Development of Computer-Aided Molecular Design Methods for Bioengineering Applications

Computer-aided molecular design (CAMD) offers a methodology for rational product design. The CAMD procedure consists of pre-design, design and post-design phases. CAMD was used to address two bioengineering problems: design of excipients for lyophilized protein formulations and design of ionic liquids for use in bioseparations. Protein stability remains a major concern during protein drug development. Lyophilization, or freeze-drying, is often sought to improve chemical stability. However, lyophilization can result in protein aggregation. Excipients, or additives, are included to stabilize proteins in lyophilized formulations. CAMD was used to rationally select or design excipients for lyophilized protein formulations. The use of solvents to aid separation is common in chemical processes. Ionic liquids offer a class of molecules with tunable properties that can be altered to find optimal solvents for a given application. CAMD was used to design ionic liquids for extractive distillation and in situ extractive fermentation processes. The pre-design phase involves experimental data gathering and problem formulation. When available, data was obtained from literature sources. For excipient design, data of percent protein monomer remaining post-lyophilization was measured for a variety of protein-excipient combinations. In problem formulation, the objective was to minimize the difference between the properties of the designed molecule and the target property values. Problem formulations resulted in either mixed-integer linear programs (MILPs) or mixed-integer non-linear programs (MINLPs). The design phase consists of the forward problem and the reverse problem. In the forward problem, linear quantitative structure-property relationships (QSPRs) were developed using connectivity indices. Chiral connectivity indices were used for excipient property models to improve fit and incorporate three-dimensional structural information. Descriptor selection methods were employed to find models that minimized Mallow's Cp statistic, obtaining models with good fit while avoiding overfitting. Cross-validation was performed to access predictive capabilities. Model development was also performed to develop group contribution models and non-linear QSPRs. A UNIFAC model was developed to predict the thermodynamic properties of ionic liquids. In the reverse problem of the design phase, molecules were proposed with optimal property values. Deterministic methods were used to design ionic liquids entrainers for azeotropic distillation. Tabu search, a stochastic optimization method, was applied to both ionic liquid and excipient design to provide novel molecular candidates. Tabu search was also compared to a genetic algorithm for CAMD applications. Tuning was performed using a test case to determine parameter values for both methods. After tuning, both stochastic methods were used with design cases to provide optimal excipient stabilizers for lyophilized protein formulations. Results suggested that the genetic algorithm provided a faster time to solution while the tabu search provides quality solutions more consistently. The post-design phase provides solution analysis and verification. Process simulation was used to evaluate the energy requirements of azeotropic separations using designed ionic liquids. Results demonstrated that less energy was required than processes using conventional entrainers or ionic liquids that were not optimally designed. Molecular simulation was used to guide protein formulation design and may prove to be a useful tool in post-design verification. Finally, prediction intervals were used for properties predicted from linear QSPRs to quantify the prediction error in the CAMD solutions. Overlapping prediction intervals indicate solutions with statistically similar property values. Prediction interval analysis showed that tabu search returns many results with statistically similar property values in the design of carbohydrate glass formers for lyophilized protein formulations. The best solutions from tabu search and the genetic algorithm were shown to be statistically similar for all design cases considered. Overall the CAMD method developed here provides a comprehensive framework for the design of novel molecules for bioengineering approaches

Computational Molecular Design Using Tabu Search

The focus of this project is the use of computational molecular design (CMD) in the design of novel crosslinked polymers. A design example was completed for a dimethacrylate as part of a comonomer used in dental restoration, with the goal to create a dental adhesive with a longer clinical lifetime than those already on the market. The CMD methodology begins with the calculation of molecular descriptors that describe the crosslinked polymer structure. Connectivity index are used as the primary set of descriptors, and have been used successfully in other CMD projects. Quantitative structure property relationships (QSPRs) were developed relating the structural descriptors to the experimentally collected property data. Models were chosen using Mallows' Cp with correlation coefficient significance. Desirable target property values were chosen which lead to an improved clinical lifetime. Structural constraints were defined to increase stability and ease of synthesis. The Tabu Search optimization algorithm was used to design polymers with desirable properties. Finally, a prediction interval was calculated for each candidate to represent the possible error in the predicted properties. The described methodology provides a list of candidate monomers with predicted properties near the desired target values, which are selected such that the adhesives will show improved propertoes relative to the standard HEMA/BisGMA formulation. The methodology can be easily altered to allow for additional property calculations and structural constraints. This methodology can also be used for molecular design projects beyond crosslinked polymers

Molecular Design of Crosslinked Copolymers

A complete methodology for the computational molecular design (CMD) of crosslinked polymers is developed and implemented. The methodology is applied to the design of novel polymers for restorative dental materials. The computational molecular design of crosslinked polymers using optimization techniques is a new area of research. The first part of this project seeks to develop a novel data structure capable of adequately storing a complete description of the crosslinked polymer structure. Numerical descriptors of polymer structure are then calculated from the data structure. Statistical methods are used to relate the structural descriptors to experimentally measured properties. An important part of this project is to show that useful property prediction models can be developed for crosslinked polymers. Desirable property target values are then set for a specific application. Finally, the structure-property relations are combined with a Tabu search optimization algorithm to design improved polymers. Tabu search allows much flexibility in the problem formulations, so a major goal of this project is to show that Tabu search is a effective method for crosslinked polymer design. To implement the molecular design procedure, a software package is developed. The software allows for easy graphical entry of polymer structures and property data, and contains a Tabu search optimization routine. Since computational molecular design of crosslinked polymers is a relatively new area of research, the software is designed to be easily modified to allow for extensive numerical experimentation. Finally, the computational design methodology is demonstrated for the design of polymers for restorative dental applications. Using the computational molecular design methodology developed in this project, several monomers are found that may offer a significant improvement over a standard HEMA/bisGMA formulation. The results of the case study show that the new data structure for crosslinked polymers is effective for calculation of topological descriptors and roperty models can be developed for crosslinked polymers. Tabu search is also shown to be an effective optimization method

Advances and Challenges in Protein-Ligand Docking

Molecular docking is a widely-used computational tool for the study of molecular recognition, which aims to predict the binding mode and binding affinity of a complex formed by two or more constituent molecules with known structures. An important type of molecular docking is protein-ligand docking because of its therapeutic applications in modern structure-based drug design. Here, we review the recent advances of protein flexibility, ligand sampling, and scoring functions—the three important aspects in protein-ligand docking. Challenges and possible future directions are discussed in the Conclusion

Diversification-driven tabu search for unconstrained binary quadratic problems

This paper describes a Diversification-Driven Tabu Search (D²TS) algorithm for solving unconstrained binary quadratic problems. D²TS is distinguished by the introduction of a perturbation-based diversification strategy guided by long-term memory. The performance of the proposed algorithm is assessed on the largest instances from the ORLIB library (up to 2500 variables) as well as still larger instances from the literature (up to 7000 variables). The computational results show that D²TS is highly competitive in terms of both solution quality and computational efficiency relative to some of the best performing heuristics in the literature

Exploration of Reaction Pathways and Chemical Transformation Networks

For the investigation of chemical reaction networks, the identification of all relevant intermediates and elementary reactions is mandatory. Many algorithmic approaches exist that perform explorations efficiently and automatedly. These approaches differ in their application range, the level of completeness of the exploration, as well as the amount of heuristics and human intervention required. Here, we describe and compare the different approaches based on these criteria. Future directions leveraging the strengths of chemical heuristics, human interaction, and physical rigor are discussed.Comment: 48 pages, 4 figure

From Theory to Bench Experiment by Computer-assisted Drug Design

Tight integration of computer-assisted molecular design with practical realization by medicinal chemistry will be essential for finding next-generation drugs that are optimized for multiple pharmaceutically relevant properties. ETH Zürich has established an interdisciplinary research group devoted to exploring the potential of this scientific approach by combining expertise from pharmaceutical chemistry and computer sciences. In this article, some of the group's activities and projects are presented. A current focus is on machine-learning applications aiming at hit and lead structure identification by virtual screening and de novo design. The central concept of 'adaptive fitness landscapes' is highlighted along with practical examples from drug discovery projects