designing organic reaction simulation engine using qualitative reasoning approach

ACS'08, 21-23 November, Venice, ITALY

Designing organic reaction simulation engine using

qualitative reasoning approach

Y.C. Alicia Tang Tenaga Nasional University Sharifuddin M. Zain (Chemistry Department, Malaya University)Noorsaadah A. Rahman (Chemistry Department, Malaya University)

MALAYSIA


Contents (I)

Introduction Qualitative reasoning (QR) Problems and motivations

Organic reactions and mechanisms System methodology Previous works Functional components of QRIOM prototype


Contents (II)

Reaction examples tested A reasoning scenario Qualitative reasoning algorithm Qualitative modeling algorithm A QPT process model Results Conclusion and future works


Introduction (I)

This work presents a framework architecture that uses the QPT ontology as the knowledge representation scheme to model the behaviors of a number of organic reactions. We investigated qualitative representation and qualitative

simulation approaches The goal is to develop a learning tool that teaches

an organic chemistry course at the University of Malaya will undergo mental change so that they are able to

explain chemical phenomenon in a more elaborated way


Qualitative Reasoning

Qualitative Reasoning (QR) research Attempts to model behavior of dynamic physical

systems without having to include a bunch of formulas and/or quantitative data

The research spans all aspects of the theory and applications Techniques, applications, task-level reasoning,

modeling, etc.


Problems and Motivations (I)

Students faced problem in organic reaction mechanism courseThey learn the subject by memorizing the steps

and formulas of each reaction taught in classrooms

Poor conceptual understanding Not knowing the principles governing the

processes and the cause effect interaction among processes


Problems and Motivations (II)

Simulation of chemical reactions that relied on pre-coded facts and rules cannot explain its results Since no tight coupling between concepts and their

embodiment in software

QPT describes processes in conceptual terms and embody notions of causality which is important to explain behavior of chemical

systems.


Organic Reactions & Mechanisms

A reaction mechanism describes the sequence of steps (processes) that occur during the conversion of reactants to productsMechanism is used to explain how a reaction

takes place by showing what is happening to valence electrons during the making and breaking of bonds.

Organic chemists could work out the mechanisms by using knowledge developed from their chemical intuition and experience.


System Methodology

Identifying chemical properties for organic reactions for model composition use

Classifying the possible reaction species and types Developing the automated model construction logic Developing the reasoning steps for predicting and

simulating the chemical behaviors of selected organic reactions

Designing the means for generating explanation


Previous Works

From numerous substrates and reagents, we classify reacting species as either a nucleophile (charged/neutral) or an electrophile (charged/neutral)upon which chemical processes are selected

Two main reusable processes identifiedNamely, the “make-bond” and “break-bond”, for

the entire reaction mechanisms, specifically on SN1 and SN2.

Inputs 1 (Substrates &

Reagents)

2 Substrates Recognizer

4 QPT model constructor

3 Chemical

Knowledge Base 1. Chemical facts 2. Chemical theories 3. OntoRM 4. QPT process models

5 Qualitative Simulator

1. Quantity space analyzer 2. Molecule update routine

6 Explanation Generator (Causal and behavioral)

7

Outputs 1. Final products 2. Reaction routes 3. mechanism used 4. explanation notes

QRIOM:Functional Components


Reaction examples Reasoning cases Mechanisms = 2 Reaction formulas = 3 Specific cases of simulation = 28

SN1 Tertiary alcohol + Hydrogen halide (CH3)3COH HX

CH3CH3CH3COH + HF CH3CH3CH3COH + HCl CH3CH3CH3COH + HBr CH3CH3CH3COH + HI

Alkyl halide (tertiary) + Water molecules (CH3)3CX 2H2O (in excess)

(CH3)3CF + 2H2O (CH3)3CCl + 2H2O (CH3)3CBr + 2H2O (CH3)3CI + 2H2O


SN2

Alkyl Halide (primary) + Incoming nucleophileCH3CH2X Hydroxyl functional group

CH3F + HO-CH3Cl + HO-CH3Br + HO-CH3I + HO-CH3CH2F + HO-CH3CH2Cl + HO-CH3CH2Br + HO-CH3CH2I + HO-CH3CH2CH2F + HO-CH3CH2CH2Cl + HO-CH3CH2CH2Br + HO-CH3CH2CH2I + HO-CH3CH2CH2CH2F + HO-CH3CH2CH2CH2Cl + HO-CH3CH2CH2CH2Br + HO-CH3CH2CH2CH2I + HO-


A QPT-basedreasoning scenario


The production of alkyl halide

A = tert-Butyl alcohol, B = Hydrogen chloride

C= tert-Butyl chloride, D = Water molecule

(CH3)3COH + HCl (CH3)3CCl + H2O (1) A B C D


Step 1: Protonation of tert-Butyl alcohol by H+. This is a “make-bond” process. .. .. .. ..

(CH3)3C – O: + H – Cl : (CH3)3C–O+–H + : Cl :

| .. | .. H H tert-butyl alcohol hydrogen chloride tert-butyloxonium ion chloride ion Step 2: Dissociation of tert-butyloxonium ion. This is a “break-bond” process. .. .. (CH3)3C O+ H CH3)3C

+ + : O–H | |

H H tert-butyloxonium tert-butyl cation water Step 3: Capturing of tert-butyl cation by chloride ion. This is a “make-bond” process. .. ..

(CH3)3C+ + : Cl :

(CH3)3C – Cl :

.. .. tert-butyl cation chloride ion tert-butyl chloride


Qualitative Simulation

TopLevelDesign

QUALITATIVE SIMULATION ALGORITHM Q_Simulation(substrate, reagent, OUTPUT) 1. Recognizing substrate 2. Suggesting a chemical process 3. Constructing QPT models 4. Processes reasoning Initialize multiple data structures Perform quantity space analysis Check qualitative proportionalities in Relation-slot Refer to quantity spaces for each view used in the process Store propagated effects in data structures Store new individuals (intermediates) that produced Update all affected data structures 5. If view_structure <> EMPTY Then Go to step 2 Else Suggest mechanism used in the simulation Show overall reaction route Display final products End If 6. Generating explanations based on users’ queries

See next slide


Qualitative Modeling

QPT VIEWS AND PROCESSES MODELING ALGORITHM Qualitative_Modeling(substrate, reagent, QPT_MODEL)

1. Examine user inputs (substrate and reagent) 1.1 Decompose inputs into functional units 2. Recognize functional units 2.1 Assign units to either nucleophile or electrophile group 3. Retrieve general properties of the groups Compose the Individual Views Put views in View Structure (VS) 4. Check Individual Views in the VS 4.1 Find a pair of individual views 4.2 Suggest a chemical process for the pair 5. Retrieve general properties for the selected process 5.1 Compose the slots for a QPT process 6. Stop


Process “Protonation” (e.g. ((CH3)3C-OH) is protonated by H+) Individuals ;there is an electrophile (charged) 1. H ; hydrogen ion ; there is a nucleophile (neutral) that has lone pairs electron 2. O ; alcohol oxygen Preconditions 3. Am [no-of-bond(O)] = TWO 4. is_reactive(R3C-OH) 5. leaving_group(OH, poor) ;check KB for good/poor leaving group Quantity-Conditions 6. Am[non-bonded-electron-pair(O)] >= ONE 7. charges(H, positive) 8. electrophile(H, charged) 9. nucleophile(O, neutral) 10. charges(O, neutral) Relations 11. Ds[charges(H)]= -1 12. Ds[charges(O)]= 1

13. lone-pair-electron(O) P no-of-bond(O)

14. charges(O) P lone-pair-electron(O)

15. lone-pair-electron(H) P no-of-bond(H)

16. charges(H) P no-of-bond(H)

Influences 17. I+ (no-of-bond(O), Am[bond-activity])

Functional dependency implemented as

qualitative proportionality modeling construct

A “make-bond” process

in QPT terms


Main interface of QRIOM


A Qualitative Model


The ability to generate causal explanation has been one of the promises of the QR approach

Causal graph generated during

reasoning


The approach enables prediction to be made, as well as causal explanation generation about theories of many chemical phenomena

Cause-effect chain can be explained by using only the ontological primitives of QPT

Results discussion (I)


The explanation generation is causal in nature and is run-time based (not pre-coded)

Model inspection and reasoning can help to enhance a learner’s critical thinking and reasoning ability

Results discussion (II)


Conclusions (I)

The qualitative models in QRIOM communicated knowledge that is common to chemistry people (via the QPT constructs).

The new computational approach can serve as alternative learning technology in developing educational software for subjects that require application of domain knowledge at intuitive level.


Conclusions (II)

What the software can provide?the software can predict final productsthe software can explain its reasoningno pre-coded solution path or search path

From a learner’s point of viewconceptual understanding is improvedreasoning ability is sharpened


Future works

To build a graphical interface that functions much as a protocol converter betweenReasoning shellGraphical outputs

To include user modeling in the softwareTowards an intelligent tutoring system


The End.

Thank you GrazieTerima Kasih

Questions?

COIT building, UNITEN

Faculty members

designing organic reaction simulation engine using qualitative reasoning approach

Documents

qualitative reasoning

qualitative representation

number of organic reactions

organic chemists

organic chemistry course

motivationsorganic reactions

behavior of chemical

chemical phenomenon