of machine learning models, CAMD score functions may be replaced by various surrogates to automatically learn the structure–property relationships. Due to their outstanding performance on graph domains, graph neural networks (GNNs) have recently appeared frequently in CAMD. But using GNNs introduces new optimization challenges. This paper formulates GNNs using mixed-integer programming and then integrates this GNN formulation into the optimization and machine learning toolkit OMLT. To characterize and formulate molecules, we inherit the well-established mixed-integer optimization formulation for CAMD and propose symmetry-breaking constraints to remove symmetric solutions caused by graph isomorphism. In two case studies, we investigate fragment-based odorant molecular design with more practical requirements to test the compatibility and performance of our approaches.

Mixed-integer optimisation of graph neural networks for computer-aided molecular design

ReLU neural networks have been modelled as constraints in mixed integer linear programming (MILP), enabling surrogate-based optimisation in various domains and efficient solution of machine learning certification problems. However, previous works are mostly limited to MLPs. Graph neural networks (GNNs) can learn from non-euclidean data structures such as molecular structures efficiently and are thus highly relevant to computer-aided molecular design (CAMD). We propose a bilinear formulation for ReLU Graph Convolutional Neural Networks and a MILP formulation for ReLU GraphSAGE models. These formulations enable solving optimisation problems with trained GNNs embedded to global optimality. We apply our optimisation approach to an illustrative CAMD case study where the formulations of the trained GNNs are used to design molecules with optimal boiling points.

Computer-aided molecular design and optimization of potent inhibitors disrupting APC‒Asef interaction

Colorectal cancer (CRC) is the second leading cause of cancer mortality worldwide. At initial diagnosis, approximately 20% of patients are diagnosed with metastatic CRC (mCRC). Although the APC‒Asef interaction is a well-established target for mCRC therapy, the discovery and development of effective and safe drugs for mCRC patients remains an urgent and challenging endeavor. In this study, we identified a novel structural scaffold based on MAI inhibitors, the first-in-class APC‒Asef inhibitors we reported previously. ONIOM model-driven optimizations of the N-terminal cap and experimental evaluations of inhibitory activity were performed, and 24-fold greater potency was obtained with the best inhibitor compared to the parental compound. In addition, the cocrystal structure validated that the two-layer π‒π stacking interactions were essential for inhibitor stabilization in the bound state. Furthermore, in vitro and in vivo studies have demonstrated that novel inhibitors suppressed lung metastasis in CRC by disrupting the APC‒Asef interaction. These results provide an intrinsic structural basis to further explore drug-like molecules for APC‒Asef-mediated CRC therapy.

Features and insights for molecular structure of Chinese Taixi anthracite at atomic scales

This work presents insights into the molecular structure of Chinese Taixi anthracite (TXA) at atomic scales. The comprehensive analysis of materials characterizations and data fitting & calculations are performed to reveal microscopic characteristics and structural parameters of TXA. Then the planar and three-dimensional molecular structure models of TXA are con with the help of computer-aided molecular design and multi-scale molecular simulation calculations. The chemical structure characteristics of TXA are systematically analyzed, such as the carbon framework, main functional groups, and typical chemical bonding features. It is found that aromatic carbons are the main skeleton of organic matter, combined with some aliphatic carbons mainly in form of methyl, methylene and methine. Conceptualized single molecular description of TXA is determined to be C763H397O21N8. Representative two-dimensional and three-dimensional molecular structural models are also constructed to describe the apparent molecular structural features of TXA. Finally, molecular structural models are further verified by the elemental composition, nuclear magnetic resonance, Fourier transform infrared, and bond concentration of TXA. In addition, we summarized microstructure features of coal with various metamorphism. This work could provide theoretical guidance for understanding the microstructure of TXA and building the correlation between coal structure and functional properties to achieve efficient utilization of coal resources.

Computer-aided molecular refrigerant design for adsorption chillers based on classical density functional theory and PC-SAFT

Adsorption chillers are a promising technology for sustainable cooling. The performance of adsorption chillers is highly influenced by the selection of the refrigerant. Still, systematic selection of refrigerants is challenging because evaluating adsorption properties requires significant experimental or simulation efforts. Thus, refrigerant design options are often limited.Here, we propose a systematic refrigerant design method for adsorption chillers assessing refrigerants based on their process performance. Our method quantifies adsorption isotherms by 1-dimensional classical density functional theory (DFT) based on the PC-SAFT equation of state. The method is used to screen over 1800 refrigerants based on their coefficient of performance (COP). We identify refrigerants with higher COPs than commonly used refrigerants and highlight the advantage of a process-based objective function over material-based heuristic selection criteria. Finally, we advance from screening to computer-aided molecular design of the refrigerant, leveraging the efficiency of the DFT model to explore the molecular design space systematically.

Rationally engineered IR-783 octanoate as an enzyme-activatable fluorogenic tool for functional imaging of hNotum in living systems

As a vital negative regulator of Wnt signaling pathway, human Notum (hNotum) plays a crucial regulatory role in the progression of many human diseases. Deciphering the relevance of hNotum to human diseases requires practical and reliable tools for visualizing hNotum activity in living systems. Herein, an enzyme-activatable fluorogenic tool (IR-783 octanoate) was rationally engineered for sensing and imaging hNotum activity in living systems by integrating computer-aided molecular design and biochemical assays. IR-783 octanoate showed good optical properties, excellent specificity and high binding-affinity towards hNotum (Km = 0.98 µmol/L). IR-783 octanoate could be well up-taken into the cancerous cells or tumors that over-expressed organic anion transporting polypeptides (OATPs), and then hydrolyzed by cellular hNotum to release free IR-783 ketone, which created brightly fluorescent signals around 646 nm. Further investigations showed that IR-783 octanoate achieved a good performance for in-situ functional imaging of hNotum in both living cells, cancerous tissues and organs. It was also found that some SW620 cells with multipolar spindles could be stained by IR-783 octanoate to emit extremely bright signals, suggesting that this agent could be used as a novel visualizing tool for tracing the cells undergoing abnormal cell mitoses. Collectively, this study devises a highly specific fluorogenic tool for in-situ functional imaging of hNotum in living systems, which offers a practical and reliable tool to dynamically track the changes in hNotum activity under various conditions.

Prediction of stability constants of metal-ligand complexes by machine learning for the design of ligands with optimal metal ion selectivity.

The new LOGKPREDICT program integrates HostDesigner molecular design software with the machine learning (ML) program Chemprop. By supplying HostDesigner with predicted logK values, LOGKPREDICT enhances the computer-aided molecular design process by ranking ligands directly by metal-ligand binding strength. Harnessing reliable experimental data from a historic National Institute of Standards and Technology (NIST) database and data from the International Union of Pure and Applied Chemistry (IUPAC), we train message passing neural net algorithms. The multi-metal NIST-based ML model has a root mean square error (RMSE) of 0.629 ± 0.044 (R2 of 0.960 ± 0.006), while two versions of lanthanide-only IUPAC-based ML models have, respectively, RMSE of 0.764 ± 0.073 (R2 of 0.976 ± 0.005) and 0.757 ± 0.071 (R2 of 0.959 ± 0.007). For relative logK predictions on an out-of-sample set of six ligands, demonstrating metal ion selectivity, the RMSE value reaches a commendably low 0.25. We showcase the use of LOGKPREDICT in identifying ligands with high selectivity for lanthanides in aqueous solutions, a finding supported by recent experimental evidence. We also predict new ligands yet to be verified experimentally. Therefore, our ML models implemented through LOGKPREDICT and interfaced with the ligand design software HostDesigner pave the way for designing new ligands with predetermined selectivity for competing metal ions in an aqueous solution.

Biological effects and mechanisms of dietary chalcones: latest research progress, future research strategies, and challenges.

Dietary plants are an indispensable part of the human diet, and the various natural active compounds they contain, especially polyphenols, polysaccharides, and amino acids, have always been a hot topic of research among nutritionists. As precursors to polyphenolic substances in dietary plants, chalcones are not only widely distributed but also possess a variety of biological activities due to their unique structure. However, there has not yet been a comprehensive article summarizing the biological activities and mechanisms of dietary chalcones. This review began by discussing the dietary sources and bioavailability of chalcones, providing a comprehensive description of their biological activities and mechanisms of action in antioxidation, anti-inflammation, anti-tumor, and resistance to pathogenic microbes. Additionally, based on the latest research findings, some future research strategies and challenges for dietary chalcones have been proposed, including computer-aided design and molecular docking, targeted biosynthesis and derivative design, interactions between the gut microbiota and chalcones, as well as clinical research. It is expected that this review will contribute to supplementing the scientific understanding of dietary chalcones and promoting their practical application and the development of new food products.

Computer‐aided design of insect‐repellent formulation with inherent safety assessment

AbstractInsect repellents are one of the most effective ways to prevent the spread of diseases such as dengue, malaria, and yellow fever. However, most insect repellents contain ingredients that pose a significant risk to consumers. Therefore, this research aims to design a safer and less harmful insect‐repellent formulation based on the formulated product design methodology. Computer‐aided molecular design (CAMD) methodology was employed to design an insect‐repellent solvent that exhibits minimum safety and health risks. The safety and health hazards of all the selected ingredients were then assessed using an inherent safety and health index known as the Product Ingredient Safety and Health Index (PISHI). As a result, the proposed insect‐repellent formulation with minimum safety and health risks may consist of picaridin, 1,5‐pentanediol, and linalool. This research contributed to an inherently safer formulation design, where the identification and elimination of hazardous ingredients has been done at the early design stage. The safer and less harmful ingredients used in the insect‐repellent formulation may reduce the significant risk to consumers.

Computer-Aided Molecular Design of Ionic Liquids as Advanced Process Media: A Review from Fundamentals to Applications.

The unique physicochemical properties, flexible structural tunability, and giant chemical space of ionic liquids (ILs) provide them a great opportunity to match different target properties to work as advanced process media. The crux of the matter is how to efficiently and reliably tailor suitable ILs toward a specific application. In this regard, the computer-aided molecular design (CAMD) approach has been widely adapted to cover this family of high-profile chemicals, that is, to perform computer-aided IL design (CAILD). This review discusses the past developments that have contributed to the state-of-the-art of CAILD and provides a perspective about how future works could pursue the acceleration of the practical application of ILs. In a broad context of CAILD, key aspects related to the forward structure-property modeling and reverse molecular design of ILs are overviewed. For the former forward task, diverse IL molecular representations, modeling algorithms, as well as representative models on physical properties, thermodynamic properties, among others of ILs are introduced. For the latter reverse task, representative works formulating different molecular design scenarios are summarized. Beyond the substantial progress made, some future perspectives to move CAILD a step forward are finally provided.

MARS Plus: An Improved Molecular Design Tool for Complex Compounds Involving Ionic, Stereo, and Cis-Trans Isomeric Structures.

MARS (Molecular Assembling and Representation Suite) (Hsu et al. J. Chem. Inf. Model. 2019, 59, 3703-3713) is a toolbox for the molecular design of organic molecules. MARS uses integer arrays to represent the elements and connectivity between elements of a molecule. It provides a collection of operations to manipulate the elemental composition and connectivity of a molecule (or a pair of molecules), enabling the creation of novel chemical compounds. In this work, the original MARS is extended to handle complex molecular structures, including geometric (cis-trans) isomers, stereo isomers, cyclic compounds, and ionic species. The extended version of MARS, referred to as MARS+, has a more comprehensive coverage of the chemical space and therefore can explore molecules with a greater chemical and physical diversity. Compared to other molecular design tools, MARS+ is designed to perform all possible manipulations on a given molecule or a pair of molecules. Molecular structure manipulation can be conducted in either a controlled or a random fashion. Furthermore, every structure manipulation has a counterpart so that the operation can be reversed. Nearly any possible chemical structure can be generated with MARS+ via a combination of molecular operations. The capabilities of MARS+ are examined by the design of new ionic liquids (ILs). The results show that MARS+ is a useful tool for computer-aided molecular design (CAMD) and molecular structure enumeration.