Nishanth G. Chemmangattuvalappil

Challenges and opportunities in computer-aided molecular design ☆

Abstract In this paper, the significant development, current challenges and future opportunities in the field of chemical product design using computer-aided molecular design (CAMD) tools are highlighted. With the gaining of focus on the design of novel and improved chemical products, the traditional heuristic based approaches may not be effective in designing optimal products. This leads to the vast development and application of CAMD tools, which are methods that combine property prediction models with computer-assisted search in the design of various chemical products. The introduction and development of different classes of property prediction methods in the overall product design process is discussed. The exploration and application of CAMD tools in numerous single component product designs, mixture design, and later in the integrated process-product design are reviewed in this paper. Difficulties and possible future extension of CAMD are then discussed in detail. The highlighted challenges and opportunities are mainly about the needs for exploration and development of property models, suitable design scale and computational effort as well as sustainable chemical product design framework. In order to produce a chemical product in a sustainable way, the role of each level in a chemical product design enterprise hierarchy is discussed. In addition to process parameters and product quality, environment, health and safety performance are required to be considered in shaping a sustainable chemical product design framework. On top of these, recent developments and opportunities in the design of ionic liquids using molecular design techniques have been discussed.

Reverse problem formulation approach to molecular design using property operators based on signature descriptors

Abstract In this work, an algorithm has been developed for the solution of property based molecular design problems. The recently introduced concept of molecular signature descriptors has been used to design molecules that meet the property targets corresponding to a required process performance. It has been shown that a variety of topological indices (TI) of molecules can be represented in terms of molecular signatures. Signatures can be used to represent different molecular groups if the property targets can be calculated using group contribution models as well. Therefore, the developed algorithm has the ability to combine a variety of property models based on group contribution expressions and topological indices based QSAR/QSPRs to track different property targets in molecular design. This algorithm utilizes molecular property operators formed from signatures for solving the reverse problem of obtaining the molecular structures that satisfy the property targets estimated during the process design step. The principles of graph theory are incorporated to ensure that the design provides feasible molecular structures. Since the molecular operators are formed based on molecular signatures, property models based on different TIs can be represented on the same property platform. Techniques have been developed to describe all TIs with a single signature height. The accuracy of this method depends only on how well the actual property–TI relationships are estimated. Since many TIs can be used to describe each property, this algorithm generally provides reliable results. In addition to physical properties, a wide variety of biological activities can be tracked using the correlations with TIs. This contribution will illustrate the developed methods and highlight their use through a case study.

Simultaneous solution of process and molecular design problems using an algebraic approach

Abstract Traditionally process design and molecular design problems have been treated as two separate problems, with little or no feedback between them. Introduction of the property integration framework has allowed for simultaneous representation of processes and products from a property perspective and hence established a link between molecular and process design. The simultaneous approach involves solving two reverse problems. The first reverse problem identifies the input molecules’ property targets corresponding to the desired process performance. The second reverse problem is the reverse of a property prediction problem, which identifies the molecular structures that match the targets identified in the first problem. Group contribution methods (GCM) are used to form molecular property operators and these help in tracking properties. Earlier contributions in this area have tried to include higher order estimation of GCM for solving the molecular design problem. In this work, the accuracy of property prediction is enhanced by improving the techniques to enumerate higher order groups. Incorporation of these higher order enumeration techniques increases the efficiency of property prediction and thus the range of applicability of group contribution methods to molecular design problems. This method of generation enables the identification of structural isomers to some extent as it puts a check on the possibility of nonexistence of each higher order group in each combination. Property operator based techniques are used to track properties in both process and molecular design problems. The developed algorithm solves the set of inequality expressions of process and molecular design problems simultaneously to identify the molecules that meet the process performance and environmental restrictions defined in terms of properties. Since the algorithm should be able to solve for any number of properties, an algebraic approach is used to generate possible molecules within the required property range. This contribution will use a case study to highlight the principles of the developed methodology.

A novel algorithm for molecular synthesis using enhanced property operators

Abstract Traditionally process design and molecular design have been treated as two separate problems, with little or no feedback between the two approaches. Introduction of the property integration framework has allowed for simultaneous representation of processes and products and established a link between molecular and process design from a properties perspective. Utilizing this methodology enables identification of the desired properties by targeting the optimum process performance without committing to any components. The identified property targets can then be used as inputs for solving a molecular design problem. Earlier works have extended the property integration framework to include group contribution methods (GCMs) for solving the molecular design problem. In this work, second order estimation of GCM has been combined with the first order estimation of GCM using the property clustering methodology in order to increase the accuracy of the property predictions. An algebraic approach has been developed utilizing second order groups built from first order groups subject to the constraints of overlapping. The advantage of using an algebraic approach is that it can handle any number of molecular groups and/or properties and can generate all possible compounds within the required range of properties. The most significant aspect of the aforementioned method is that both the application range and reliability of the molecular property clustering technique are considerably increased by incorporating second order estimation. This contribution will illustrate the developed methods and highlight their use through a case study.

Ionic liquid design for enhanced carbon dioxide capture by computer-aided molecular design approach

Carbon capture and storage is an emerging technology to mitigate carbon dioxide (CO2) emissions from industrial sources such as power plants. Post-combustion capture based on aqueous amine scrubbing is one of the most promising technologies for CO2 capture currently. This technology, however, possesses a number of shortcomings, including high regeneration energy requirement, high solvent loss, degradation of solvent, etc. To overcome these limitations, researchers suggested different solvents and alternative technologies to replace the current amine scrubbing technique. Ionic liquids (ILs) are the most potential substitute among all. This is mainly because they have negligible vapour pressure and high thermal stability, which reduce solvent loss. However, there are up to a million possible combinations of cation and anion that may make up the ILs, which makes experimental works very time consuming and costly. In this work, optimal IL solvents specifically for carbon capture purpose are designed using computer-aided molecular design approach. This approach utilises group contribution method to estimate the thermophysical properties of ILs, and UNIFAC model to predict CO2 solubility in the ILs. Structural constraints are included to ensure that the synthesised ILs structure will satisfy the bonding requirement. This work focuses on design of ILs based on a physical absorption mechanism, and hence no chemical reaction is involved. The results show that the designed ILs are capable of capturing CO2 and their predicted properties are in good agreement with properties as determined through experimental works.

Combined property clustering and GC+ techniques for process and product design

Abstract Recent developments in the area of process and product integration have enabled the systematic identification of suitable candidate molecules to meet certain process performance. In this approach, the property targets for the input molecules corresponding to the optimum process performance have been identified in the first step and the molecules that have the target properties have been designed in the next step. The focus of this work is to develop a combined property clustering and GC + algorithm to identify molecules that meet the property targets identified during the process design stage. In our earlier works, a methodology was introduced for identifying molecules with a given set of properties by combining property clustering and group contribution methods. Yet, there are situations when the property contributions of some of the molecular groups of interest are not available in literature. To address this limitation, an algorithm has been developed to include the property contributions predicted by combined group contribution and connectivity indices methods into the cluster space. For the design of simple monofunctional molecules, a modified visual approach has been used, while for the design of more complicated structures an algebraic method has been developed. The applicability of the algebraic method has been increased by including the property contributions from second and third order groups.

A systematic method for integrating product attributes within molecular synthesis

The complete and efficient solution to the enumeration of candidate compounds and mixtures that meet specified consumer attributes is often a difficult mathematical programming problem. Most approaches to this problem involve the solution of a mixed integer non-linear program (MINLP) which may achieve only local optima solutions. In this paper a proof-of-concept study is presented to show that empirical models can be used in a reverse problem formulation to ensure a complete set of candidate compounds and mixtures are found subject to the predictive power of the model. The method utilizes a transformation of consumer attributes to properties described by the group contribution method and solves the reverse problem formulation using the property clustering technique. A case study in refrigerant design is used to highlight the method.

A Property Based Approach for Simultaneous Process and Molecular Design

Abstract In this work, property clustering techniques and group contribution methods are combined to enable simultaneous consideration of process performance requirements and molecular property constraints. Using this methodology, the process design problem is solved to identify the property targets corresponding to the desired process performance. A significant advantage of the developed methodology is that for problems that can be satisfactorily described by only three properties, the process and molecular design problems can be simultaneously solved visually on a ternary diagram, irrespective of how many molecular fragments are included in the search space. On the ternary cluster diagram, the target properties are represented as individual points if given as discrete values or as a region if given as intervals. The structure and identity of candidate components is then identified by combining or “mixing” molecular fragments until the resulting properties match the targets.

A systematic methodology for optimal mixture design in an integrated biorefinery

Abstract Biomass is a sustainable source of energy which can be utilised to produce value-added products such as biochemical products and biomaterials. In order to produce a sustainable supply of such value-added products, an integrated biorefinery is required. An integrated biorefinery is a processing facility that integrates multiple biomass conversion pathways to produce value-added products. To date, various biomass conversion pathways are available to convert biomass into a wide range of products. Due to the large number of available pathways, various systematic screening tools have been developed to address the process design aspect of an integrated biorefinery. Process design however, is often inter-linked with product design as it is important to identify the optimal molecule (based on desired product properties) prior to designing its optimal production routes. In cases where the desired product properties cannot be met by a single component chemical product, a mixture of chemicals would be required. In this respect, product and process design decisions would be a challenging task for an integrated biorefinery. In this work, a novel two-stage optimisation approach is developed to identify the optimal conversion pathways in an integrated biorefinery to convert biomass into the optimal mixtures in terms of target product properties. In the first stage, the optimal mixture is designed via computer-aided molecular design (CAMD) technique. CAMD technique is a reverse engineering approach which predicts the molecules with optimal properties using property prediction models. Different classes of property models such as group contribution (GC) models and quantitative structure property relationship (QSPR) are adapted in this work. The main component of the mixture is first determined from the target product properties. This is followed by the identifying of additive components to form an optimal mixture with the main component based on the desired product properties. Once the optimal mixture is determined, the second stage identifies the optimal conversion pathways via superstructural mathematical optimisation approach. With such approach, the optimal conversion pathways can be determined based on different optimisation objectives (e.g. highest product yield, lowest environmental impact etc.). To illustrate the proposed methodology, a case study on the design of fuel additives as a mixture of different molecules from palm-based biomass is presented. With the developed methodology, optimal fuel additives are designed based on optimal target properties. Once the optimal fuel additives are designed, the optimal conversion pathways in terms of highest product yield and economic performance that convert biomass into the optimal fuel additives are identified.

Robust chemical product design via fuzzy optimisation approach

Abstract Traditionally, the design of new chemical products for specific applications is done by using a combination of design heuristics, experimental studies and expert judgements. In addition to the conventional methods, chemical products can also be designed by using computer-aided molecular design (CAMD) techniques. Based on CAMD, optimal chemical products can be designed by identifying the molecule with the best properties that correspond with the target functionalities of the product. In general, the optimality of product property (termed as property superiority) is the only factor considered while designing optimal products by using CAMD techniques. However, it is noted that property prediction models are developed with certain accuracy and uncertainties. As the accuracy of property prediction models (termed as property robustness) can affect the effectiveness of CAMD techniques in predicting the product property, the effects of property prediction uncertainty have to be considered while applying CAMD techniques. This paper presents a systematic fuzzy optimisation based molecular design methodology. The methodology is developed for the design of optimum molecules used in chemical processes by considering and optimising both property superiority and robustness. Property superiority is quantified by property optimality. Meanwhile, property robustness is expressed by the standard deviation of the property prediction model, which is a measure of average variation between the experimental data and estimated values of product property using property prediction model. Fuzzy optimisation approach is extended in this work to address and trade off property superiority and robustness simultaneously. Molecular design technique is adapted in this work to identify the optimal molecular structure which satisfies multiple product specification. To illustrate the proposed method, a case study is presented where optimal solution is selected based on how much the solution satisfied the criteria of property superiority and robustness.