![CITED LITERATURE
Lynch E, Speelman A, Curry B, Murillo C, Gillmore J. 2012. Expanding and
Testing a Computational Method for Predicting the Ground State
Reduction Potentials of Organic Molecules on the Basis of Empirical
Correlation to Experiment. ACS. 77(15):6423–6430.
Méndez Hernández D, Gusta D, Moorea T, Gillmorea J, Montano L, Moore A,
Mujica, V. 2015. Building and testing correlations for the estimation
of one-electron reduction potentials of a diverse set of organic
molecules. Physical Organic Chemistry. 28(5):320–328.
Reece J, Urry L, Cain M, Wasserman S, Minorsky P, Jackson R. 2014.
Campbell Biology. 10th ed. Glenview, IL: Pearson Education.
28-56 p.
Rusdi, R. Basic definition of computational chemistry [Internet]. [Updated
2007 February 9]. [cited 2016 May 4]. Available from: https://
stalischem.wordpress.com/2007/02/09/basic-definition-of-
computational-chemistry/](https://image.slidesharecdn.com/computationalchemistryfinalpresentation-160714180129/85/Computational-Chemistry-Workshop-Presentation-Final-revised-29-320.jpg)
Computational Chemistry Workshop Presentation (Final, revised)
- 1. COMPUTATIONAL CHEMISTRY CARLOS J. CABELLO LÓPEZ NICOLE M. CRUZ REYES STEPHANNIE ROSARIO GARRIDO ADRIANA N. SANTIAGO RUIZ PROF. DALVIN MÉNDEZ UNIVERSITY OF PUERTO RICO IN CAYEY DEPARTMENT OF BIOLOGY RISE PROGRAM
- 2. CONTENT • Introduction: • Definitions • Concepts • Programs Definition • Problem & Hypothesis • Methodology & Results • Discussion: • What do our results mean? • Comparisons • New Application • Conclusion • Cited Literature
- 3. INTRODUCTION
- 4. COMPUTATIONAL CHEMISTRY • Computational Chemistry is a branch of chemistry that uses the product of theoretical chemistry that is translated into computational programs to calculate molecular properties and its changes and also to perform simulation to macromolecular systems. (Rusdi 2007)
- 5. BASIC CONCEPTS • Chemical compounds Electronegativity • Molecular compounds • Ionic compounds • Polarity • Redox Potential • LUMO/HOMO M+ NM-
- 6. SCHRÖDINGER’S EQUATION • H=Hamiltonian operator, total energy of the electron within the atom • E=Actual energy of the electron • Ψ=wave function, describes the wavelike nature of the electron (Tro 2014). By modifying H, various molecular properties can be studied. Hψ = Eψ
- 7. PROGRAMS • Gabedit (ORCA) • Excel • R Studio
- 8. OBJECTIVES • Be able to obtain a knowledge about computational chemistry, its relevance and application’s in science • Learn to use the Gabedit program • Calculate the redox potential of certain molecules • Assigned • Created • Modified • Analyze the final data using R Studio
- 9. PROBLEM If the molecules are correctly drawn and the proper chemical group was added to each model, will the experimental values of the redox potentials match the postulate values?
- 10. HYPOTHESIS By analyzing the obtained data of the molecular models with the Gabedit (ORCA) program, and using the R Studio to create a graph of the results, the Redox potentials will match their postulate values.
- 12. PROCEDURES
- 13. PROCEDURE #1 1. Draw the molecule in Gabedit (this is mainly visual) 2. Close the tab where the molecule is drawn 3. Click on “ORCA” 4. Write an input file a. Copy the results the program gives b. Paste the results in any text-editor program (TextEdit (Mac), Notepad (Windows), there are many options to choose from) i. Use the correct format to write before and after the results pasted c. Save the document with “.inp” 5. Write the “sh” file a. With an sh file already written change the title of the files being used b. Make sure the names are written correctly since those files will be used by the Super Computer to produce the final file 6. Use the files to reproduce the results using a Super Computer
- 14. RESULTS #1 0 0.5 1 1.5 2 2.5 0 1 2 3 4 DipoleMoment(Debye) # of Cl Dipole Moment vs # Cl Atoms exp HF DFT normalized guess
- 17. PROCEDURE #2 1. Choose a molecule (or create a completely new one) 2. Add an Electron Donating Group or an Electron Withdrawing Group 3. Make a prediction of the redox potential 4. Compare the prediction with the results obtained
- 18. RESULTS #2 • Explain problem
- 19. RESULTS #2
- 20. PROCEDURE #3 1. Use the theoric results from the paper titled “Building and testing correlations for the estimation of one-electron reduction potentials of a diverse set of organic molecules.” (Méndez-Hernández et al. 2014) 2. Make a table with the theoric and the experimental results obtained from procedure #1. 3. Compare and analyze the values on the table
- 21. RESULTS #3 Table 4: Results of Dipole Moments (used for Graph 2) Name Molecule LUMO E (eV) red. Potential (V) Adriana N. Santiago-Ruiz 1 -2.0457 -1.9 Adriana N. Santiago-Ruiz 2 -2.5354 -1.6 Adriana Vera-Rios 3 -3.2039 -1.1 Adriana Vera-Rios 4 -3.2977 -0.95 Carlos Cabello-Lopez 5 -3.9205 -0.75 Carlos Cabello-Lopez 14 -3.0212 -0.8 Carlos Villagrasa-Mendez 15 -3.1292 -0.75 Carlos Villagrasa-Mendez 16 -3.2487 -0.63 Cristina Rivera-Quiles 17 -3.3917 -0.58 Cristina Rivera-Quiles 18 -3.5354 -0.47 Edmaritz Hernandez-Pagan 19 -3.7898 -0.34 Edmaritz Hernandez-Pagan 20 -4.0163 -0.18 Emmanuel Santiago-Burgos 21 0 Emmanuel Santiago-Burgos 22 -4.1968 0.02 Gabriel Pastrana-Castellanos 23 -4.2725 0.05 Gabriel Pastrana-Castellanos 24 -4.8039 0.28 Joanly Rivera-Ortiz 25 -5.0953 0.59 Joanly Rivera-Ortiz 26 -5.767 0.9 Laura Diaz-Mendez 39 -1.4753 -2.1 Laura Diaz-Mendez 40 -1.4425 -2.15 Marangely D Martinez- Justiniano 42 -1.3747 -2.2 Marangely D Martinez- Justiniano 43 -1.1817 -2.22 Natalia D Rivera-Sanchez 44 -1.8144 -2.4 Natalia D Rivera-Sanchez 69 -0.613 -2.636 Nicole Cruz-Reyes 72 -1.4182 -2.08 Nicole Cruz-Reyes 45 -0.9618 -2.66 Rafael J Cummings-Lopez 48 -1.6345 -2.1 Rafael J Cummings-Lopez 6 -1.8068 -1.98 Stephannie M Rosario-Garrido 7 -1.8144 -1.96 Stephannie M Rosario-Garrido 8 -1.8867 -1.88 Valeria Laboy-Collazo 9 -2.7621 -1.27 Valeria Laboy-Collazo 49 -0.9959 -2.62
- 22. GRAPH (EXCEL)
- 23. THE “HOLISTIC” TABLE Molecule LUMO E (eV) red. Potential (V) Theorical estimate residual residual^2 1 -2.0457 -1.9 -1.77684976 -0.1231502 0.01516598 2 -2.5354 -1.6 -1.42269872 -0.1773013 0.03143574 3 -3.042 -1.1 -1.0563256 -0.0436744 0.00190745 4 -3.144 -0.95 -0.9825592 0.0325592 0.0010601 5 -3.648 -0.74 -0.6180664 -0.1219336 0.0148678 14 -3.4684 -0.8 -0.74795312 -0.0520469 0.00270888 15 -3.5674 -0.75 -0.67635632 -0.0736437 0.00542339 16 -3.6795 -0.63 -0.5952856 -0.0347144 0.00120509 17 -3.3917 -0.58 -0.80342256 0.22342256 0.04991764 18 -3.5354 -0.47 -0.69949872 0.22949872 0.05266966 19 -4.2206 -0.34 -0.20396208 -0.1360379 0.01850632 20 -4.4852 -0.18 -0.01260336 -0.1673966 0.02802164 21 -4.754 0 0.1817928 -0.1817928 0.03304862 22 -4.1968 0.02 -0.22117424 0.24117424 0.05816501 23 -4.2725 0.05 -0.166428 0.216428 0.04684108 24 -4.8039 0.28 0.21788048 0.06211952 0.00385883 25 -5.095 0.59 0.428404 0.161596 0.02611327 26 -5.767 0.9 0.9143944 -0.0143944 0.0002072 39 -1.4753 -2.1 -2.18936304 0.08936304 0.00798575 40 -1.4425 -2.15 -2.213084 0.063084 0.00397959 42 -1.3799 -2.2 -2.25835632 0.05835632 0.00340546 43 -1.1773 -2.22 -2.40487664 0.18487664 0.03417937 44 -1.1465 -2.4 -2.4271512 0.0271512 0.00073719 69 -0.613 -2.636 -2.8129784 0.1769784 0.03132135 72 -1.4182 -2.08 -2.23065776 0.15065776 0.02269776 45 -0.9618 -2.66 -2.56072624 -0.0992738 0.00985528 48 -1.9085 -2.1 -1.8760728 -0.2239272 0.05014339 6 -2.2757 -1.98 -1.61051376 -0.3694862 0.13652008 7 -1.8144 -1.96 -1.94412592 -0.0158741 0.00025199 8 -1.8867 -1.88 -1.89183856 0.01183856 0.00014015 9 -2.7621 -1.27 -1.25874928 -0.0112507 0.00012658 49 -0.9959 -2.62 -2.53606512 -0.0839349 0.00704506 RMSD abs(residual) LAD 0.12315024 0.17730128 0.0436744 0.0325592 0.1219336 0.05204688 0.07364368 0.0347144 0.22342256 0.22949872 0.13603792 0.16739664 0.1817928 0.24117424 0.216428 0.06211952 0.161596 0.0143944 0.08936304 0.063084 0.05835632 0.18487664 0.0271512 0.1769784 0.15065776 0.09927376 0.2239272 0.36948624 0.01587408 0.01183856 0.01125072 0.08393488 0.14785051 0.12059179 y = -0.7232x - 3.2563 R² = 0.97897 -7 -6 -5
- 24. PROCEDURE #4 1. Import Excel file with the organized collected data a. Important: choose ‘x’ and ‘y’ columns correctly 2. Use the command in order to tell the program to read all the data needed 3. Create a linear model with the imported data 4. Create a column with the predicted values using the explanatory variable (le) 5. Calculate the error stats a. In order to obtain the most accurate amount of data 6. Create the plotting graph with the imported data a. It is possible to customize the graph with colors 7. Calculate the Root Mean Standard Deviation (RMSD) and the Mean Absolute Deviation (MAD) 8. Label the axis with the corresponding titles based on the data being graphed 9. Export graph and save the file
- 25. RESULTS GRAPHED WITH R −6 −5 −4 −3 −2 −1 0 −4−202 Ered vs. Î LUMO Î LUMO / (eV) Ered/(V) R2 = 0.979 Ered = -0.72 Î LUMO - 3.3
- 26. −6 −5 −4 −3 −2 −1 0 −4−202 Ered vs. Î LUMO Î LUMO / (eV) Ered/(V) R2 = 0.979 Ered = - 0.72 Î LUMO - 3.3 COMPARISON VS
- 27. CONCLUSION • We were able to obtain a knowledge about computational chemistry, its relevance and application’s in science • Learned to use the Gabedit program • Calculated the redox potential of certain molecules • Assigned • Modified • Redox potential matched the reported values • Hypothesis was proven • Analyzed the final data using R Studio • Benefits of using the programs
- 28. APPLICATIONS • Medical applications • What is the benefit of making a drug harder to oxidize? • Metabolism of drugs (Vogel & Catherine)
- 29. CITED LITERATURE Lynch E, Speelman A, Curry B, Murillo C, Gillmore J. 2012. Expanding and Testing a Computational Method for Predicting the Ground State Reduction Potentials of Organic Molecules on the Basis of Empirical Correlation to Experiment. ACS. 77(15):6423–6430. Méndez Hernández D, Gusta D, Moorea T, Gillmorea J, Montano L, Moore A, Mujica, V. 2015. Building and testing correlations for the estimation of one-electron reduction potentials of a diverse set of organic molecules. Physical Organic Chemistry. 28(5):320–328. Reece J, Urry L, Cain M, Wasserman S, Minorsky P, Jackson R. 2014. Campbell Biology. 10th ed. Glenview, IL: Pearson Education. 28-56 p. Rusdi, R. Basic definition of computational chemistry [Internet]. [Updated 2007 February 9]. [cited 2016 May 4]. Available from: https:// stalischem.wordpress.com/2007/02/09/basic-definition-of- computational-chemistry/
- 30. ACKNOWLEDGEMENTS • RISE Program • Dr. Díaz • Dr. Ross • Dr. Bansal • Special thanks to… • Dr. Méndez
- 31. QUESTIONS?