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Knowledge Based Reactions

Knowledge Based Reactions. Outside of quantum mechanical modeling, typical method is to explicitly specify the participating reactant atoms and how bonds are rearranged as a reaction profile. O. O. H. NH 2 -R 2. +. + H 2 O. OH. R 1. NH 2 -R 2. R 1. Sample Reaction Profile.

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Knowledge Based Reactions

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  1. Knowledge Based Reactions • Outside of quantum mechanical modeling, typical method is to explicitly specify the participating reactant atoms and how bonds are rearranged as a reaction profile O O H NH2-R2 + + H2O OH R1 NH2-R2 R1 Sample Reaction Profile

  2. Knowledge Based Limitations • Requires manual pre-specification of many different known reaction profiles to achieve any degree of generalization + + + + +

  3. Knowledge Based Limitations A B C D A B C D • A very generic reaction profile can cover everything from previous slide and indeed about 50% of all known organic reactions • Still, a screening or ranking method is needed to filter many unrealistic reactions proposed above • Many reactions with more sophisticated profiles are not covered without manually specifying more knowledge based profiles • Diels-Alder • Azide + Alkyne aromatic cyclization

  4. Reaction Favorability Scoring • Simple scoring method to suggest predicted reaction feasibility and favorability based on a simplification of Hess’ law DHreaction = S(BDEbroken) – S(BDEformed) • Estimate change in enthalpy by looking up bond-dissociation energies (BDE) • Apply additional bonuses and penalties for aromaticity and ring strain

  5. Pseudo-Mechanistic Reactions • First step towards more generalized, pseudo-mechanistic reaction modeling is the introduction of “intermediates” • Model breaking a bond in each reactant by separating charge, representative of the bond electrons moving to one atom • Closing the intermediates is then just a matter of matching + and - charges A B C D A+ B- C- D+ A B C D

  6. Pseudo-Mechanistic Reactions O- H+ O H O O - H+ • Applying general electron-shifting rules on the intermediates before closing them into products provides a lot of power and chemically intuitive results

  7. Azide + Alkyne Example R1 N N+ N- R2 C C R3 R1 N N N R1 N+ N N- C C R2 R3 R2 C- C+ R3

  8. Diels-Alder Example C C C C + C C C- C C+ C + C- C+ C C C+ C- + C- C+

  9. ChemDB Architecture finger.ics.uci.edu 2. Similarity Query 1. HTTP Request 3. Similarity Results cdb.ics.uci.edu Client 4. DB Query 6. HTML Results 5. DB Results chemdb.ics.uci.edu

  10. ChemDB Architecture • cdb.ics.uci.edu • Linux, 1GB • Primary Integration Point • Web Server (Apache) • Image Server (internal smi2gif.py script) • Issue Tracker (Scarab, Tomcat JSP, PostgreSQL DB) • chemdb.ics.uci.edu • MS Windows Server 2003 • Database Server (PostgreSQL) • finger.ics.uci.edu • Linux, 4GB • Similarity Search Server (Internal FINGER module)

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