Journal of Theoretical & Computational Science
Open Access

Complications and Molecular Modelling Methods of Computer Aided Molecular Design

Jeffrey Filliar^{* *}Correspondence: Jeffrey Filliar, Department of Computer Engineering, University of London, England, UK, Email:

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Introduction

Computer aided molecular design problems are defined as given as a set of building blocks and a specified set of target properties, determine the molecule or molecular structure that matches these properties. CAMD has been used widely in applications to design molecules of improved performance the most common performance criteria include molecular or process properties pertaining to the thermodynamic or process performance of the evaluated molecular structures. A method for identifying molecules or combinations of molecules with a complex set of physicochemical characteristics is Computer Aided Molecular Design (CAMD). The application range of CAMD is restricted due to limitations on the complexity of the generated molecular structures and on the availability of suitable models for property prediction.

This issue is addressed by a new CAMD methodology that combines molecular modelling methods with a conventional CAMD approach. The new approach comprises a brand new algorithm for generating molecules and atoms, a sizable set of techniques for estimating properties, and a connection to tools for molecular modeling. The capacity of CAMD approaches to forecast, estimate, and create molecules with a set of present target qualities makes them crucial for chemical product creation. If the focus of the product is emphasized on the macroscopic properties, the product design can be considered as a combination of molecular design and mixture design. Contrary to property prediction of pure components where usually only the type of components plays the role, property estimation for mixture is usually affected by temperature, pressure, density, activity coefficient and composition of the mixture components. There are instances in chemical product design where the prime objective and focus of the design process is to create a product that drives a certain process, in addition to developing chemical products based on chemical components. Process design and molecular design have typically been separated and approached as two distinct issues with minimal to no connections between the design methodologies. Designing new molecules possessing desired properties is an important activity in the chemical and pharmaceutical industries. Much of this design involves an elaborate and expensive trial-and-error process that is difficult to automate. New study describes the computer-aided molecular design approach using genetic algorithms. Genetic algorithms allow for the direct incorporation of higher level chemical knowledge and reasoning strategies to make the search more efficient and the utility of genetic algorithms for molecular design is demonstrated with some studies in polymer design.

Description

The design and development of molecular chemical products, such as pharmaceutical medicines, solvents, and other functional goods, has seen extensive use of the CAMD technique. The CAMD issue seeks to select the best molecules (or just decent molecules) from the set of theoretically feasible chemical structures. At first glance, the CAMD problem must take into account a very abstract space of atoms, bonds, aromaticity, structural isomers, electronic effects, and other aspects of chemical design. Though many of these features are certainly what give molecules their specific properties and chemical functionality, they are difficult to build into any type of optimization scheme. A new framework to automate, augment, and accelerate steps in computer-aided molecular design is presented. There are three phases to the problem's resolution:

• Design for the composition.
• Design for the structure.
• Design for the extended to achieve computational efficiency, composition identification and structure determination is decoupled.

Conclusion

In the first stage, molecular compositions that correspond to design targets are identified using approximate group contribution methods. In the second stage, the isomer structures of the solution compositions are systematically determined, and the solution pool is improved using structure-based property corrections. By utilizing problem specific property models in the last stage, the design is enhanced beyond the capabilities of group-contribution methods. Using a variety of property models, novel optimization models and graph theoretic algorithms generate a large and diverse pool of candidates at each design stage. The promise of saving time and effort compared to traditional empirical procedures has recently attracted a lot of attention to computer assisted molecular design approaches for the synthesis of novel molecules. These traditional procedures have entailed a lot of compound synthesis through trial and error methods.