Elfi Kraka
CD4 project:
Towards the next generation of computer aided drug design: From macro- to microscale
The discovery of a novel drug requires approximately 12 years and around 1 billion dollars. There is the great expectation that computer aided drug design (CADD) can help predict the biological response with regard to a target protein. This complex task requires an advanced strategy ranging from screening billions of drug-like molecules as potential candidates, de novo design of new leads based on specific target properties (macroscale level) to the high accuracy calculation of target-lead properties, including binding energies and target-lead hydrogen bonding, at the quantum chemical level (microscale level). Current computational approaches generally operate either on the microscale or the macroscale end of the spectrum. The Computational and Theoretical Chemistry Group (CATCO) at SMU has the knowledge, expertise, computer software, and computational resources at SMU’s high-performance computer center to close this gap and to connect the loose ends. We are designing an open-source platform of computer programs incorporating novel machine learning techniques covering for the first time the whole range of the CADD process in a holistic way. Our ambitious goal is to speed up the lead selection process on the macroscale end in synergy with providing chemical accuracy information about the target-lead complex on the microscale end.
Pilot projects include i) The design of the next generation of EGFR inhibitors for treatment of non-small-cell lung cancer; ii) The identification and assessment of protein mutations on drug efficiency applied to the pyruvate kinase protein PKM2 and to mutations of cardiac troponin influencing calcium regulation leading to irregular heartbeat; iii) The exploration of new Dengue fever drug candidates.
Recent related publications:
Correlating the Vibrational Spectra of Structurally Related Molecules: A Spectroscopic Measure of Similarity,
Y. Tao, W. Zou, D. Cremer, and E. Kraka,
J. Comput. Chem, 39, 293-306 (2018)
New Mechanistic Insights into the Claisen Rearrangement of Chorismate - A Unified Reaction Valley Approach Study
M. Freindorf, Y. Tao, D. Sethio, D. Cremer, and E. Kraka
Mol. Phys., 117, 1172-1192 (2019)
Correlation between molecular acidity (pKa) and vibrational spectroscopy
N. Verma, Y. Tao, B. Luana Marcial, and E. Kraka
J. Mol. Model, 5, 48-1-48-15 (2019)
The Interplay of Ring Puckering and Hydrogen Bonding in Deoxyribonucleosides
S. Lyu, Siying, N. Beiranvand, M. Freindorf and E. Kraka
J. Phys. Chem. A, 123, 7087-7103 (2019)
Local vibrational force constants - from the assessment of empirical force constants to the description of bonding in large systems,
W. Zou, Y. Tao, M. Freindorf, D. Cremer and E. Kraka
Chem. Phys. Lett., 748, 137337 (2020)
Quantitative Assessment of Intramolecular Hydrogen Bonds in Neutral Histidine
S. Yannacone, D. Sethio, and E. Kraka
Theor. Chem. Acc., 139, 125 (2020)
A Critical Evaluation of Vibrational Stark Effect (VSE) Probes with the Local Vibrational Mode Theory,
N. Verma, Y. Tao, W. Zou, Xia Chen, Xin Chen, M. Freindorf, and E. Kraka
Sensors, 20, 2358 (2020)
Decoding chemical information from vibrational spectroscopy data: Local vibrational mode theory,
E. Kraka, W. Zou, and Y. Tao,
WIREs: Comput. Mol. Sci., e1480 (2020)
Characterizing the Metal Ligand Bond Strength via Vibrational Spectroscopy: The Metal Ligand Electronic Parameter (MLEP),
E. Kraka and M. Freindorf,
In Topics in Organometallic Chemistry - New Directions in the Modeling of Organometallic Reactions, A. Lledos and G. Ujaque, Eds.: Springer, New York, Vol. 67, 1-43 (2020)
Critical assessment of the FeC and CO bond strength in carboxymyoglobin - A QM/MM Local Vibrational Mode Study,
M. Freindorf and E. Kraka,
J. Mol. Model., 26, 281 (2020)
Exploring the Mechanism of Catalysis with the Unified Reaction Valley Approach (URVA) - A Review
E. Kraka, W. Zou, Y. Tao and M. Freindorf
Catalysts, 10, 691-1-691-32 (2020)
Local Vibrational Mode Analysis of π-Hole Interactions between Aryl Donors and Small Molecule Acceptors
S. Yannacone, M. Freindorf, Y. Tao, W. Zou and Kraka
Crystals, 556-1-556-25 (2020)
Local Vibrational Mode Analysis of Ion-Solvent and Solvent-Solvent Interactions for Hydrated Ca2+ Clusters
A. A. A. Delgado, D. Sethio, I. Munar, V. Aviyente and E. Kraka
J. Chem. Phys., 153, 224303 (2020)
Deep Learning-based Ligand Design using Shared Latent Implicit Fingerprints from Collaborative Filtering
R. Srinivas, N. Verma, E. Kraka and E. C. Larson
BioRxiv, 1-41 (2020)
Mechanosynthesis of a coamorphous formulation of creatine with citric acid and humidity-mediated transformation into a cocrystal
K. B. Pekar, J. B. Lefton, C. McConville, J. Burleson, D. Sethio, E. Kraka and T. Runcevski.
Cryst. Growth Des., 21, 1297-1306 (2021)
SSnet: A Deep Learning Approach for Protein-Ligand Interaction Prediction
N. Verma, X. Qu, F. Trozzi, M. Elsaied, N. Karki, Y. Tao, B. Zoltowski, E. C. Larson and E. Kraka,
Int. J. Mol. Sci. 22, 1392 (2021)
Predicting Potential SARS-COV-2 Drugs-In Depth Drug Database Screening Using Deep Neural Network Framework SSnet, Classical Virtual Screening and Docking,
N. Komal Karki, N. Verma, F. Trozzi, P. Tao, E. Kraka and B. Zoltowski
Int. J. Mol. Sci. 22, 1573 (2021)
Expertise:
Artificial Intelligence
Machine learning
High-accuracy quantum chemical calculations
Enzyme catalysis
Facilities:
https://www.smu.edu/OIT/Services/HPC