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  • Benchmarking reaction energies, barrier heights, and noncovalent interaction energies

    Speaker: Professor Gershom (Jan M.L.) Martin
    Institute: Weizmann Institute of Science
    Country: Israel
    Speaker Link:

    Gershom (Jan M.L.) Martin

    Department of Organic Chemistry
    Weizmann Institute of Science
    7610001 Reḥovot, Israel

    Video Recording

    Video is available only for registered users.


    While density functional theory has made great strides, even the best exchange-correlation functionals are about one order of magnitude less accurate than can be achieved using modern wavefunction ab initio techniques. The latter have a well-defined road map for refinement in accuracy; however, their steep computational cost scaling with system size limits their use to relatively small molecules. While some applications (e.g., atmospheric chemistry, fine thermochemistry) demand such levels of accuracy, a perhaps more important application is the creation of benchmark datasets for the parametrization and validation of density functional, reactive force fields, and other lower-cost methods.
    Using the case of total atomization energies, we will discuss the breakdown of molecular binding energies into their constituent components, as well as the optimal convergence strategy for each. By such “layered approximations” as implemented in the Weizmann-n series of thermochemistry protocols [1,2,3] (and its ‘competitor’, the HEAT approach [4]), CPU times and memory requirements can be drastically reduced versus brute-force approaches. The introduction of explicitly correlated coupled cluster theory brings still larger molecules within reach, as long as non dynamical correlation effects are not too important. (See [5] for a discussion of static correlation diagnostics.)

    We will illustrate some of the concepts using the W4-11 atomization energy benchmark [6], the DBH24 barrier heights benchmark [7], the HFREQ27 vibrational frequencies benchmark [8], and several recent benchmarks for noncovalent interactions such as the S66x8 set of biomolecule dimer potentials, [9], conformational energies of the proteinogenic amino acids, [10], and water clusters [11].

  • Computational medicinal chemistry

    Speaker: Professor Robert J. Doerksen
    Institute: University of Mississippi
    Country: USA
    Speaker Link:

    Dr. Robert J. Doerksen

    Associate Dean, Graduate School
    Associate Professor of Medicinal Chemistry, Department of BioMolecular Sciences
    Research Associate Professor, Research Institute of Pharmaceutical Sciences
    University of Mississippi, University, MS, USA

    Video Recording


    A wide variety of computational chemistry methods are useful in the search for new drugs. These approaches are collectively termed computational medicinal chemistry. A typical small molecule drug (molecular weight < 500 Da) needs to interact with or react with a protein target to achieve its useful pharmacological effect. Its path through the human body can also include changing protonation state, crossing lipid barriers, being carried by proteins, and undergoing metabolic transformations. A series of computational methods can be used to study the progress of a drug through the body in the various stages of pharmacokinetics and pharmacodynamics. Three-dimensional representations of both the drug and of what it interacts with are often helpful. For this, conformational search and methods to calculate and rank the relative energies of conformations are necessary. Many electronic structure properties of the drug molecule can be calculated, which can be used to characterize the molecule and predict its behavior. Protein modeling is also important to carry out, including effective use of experimental structural information. The conformations of drug and target can then be used in molecular docking which in turn can serve as a key step in virtual screening to find, from a database of known structures, drug hits with never-before-reported useful pharmacological activity at targets of interest. This presentation will include examples of best-practice application of these methods, such as for identifying selective protein kinase inhibitors or cannabinoid receptor ligands.