Chapter

Practical Chemoinformatics

pp 317-374

Date:

Representation, Fingerprinting, and Modelling of Chemical Reactions

  • Muthukumarasamy KarthikeyanAffiliated withDigital Information Resource Centre, National Chemical Laboratory Email author 
  • , Renu VyasAffiliated withScientist (DST) Division of Chemical Engineering and Process Development, National Chemical Laboratory

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Abstract

Designing a better molecule is just one aspect of computational research, but getting it synthesized for biological evaluation is the most significant component in a drug discovery program. A molecule can be formed by a number of synthetic routes. Manually keeping track of all the available options for a product formation in various reaction conditions is a herculean task. Chemoinformatics comes to the rescue by providing a number of computational tools for reaction modelling, albeit less in number than structure property prediction software. The current computational tools help us in modelling various aspects of a given organic reaction—synthetic feasibility, synthesis planning, transition state prediction, the kinetic and thermodynamic parameters, and finally mechanistic features. Several methods like empirical, semiempirical, quantum mechanical, quantum chemical, machine learning, etc. have been developed to model a reaction. The computational approaches are based on the concept of rational synthesis planning, retro-synthetic approaches, and logic in organic synthesis. In this chapter, we begin with reaction representation in computers, reaction databases, free and commercial reaction prediction programs, followed by reaction searching methods based on ontologies and reaction fingerprints. The commonly employed quantum mechanics (QM) and quantum chemistry (QC)-based methods for intrinsic reaction coordinate (IRC) and transition state (TS) determination using the B3LYP/6–31G* scheme are described using simple name reactions. Most of the computational reaction prediction programs such as CHAOS/CAOS are based on the identification of the strategic bonds which are likely to be cleaved or formed during a certain chemical transformation. Accordingly, an algorithm has been developed to identify more than 300 types of unique bonds occurring in chemical reactions. The effect of implicit hydrogens on chemical reactivity modelling is discussed in the context of bioactivity spectrum for structure–activity relationship studies. Other parameters affecting reactivity such as solvent polarity, thermodynamics etc. are also briefly highlighted for frequently used name reactions, hazardous high-energy reactions, as well as industrially important reactions involving bulk chemicals.

Keywords

Chemical reaction modelling Chemoinformatics Retro-synthesis Artificial intelligence Ontologies