COMPUTATIONAL BASICS UNDERLYING SYNTHETIC STRATEGIES

<< SOME CONCEPTUAL DEVELOPMENTS IN SYNTHESIS IN CHEMISTRY

Chapter - 17

COMPUTATIONAL BASICS UNDERLYING SYNTHETIC

STRATEGIES

S. Sabiah

KEY WORDS: Computation, Synthetic strategy, Synthesis design, Computer-

assisted synthesis

INTRODUCTION:

Synthetic strategy is an important stepping stone for scientific community. It has been

found so crucial and innovative in the four mainstream of research namely,

1. Observational Science

2. Experimental Science

3. Theoretical Science

4. Computational Science

Since the topic of choice is on computational, I would like to draw few lines about the

computational science. Computational science (or scientific computing) is the field

of study concerned with constructing mathematical models and numerical solution

techniques and using computers to analyze and solve scientific, social scientific and

engineering problems. In practical use, it is typically the application of computer

simulation and other forms of computation to problems in various scientific

disciplines. It uses everything that scientists already know about a problem and

incorporates it into a mathematical problem which can be solved. The mathematical

model which then develops gives scientists more information about the problem.

Computational Science is beneficial for two main reasons:

1. It is a cheaper method of conducting experiments.

2. It provides scientists with extra information which helps them to better

plan and hypothesizes about experiments.

Due to these reasons, computation has gained much attention in almost all fields of

science which are listed below under computational disciplines.

COMPUTATIONAL DISCIPLINES

Bioinformatics

Cheminformatics

Chemometrics

Computational biology

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Computational Basics Underlying Synthetic Strategies

Computational chemistry

Computational economics

Computational electromagnetics

Computational engineering

Computational fluid dynamics

Computational mathematics

Computational mechanics

Computational physics

Computational statistics

Environmental simulation

Financial modeling

Geographic information system (GIS)

High performance computing

Machine learning

Network analysis

Numerical weather prediction

Pattern recognition

Since the main focus of the chapter is on synthetic strategies in chemistry, it is

likely to be elaborated towards chemical aspects. In the field of chemistry, we all

know that the beginning of the organic synthesis is started with the preparation of urea

by Wohler in 1828. It is only in 1967 that a systematic analysis of synthesis towards

the direction of computer-assisted synthesis design (CASD) was reported by E. J.

Corey [1]. Since then the computational methods in chemistry has been growing in

many dimensions and every scientific paper pays attention to computational support

in addition to the experimental evidences. It has also become a useful way to

investigate materials that are too difficult to find or too expensive to purchase. It helps

chemists to make predictions before running the actual experiments so that they can

be better prepared for making observations. Hence, we can say that computational

chemistry is partly assisting the basic chemical problems deal with synthesis, structure

and spectroscopy of materials and indeed important to discuss further.

Computational Chemistry

Computational chemistry is a branch of chemistry that generates data, which

complements experimental data on the structures, properties and reactions of

substances. It can in some cases predict hitherto unobserved chemical phenomena. It

Synthetic Strategies in Chemistry

17.3

is widely used in the design of new drugs and materials. The calculations are based

primarily on Schrödinger's equation (eqn. 1).

.................eqn 1

The symbol ψ is a mathematical function that calculates the strength of the deBroglie

wave at various positions in space. The rest of the components are as follows:

h = Planck's constant

m = the mass of the particle

Ć = a partial differential operator called the Laplacian operator

V=the potential energy

=psi, the wave function

i =the square root of -1

The final form of equation 1 is Hψ = Eψ

Where H = Hamiltonian Operator; E = total energy of the system

Computational chemistry is particularly useful for determining molecular

properties which are inaccessible experimentally and for interpreting

experimental data

With computational chemistry, one can calculate:

electronic structure determinations

geometry optimizations

frequency calculations

transition structures

protein calculations, i.e. docking

electron and charge distributions

potential energy surfaces (PES)

rate constants for chemical reactions (kinetics)

thermodynamic calculations- heat of reactions, energy of activation

There are three main types of calculations:

1. Ab Initio: (Latin for "from scratch") a group of methods in which

molecular structures can be calculated using nothing but the

Schroedinger equation, the values of the fundamental constants and the

atomic numbers of the atoms present

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Computational Basics Underlying Synthetic Strategies

2. Semi-empirical: techniques use approximations from empirical

(experimental) data to provide the input into the mathematical models

3. Molecular mechanics: uses classical physics to explain and interpret

the behavior of atoms and molecules

Currently, there are two ways to approach chemistry problems: computational

quantum chemistry and non-computational quantum chemistry. Computational

quantum chemistry is primarily concerned with the numerical computation of

molecular electronic structures by ab initio and semi-empirical techniques and non-

computational quantum chemistry deals with the formulation of analytical expressions

for the properties of molecules and their reactions. Examples of such properties are

structure (i.e. the expected positions of the constituent atoms), absolute and relative

(interaction) energies, electronic charge distributions, dipoles and higher multipole

moments, vibrational frequencies, reactivity or other spectroscopic quantities, and

cross sections for collision with other particles.

The methods employed cover both static and dynamic situations. In all cases the

computer time increases rapidly with the size of the system being studied. That

system can be a single molecule, a group of molecules or a solid. The methods are

thus based on theories which range from highly accurate, but are suitable only for

small systems, to very approximate, but suitable for very large systems. The accurate

methods used are called ab initio methods, as they are based entirely on theory from

first principles. The less accurate methods are called empirical or semi-empirical

because some experimental results, often from atoms or related molecules, are used

along with theory.

There are two different aspects to computational chemistry:

Computational studies can be carried out in order to find a starting point for a

laboratory synthesis, or to assist in understanding experimental data, such as

the position and source of spectroscopic peaks.

Computational studies can be used to predict the possibility of so far entirely

unknown molecules or to explore reaction mechanisms that are not readily

studied by experimental means.

To perform these computational simulations or calculations, super computers with

high performance computing facility is needed.

Synthetic Strategies in Chemistry

17.5

Impact of High Performance Computing

The amount of chemical information is quite large and calls for computer as the main

source for storage. With presently

17 million known compounds

500000 new compounds each year

600 000 chemistry-related publications annually

An overview and access to all this information can only be maintained by electronic

means.

Thus, databases on chemical information play a major role in present day research and

development. Databases provide access to

literature on 17 million compounds

factual data on 7 million organic compounds

several million reactions

140000 experimental 3D structures

250 000 spectra

SOFTWARES

The use of computers as tools in chemistry dates back to late 1950s. The adoption of

FORTRAN as a scientific programming language

is the beginning of these

studies[2]. Only from around 1980 1990, the user had access to a variety of network

based resources from a single point of use. A more sophisticated example is the use of

Java to display digital spectral information derived from an NMR spectrometer, [3]

and Java is a computer language developed by Sun Microsystems for writing

programs to run on the web within a browser. It is used to link regions of the spectrum

to specific atoms or residues in a 3D molecular object. The links can be bi-directional,

i.e. clicking on a specified atom will highlight the spectral region containing peaks

associated with that atom.

Chemists have been some of the most active and innovative participants in this rapid

expansion of computational science. Some common computer software used for

computational chemistry includes:

Gaussian 03

GAMESS

MOPAC

Spartan

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Sybyl

Now let us move towards the use of these computational basics for synthetic strategy

especially in basic organic chemistry. Once we know how it works, it can be

extendable to other materials.

1. IN ORGANIC SYNTHESIS

When a chemist wants to synthesize a compound of interest, it is called target

molecule and he looks in to the target to identify any known fragment called

substructure is present whose synthesis is already available. For example, in the

synthesis of the Ibogamine, chemist perceives the presence of an indole nucleus,

whose synthesis is known (Chemical structures are shown inside the box). So, he may

plan the synthesis similar to Indole as outlined in Scheme 1.

Indole

Ibogamine

Scheme 1

Synthesis of Ibogamine

Synthesis of Indole

NH2 +

NH2

Indole

Ibogamine

A known starting material is very similar to the target and the problem is 'reduced' to

find the reactions which will convert it to the target. Despite the similarity of the

starting material and the target, the number of steps to obtain the target may be high in

certain cases which triggered the venture of using a computer to solve synthesis

problems [4]. The number of reactions being very high (several thousands), the

computer should be able to store all these reactions. It would never forget them and it

Synthetic Strategies in Chemistry

17.7

could generate all the possibilities. For this purpose, Corey proposed a general

approach which is discussed.

Corey's Approach

Starting from the target, the program finds its precursors, then each precursor

becomes a target and new precursors are generated; the process is then repeated until

commercial or simple products are generated. This approach is called retro-synthetic

or antithetic, since it is the reverse of the synthesis as practiced at the bench. This

approach may be visualized by a 'synthesis tree' (Scheme 2). This approach should,

formally, be 'easily' programmed: it necessitates 'only' writing a program able to

generate the precursors of a target and this program is repeated again and again.

Numerous programs have been written after this initial report and several reviews on

this subject have been published [5].

Scheme 2

Target

First level

of precursors

Second level

of precursors

The above tree points outline the essential problems like how to describe a molecule

and how to describe a reaction, that is, how to generate a precursor. Let us consider

the classical Diels-Alder reaction,

Scheme 3

dienophile

diene

cyclohexene

The above equation corresponds to writing of the reaction in the synthetic (or

forward) direction: reacting 1 with 2, under appropriate conditions, generates 3. In the

approach proposed by Corey, the program works backward, from the target to the

precursor: in the retro-synthetic or antithetic direction. So the program has to search

the substructure 3 in the target and, if it is present, it replaces it by the precursor(s)

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Computational Basics Underlying Synthetic Strategies

substructure(s), here 1 + 2. To describe the reaction in the retro-synthetic direction,

the term of 'transform' has been proposed [6] and the use of a double lined arrow

indicates this operation (Scheme 4):

Scheme 4

cyclohexene

dienophile

diene

This description may look simple for a computer, but fails for certain circumstances.

For example,

Scheme 5

If 4 is the target then the precursor would be 5, which is an impossible solution due to

the allenic function in a five-membered ring. But, computer predicts scheme 5 as a

retro-synthetic approach which is not applicable to laboratory synthesis. Similarly, if

the computer is taught the reduction of a keto group (7) to form an alcohol (6),

described in scheme 6, the backward mode by the transform of scheme it will

generate 7 as a possible precursor for 6. This is again a wrong solution, indeed there is

another keto group which will generally be reduced, so that, in the laboratory the

result would not be 6 but 8 (Scheme 6).

Scheme 6

These two examples clearly indicate that the computer should know to discard wrong

solutions for a synthetic strategy. Hence there should be a way to settle

communication between the computer and the chemist for the following aspects:

Description of molecules

II.

Description of reactions

Synthetic Strategies in Chemistry

17.9

III.

Pruning the synthesis tree

These aspects are discussed in the upcoming section.

I. Description of Molecules

(a) Use of Connectivity Table

The molecules of interest are first described by connectivity tables which list in

several arrays the atoms and the bonds of the target. An example is given with atom

labeling (bold) and nature of bonds (normal numbers) for compound A.

H3C

compund A

Then the connectivity is given by atomic table and bond table as described in Tables

17.1 and 17.2. These tables are not given in this form to the computer. Actually the

chemist draws the structure on the screen using a mouse [7], and a program takes care

of transforming this graphic structure into a connectivity table.

Table 17.1. Atomic table for compound A

Bonds

Number of hydrogen

Atom number

Atom type

Neighbors

number

atoms

This description is a topological one, but when a chemist looks at a target he does not

only see a succession of atoms and bonds. He automatically perceives structural

features, such as functional groups, rings, stereo-centers, and their relative positions.

The information is central to finding the solutions of the problem. The computer also

needs to be taught this chemical perception of the target. So, when the drawing of the

target is done and the connectivity tables have been established, before starting the

retro-synthetic process, the program searches for these characteristics and stores them

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Computational Basics Underlying Synthetic Strategies

in a binary array which is a kind of identity card of the target. An example of a partial

binary array for structure A is given in Table 17.3.

Table 17.2. Bond table for compound A

Bond number

Atom 1

Atom 2

Bond type

Table 17.3. Binary array for compound A

Groups

CH2

CH3

Ketone Nitrile

Cyclic

atom

number

With the topological tables and this analytical one, the program gains a chemical

perception of the target.

(b) Use of Matrices

In 1971 Ugi and Gillespie [8] proposed the concept of 'BE' (for bond-electron)

matrices to describe molecules. 'BE' matrices were then transformed into connection

tables. In this model the element (i, j) of the matrix corresponds to the bond order

between atoms i and j (l = single bond, 2 = double bond, ...) and the diagonal elements

(i, i) describe the free valence electrons of atom i. Compound A is described by the

matrix of equation (1):

Synthetic Strategies in Chemistry

17.11

(1)

In 1971, Hendrickson proposed a logical description of structures and reactions by a

simple mathematical model [9] which has been developed and used in the program

SYNGEN. This mainly focuses on carbon skeleton as shown for a three carbon

system of compound B. In the original system four kinds of attachments to any carbon

are described and counted: H for attachment of hydrogen, or electropositive atoms; R

for σ-bond to another carbon; Π for š-bond to another carbon and Z for a bond (σ or

š) to an electronegative heteroatom (N, O, S, X). The H, R, Π and Z are notations

used in the program. How many numbers of such attachments is described by h, σ, š,

z, respectively. The functionality (f) at a carbon site is defined as the sum of z and š,

and the character (c) of a carbon site is c = l0 σ + f.

In SYNGEN the functional groups on each carbon atom are abstracted with the two

digits z and š; for atom 2 of Scheme 7, the zš list is 11, for atom 3 it is 30.

H2C

Scheme 7

Compound B

atom

II. Coding of a Particular Reaction

The three main systems described above lead to three main methods for coding

reactions in CASD programs. The description of reactions leads also to two families

of CASD programs: empirical and non-empirical. The empirical programs are based

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Computational Basics Underlying Synthetic Strategies

on known reaction libraries. The advantage of such an approach is that the programs

predict syntheses which have great chances of feasibility provided that specific

structural features do not strongly interfere with one of the proposed reactions. On the

other hand, these programs cannot suggest totally new synthesis reactions. Further,

the number of reactions to code is very large; theoretically, all known reactions should

be coded in the reaction files.

In the non-empirical programs reactions are coded in a logical, mathematical or

general way, in order to describe the maximum of reactions with a minimum of

principles. The aim is also to have a system able to propose new syntheses, even new

reactions if they have not yet been described in the literature

(a) The Transform Approach

As indicated above, the word 'transform' is employed to describe a reaction in the

retro-synthetic direction. The aim of a CASD program is to generate the precursors of

a target. To do this, a program has three main tasks to perform:

- Search for a substructure which describes the transform (called synthon or retron)

- Generation of the precursors

- Evaluation of the validity of the solutions.

The main differences between the various programs come from the evaluation step,

which may be more or less accurate. In SOS program, a full graphic interface has

been developed to input transform and evaluation tests. For example, the user draws

the retron and its precursor with the mouse (Fig. 17.1). Let us consider the example of

the Diels-Alder reaction. Symbol A stands for any atom in order to generalize the

reaction. Since the diene may react with a double or a triple bond, one may use the

simple/double bond in the target and double/triple bond in the precursor (bonds with

dotted lines).

Synthetic Strategies in Chemistry

17.13

Fig. 17.1. Representation of an active window from SOS program

To enter a test, the chemist selects this option in the reaction menu and indicates that

the test must be applied on the target. The atoms and the bonds are numbered and the

user may enter the test by clicking on the buttons at the bottom of the screen. The test

allows for a solution such as that if atom 4 is sp2 and bond 3 is a fused bond then the

reaction is impossible. This program also allows graphical tests: the user draws a

substructure and the action to perform if it is present in the scrutinized target.

In SOS, coding of reactions by means of mechanisms (i.e., elimination,

nucleophilic addition, nucleophilic substitution, etc.) allowed chemists to generalize

reactions, to reduce the number of reactions in the files, and to extend a scheme to a

new case even if it has not been described in the literature.

(b) Be-matrices Approach.

Ugi and Dugundji developed a mathematical model of constitutional chemistry [10].

This model is based on the concept of isomerism of molecules which has been

extended to ensembles of molecules. For example, a theoretical reaction: A + B

+ D can be seen as the conversion of an ensemble of molecules (A + B) into an

isomeric ensemble (C + D). As an extension, the discovering of a synthesis: Target

Precursor 1

Precursor 2

:::: Starting materials, may be done by generation of

isomers.

The description of molecules by means of matrices allows one to describe

reactions by additions of matrices; let us take the addition of H-Br on a double bond

(equation 2).

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Computational Basics Underlying Synthetic Strategies

H2C

CH2

H2C

CH2

(2)

Let B be the be-matrix for the starting materials and E the be-matrix for the end

products (Scheme 8).

Scheme 8

The transformation of B into E is defined by the reaction matrix R of equation (3)

such that: B + R = E where off-diagonal entries Rij = Rji = 0, ±l, ±2, ±3, indicate the

bonds made or broken. This method is conceptually attractive because the retro-

synthesis is simply: B = E - R.

-1

(3)

-1

The numerical approach is dealt by different ways by different programs. The

SYNGEN program is based upon the concept of half-reactions; the formation of a

bond may be seen as two linked half-reactions on each side of the bond (Scheme 9).

Synthetic Strategies in Chemistry

17.15

Scheme 9

C-

Electrophilic

Nucleophilic

Half-reaction

Only three atoms are considered around the bond which is formed, because more than

three carbons which change in a half-reaction are virtually never found [11]. The

nucleophilic centers are given in Scheme 10, and the electrophilic ones in Scheme 11.

Scheme 10

Thus, combining these three nucleophilic half-reactions with the three electrophilic

half-reactions produces nine possible full construction reactions. A reaction may also

be described by two letters, the first for the bond made, second for that broken.

Scheme 11

For the Michael reaction (Scheme 12), this concept yields for atom 2: formation of an

σ bond (+ R) and loss of hydrogen (- H), the reaction on this carbon is designated by

RH; for atom 3: formation of an σ bond and loss of a š bond: R Π and for atom 4: H

Π.

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Computational Basics Underlying Synthetic Strategies

Scheme 12

Since there are four kinds of bond (H, R, Π, Z), there are 16 possible unit exchanges

per carbon. This code allows a simple classification of reactions, for example ZH

represents oxidation reactions (CH →C-OH), HZ represents reduction, the opposite of

the previous one; addition reactions on a carbon-carbon double bond are: HΠ, RΠ,

ZΠ, etc. This systematic definition of organic reactions provided a basis for

developing COGNOS, a program for organizing and retrieving reactions in a large

database [12].

III. Pruning the Synthesis Tree

The descriptions which are given above concern the coding of one reaction and how a

precursor may be generated. In a basic retro-synthesis program, each reaction of the

file (or of the files) is applied in turn for generating precursors and building, step by

step, the synthesis tree. As indicated, the number of solutions found may be large and

it is necessary to develop methods that are strategies, in order to reduce this tree and

to try to select the best solutions.

The search of the key-step in a synthesis is, rather, a fundamental step. When it

has been found, one may say that a general plan of the synthesis has been found. This

search has been done in a very simple way in the SAS program: this program simply

deleted one or several bonds in the target, suggesting ideas of synthesis. For example,

in the case of ellipticine (Compound D), it suggested that several internal Diels-Alder

reactions are involved. Solutions similar to E and F have been subsequently and

independently found experimentally by others groups of researchers [13] (Scheme

13).

Synthetic Strategies in Chemistry

17.17

Scheme 13

The other Strategies also include starting material oriented strategy, topological

Strategies, stereo-chemical Strategies and Tactical Combinations of Transforms

Strategy for pruning the synthesis tree for organic compounds. The idea derived

from this is also applicable to synthesis of Inorganic metal complexes,

Energetic materials, Bio-molecules and drug design which are briefly

described in the following section.

2. INORGANIC MATERIALS

(a) Structure Identification:

In principle, systems for representing organic compounds can be applied to inorganic

substances, as far as compounds are concerned in which the atoms are connected by

covalent bonds. The Chemical Abstracts Service (CAS) registry system uses the

notion of a mixture to accommodate substances without structure, e.g., alloys,

whereas covalently bonded inorganic compounds represented by the connection table

(CT) based system of CAS. Salts are represented as mixtures of the possibly

structured ions constituting the salt.

In organic compounds an atom is generally connected to at most four

neighbouring atoms where as in inorganic compounds larger coordination numbers is

found and, consequently, a more involved stereochemistry is encountered.

Furthermore, one has to deal with more complicated types of chemical bonds and

sometimes there are well defined substances for which it is not obvious how to draw a

structure at all. Whereas the subjects of organic chemistry are exclusively covalently

bonded compounds of carbon, inorganic compounds furthermore comprise ionically

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bonded substances like salts, alloys and glasses, different kinds of solutions, minerals,

etc. Therefore a representation of inorganic compounds must provide some means to

deal not only with covalently bonded compounds but also with substances which

belong to those other types [14].Thus, the generalization leads the concept of multi-

component systems in which each component consists of one or several fragments.

The fragments turn may be structured or not, i.e., it may be possible to draw a

structure diagram for them or not. In the structure storage system of the Gmelin

Database, compounds without a structure are taken into account by a tabular

representation, the inorganic structure tables (1ST). The hierarchy is shown in Fig.

17.4.

Substance

Figure 4

Component 1

Component n

Fragment n

Fragment 1

Yes

Structure ?

IST

(b) Methodology

Molecular mechanics (MM) calculations have become increasingly important in

understanding the structures and steric interactions that occur in inorganic,

bioinorganic, and organo-metallic compounds. Since 1984, the growing use of the

MM model in inorganic chemistry has been documented in a number of reviews [15].

Metal complexes that have been treated with MM typically involve one metal and

multidentate organic ligands. The common functional groups found in these ligands

include amines, imines, pyridines, amino acids, alcohols, ethers, carboxylic acids,

thiols, sulfides, and phosphines. Many MM models, developed originally for

Synthetic Strategies in Chemistry

17.19

application to organic molecules, have the capability to treat the ligand part of the

metal complex. These organic MM models have been extended to include metal-

ligand interactions with the addition of relatively few terms. Various approaches are

distinguished by the types of metal-dependent interaction that are used.

The two bonded methods in common use are the valence force field (VFF)

method and the points-on-a-sphere (POS) method. In both methods the metal ion, M,

is formally connected to the ligand donor atoms, L. In a normal organic MM program,

this connection will create M - L bonds, M - L- X and L-M-L bond angles, M-L-X-X

and X-M-L-X torsions, and M - X non-bonded interactions. The user decides which

of these interactions will be used in the calculation through a choice of parameters.

The VFF and POS methods differ only in the treatment of L- M - L angles. In the

VFF method, L- M - L bond angle interactions are used to define a geometry prefer-

ence about the metal center. In the POS method, L- M - L bond angle interactions are

not used and geometry preference about the metal center derives primarily from 1, 3

van der Waals interactions between the donor atoms. The third method is non-bonded

method where the metal ion is not formally connected to the ligand donors; the metal-

ligand complex is modeled with a collection of pair-wise electrostatic and van der

Waals interactions. The three methods which are commonly used for denoting these

interactions are summarized in Table 17.4.

Table 17.4. Methods to extend MM models for metal complexes

Interaction

Bonded

Non-bonded

VFF

POS

Ionic

M-L stretch

yes

M-L-X bend

yes

L-M-L bend

yes

L-L non-bonded

yes

M-L-X-X torsion

yes

L-M-L-X torsion

M-L non-bonded

yes

M-X non-bonded

yes

Apart from this, the geometry optimization and frequency analysis will give an idea

about the stability of the molecules for laboratory synthesis. It is also possible to get

structure-reactivity relationship for metal complexes. The concept of structure-

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reactivity relationship implies that changes in structure should be quantitatively

reflected in some measurable reactivity parameters associated with the molecule. For

metal complexes, the capacity of the ligand structure to influence chemical properties

is measurable in terms of reactivity parameters such as stability constants, rates of

ligand dissociation, and reduction potentials. The influence of structure on chemical

reactivity can often be rationalized in terms of steric and electronic components which

module the synthesis of a particular compound. Same way, the structure-reactivity

plays important role in constructing energetic materials which is outlined below.

3. ENERGETIC MATERIALS

Energetic materials encompass different classes of chemical compositions of fuel and

oxidant that react rapidly upon initiation and release large quantities of force (through

the generation of high-velocity product species) or energy (in the form of heat and

light). These particular features have been advantageously employed in a wide variety

of industrial and military applications, but often these utilizations have not been fully

optimized, mainly due to the inability to identify and understand the individual

fundamental chemical and physical steps that control the conversion of the material to

its final products. The conversion of the material is usually not the result of a single-

step reaction, or even a set of a few simple consecutive chemical reactions. Rather, it

is an extremely complex process in which numerous chemical and physical events

occur in a concerted and synergistic fashion, and whose reaction mechanisms are

strongly dependent on a wide variety of factors.

Also, these processes often occur under extreme conditions of temperature and

pressures, making experimental measurement difficult. These are but a few of the

complexities associated with studies of reactions of energetic materials that make

resolving the individual details so difficult. These difficulties have required the

development of a variety of innovative theoretical methods, models and experiments

designed to probe details of the various phenomena associated with the conversion of

energetic materials to products [16].

The high time and pecuniary costs associated with the synthesis or formulation,

testing and fielding of a new energetic material has called for the inclusion of

modeling and simulation into the energetic materials design process. This has resulted

in growing demands for accurate models to predict properties and behavior of

notional energetic materials before committing resources for their development. For

example, in earlier times, extensive testing and modification of proposed candidate

Synthetic Strategies in Chemistry

17.21

materials for military applications could take decades before the material was actually

fielded, in order to assure the quality and consistent performance of the Predictive

models that will allow for the screening and elimination of poor candidates before the

expenditure of time and resources on synthesis and testing of advanced materials

promise significant economic benefit in the development of a new material.

4. BIO-MOLECULES

Computational bio-modeling refers to a type of artificial life research concerned

with building computer simulations of biological systems (bio-modeling). The

immediate goal is to understand how biological entities such as cells or whole

organisms, develop, work collectively, and survive in changing environments using a

purely computational model. In order to meet this challenge we need to establish the

methodologies and techniques that will enable us to gain a system-level understanding

of biological processes. These kinds of models hold great promise for new discoveries

in a wide variety of biological systems. Once an executable model has been built of a

particular system, it can be used to get a global dynamic picture of how the system

responds to various perturbations. In addition, preliminary studies can be quickly

performed using executable models, saving valuable laboratory time and resources for

only the most promising avenues.

One way of approaching this problem is to use ideas and methods originally

developed in computer science, mainly in software and systems engineering (and in

particular visual languages and formal verification) to construct, simulate and analyze

biological models. The benefit of molecular modeling is that it reduces the complexity

of the system, allowing many more particles (atoms) to be considered during

simulations. The types of biological activity that have been investigated using

molecular modeling include protein folding, enzyme catalysis, protein stability,

conformational changes associated with bio-molecular function, and molecular

recognition of proteins, DNA, and membrane complexes.

Softwares for bio-models

SPiM

The Stochastic Pi Machine (SPiM) is a simulator for the stochastic pi-calculus that

can be used to simulate models of Biological systems. The machine has been

formally specified, and the specification has been proved correct with respect to

the calculus.

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Computational Basics Underlying Synthetic Strategies

ScatterWeb.NET SDK

ScatterWeb.NET SDK is a new approach to working with wireless sensor

networks. It hides the complexity of embedded programming and makes it easy to

handle objects representing wireless sensors.

5. DRUG DESIGN

Drug design is presently the most prominent and visible application of information

processing in chemistry. Clearly this is due to the large scientific and economic

investment necessary for the development of a new drug. Efforts are made to combine

all possible means for developing an understanding of the relationships between

structure and biological activity. Consequently, this is an area where quantum

mechanical and molecular mechanics calculations are effectively employed.

CONCLUSIONS

The collected examples explain the use of computational methods in synthesis of

organic compounds and also extendable to inorganic and energetic materials.

Researchers are focusing on creating the computational tools that will enable

biologists and others working in the life sciences to better understand and predict

complex processes in biological systems, which could revolutionize our

understanding of disease, and lead to new and faster insights into entirely novel

therapies and better vaccines.

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Abbreviations

NMR Nuclear Magnetic Resonance

GAMESS- The General Atomic and Molecular Electronic Structure System

MOPAC- Molecular Orbital PACkage

SYNGEN- SYNthesis GENerator

SOS-Simulated Organic Synthesis

SAS-Simulated Analytical Synthesis

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