Charles C. Solvason

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.

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.

Characterization-based molecular design of bio-fuel additives using chemometric and property clustering techniques

In this work, multivariate characterization data such as infrared (IR) spectroscopy was used as a source of descriptor data involving information on molecular architecture for designing structured molecules with tailored properties. Application of multivariate statistical techniques such as principal component analysis (PCA) allowed capturing important features of the molecular architecture from complex data to build appropriate latent variable models. Combining the property clustering techniques and group contribution methods (GCM) based on characterization data in a reverse problem formulation enabled identifying candidate components by combining or mixing molecular fragments until the resulting properties match the targets. The developed methodology is demonstrated using molecular design of biodiesel additive which when mixed with off-spec biodiesel produces biodiesel that meets the desired fuel specifications. The contribution of this work is that the complex structures and orientations of the molecule can be included in the design, thereby allowing enumeration of all feasible candidate molecules that matched the identified target but were not part of original training set of molecules.

Molecular Design of Biofuel Additives for Optimization of Fuel Characteristics

Abstract Several technical problems, such as relatively poor low temperature flow properties and oxidative stability, have impaired the use and commercialization of biodiesel. In this work, the objective of this research is to design biodiesel additives that result in performance properties comparable to the petroleum derived fuel. To meet this end, chemometric techniques combined with Principal Component Analysis (PCA) and Principal Component Regression (PCR) are employed. IR characterization based molecular design using group contribution parameters is used to identify novel additives that enable matching of the desired fuel specifications in a latent property space.

Molecular Signature Descriptors for Integrated Flowsheet and Molecular Design

The reverse problem formulation is a technique for solution of integrated process and product design problems from a properties perspective. In this work, an algorithm is introduced for reverse problem formulations using property operators based on molecular signature descriptors. A general framework has been developed for the integration of flowsheet design techniques with the solution of combined process and molecular design problems.

An algebraic approach for simultaneous solution of process and molecular design problems

The property integration framework has allowed for simultaneous representation of processes and products from a properties perspective and thereby established a link between molecular and process design problems. The simultaneous approach involves solving two reverse problems. The first reverse problem identifies the 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 that will be used to track properties. Earlier contributions in this area have worked to include higher order estimation of GCM for solving the molecular design problem. In this work, the accuracy of the property prediction is further 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 application range of the group contribution methods in molecular design problems. Successful tracking of properties is the key in applying the reverse problem formulation for integrated process and product design problems. An algebraic technique has been developed for solving process and molecular design problems simultaneously. Since both process and molecular property operators target the same optimum process performance, the set of inequality expressions can be solved simultaneously to identify the molecules that meet the desired process performance. Since this approach is based on an algebraic algorithm, any number of properties can be tracked simultaneously.