Gershom (Jan M.L.) Martin

Department of Organic Chemistry
Weizmann Institute of Science
7610001 Reḥovot, Israel
http://compchem.me

Video Recording

Video is available only for registered users.

Abstract

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].

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

Abstract

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.

Professor György M Keserű

Medicinal Chemistry, Research Center for Natural Sciences, Budapest, Hungary

Covalent drugs are electrophilic molecules that bound to the target protein by forming covalent bond with the targeted nucleophilic residue at the binding site. Formerly, covalent inhibitors were typically filtered out in drug discovery programs due to the risk of off-target activity attributed to their reactivity. Few compounds acting by covalent mechanism of action were discovered serendipitously. However, a paradigm change has occurred around the millennium owing to the recognition of distinct therapeutic advantages of covalent inhibition that include potentially full target occupancy and long-action, decoupling pharmacodynamics from pharmacokinetics. Therefore, the rational design of targeted covalent inhibitors (TCIs) has gained increased attention.

The binding of covalent inhibitors follows a two-step mechanism including the first non-covalent binding stage that is the molecular recognition of the non-covalent scaffold. Then the electrophilic functionality of the inhibitor, called warhead reacts with the targeted nucleophilic sidechain of the protein. Here I would focus both steps at two different levels. First I discuss virtual screening applications that allow the prioritization of compounds for experimental testing. After the evaluation of available covalent docking tools [1] we developed new methodologies that allow warhead independent docking of potential covalent inhibitors [2-4]. Next I turned to the accurate prediction of the binding free energy of covalent inhibitors by QM/MM calculations [5]. This approach allows the investigation of the molecular mechanism of action that together with the thermodynamic characterisation facilitate the design of potent covalent inhibitors [6,7].

Recording:

Video is available only for registered users.

References:

1. Andrea Scarpino, Gyorgy G Ferenczy and György M Keserű:Comparative Evaluation of Covalent Docking Tools Journal of Chemical Information and Modeling, 2018, 58 (7), 1441-1458.
2. Andrea Scarpino, László Petri, Damijan Knez, Tímea Imre, Péter Ábrányi-Balogh, György G. Ferenczy, Stanislav Gobec and György M. Keserű:WIDOCK: a reactive docking protocol for virtual screening of covalent inhibitors Journal of Computer-Aided Molecular Design, 2021, 35, 223–244.
3. Andrea Scarpino, György G. Ferenczy, György M. Keserű:Binding Mode Prediction and Virtual Screening Applications by Covalent Docking In: Protein-Ligand Interactions and Drug Design (ed. Flavio Ballante), Springer, 2021, pp. 73-88.
4. Moira Rachman, Andrea Scarpino, Dávid Bajusz, Gyula Pálfy, István Vida, András Perczel, Xavier Barril, György M Keserű:DUckCov: a Dynamic Undocking‐based Virtual Screening Protocol for Covalent Binders ChemMedChem, 2019, 14, 1011-1021. 
5. Levente M. Mihalovits, György G. Ferenczy, György M. Keserű:The role of quantum chemistry in covalent inhibitor design International Journal of Quantum Chemistry, 2021, 1-17
6. Levente M. Mihalovits, György G. Ferenczy, György M. Keserű:Affinity and Selectivity Assessment of Covalent Inhibitors by Free Energy Calculations Journal of Chemical Information and Modeling, 2020, 60 (12), 6579-6594.
7. Levente M. Mihalovits, György G. Ferenczy, György M. Keserű:Mechanistic and thermodynamic characterization of oxathiazolones as potent and selective covalent immunoproteasome inhibitors Computational and Structural Biotechnology Journal, 2021, 19, 4486-4496.

Dr Akos Tarcsay

ChemAxon Kft. Záhony str. 7., Budapest, Hungary, H-1031

Discovery of a novel drug is an optimizing challenge against an array of chemical and biological attributes to reach the desired efficacy and safety profile. The immense complexity of the human body combined and the astronomically large druggable chemical space hinders the selection of molecules with such a balanced profile. Therefore, the medicinal chemistry toolbox embraces all computational techniques with predictive power to focus the chemical space to the most promising candidates for synthesis and testing. The diversity includes data analysis tools, physics-based simulations, biological target structure driven or ligand structure based approaches [1-3]. While the size of the compound collections vary from a couple of close analogues up to billions of virtual compounds to process[4]. This presentation will highlight general concepts and techniques applied in computer aided drug design, focusing on data and ligand based computational chemistry approaches and showcase solutions developed by ChemAxon.

Recording:

References

[1] Gisbert Schneider, David E Clark, Angew Chem Int Ed Engl. 2019, 5;58(32):10792-10803.

[2] John G Cumming, Andrew M Davis, Sorel Muresan, Markus Haeberlein, Hongming Chen, Nat Rev Drug Discov,2013, 12(12):948-62.

[3] Yu-Chen Lo, Stefano E Rensi, Wen Torng, Russ B Altman, Drug Discov Today 2018, 23(8):1538-1546

[4] Torsten Hoffmanm, Marcus Gastreich, Drug Discov Today,  2019, 24(5):1148-1156.