Efficient and Accurate Force Fields for Computer-Aided-Drug Design Computer-aided drug design is hampered by the limited accuracy in the force fields that describe the interactions between drug molecules, their targets, and shared environment. An automated protocol, adaptive force matching (AFM), is proposed for creating customized force fields that enable more efficient and accurate computational studies of the structure and function of drug candidates. Putting AFM in place will require the adaption of highly efficient techniques for sampling of configurations and quantum mechanical calculations of large systems. The approach will be developed and evaluated in a series of aims with increasing complexity. In the first two aims, force fields are generated for prediction of hydration free energies of small drug-like molecules and of the binding affinities for guest-host pairs. The performance of the method will be tested on examples from the recent SAMPL4 competition. In the last aim, force fields are created for simulations that allow one to identify nisin derivatives with improved solubility and stability at physiological pH as required for the design of nisin-based novel antibiotics. This class of drug is generally referred to as lantibiotics, and it carries the promise to address the increasingly severe problem of multi-drug resistant bacterial infection.

Public Health Relevance

By developing a new method for creating atomistic models for guest-host interactions, the successful completion of this project will increase the accuracy of computational prediction of protein-ligand binding affinities; therefore increase the reliability of computer aided drug design. This in turn will reduce the cost for drug discovery and have a great impact on public health.

Agency
National Institute of Health (NIH)
Institute
National Institute of General Medical Sciences (NIGMS)
Type
Research Project (R01)
Project #
1R01GM120578-01A1
Application #
9260418
Study Section
Macromolecular Structure and Function D Study Section (MSFD)
Program Officer
Lyster, Peter
Project Start
2017-01-01
Project End
2021-12-31
Budget Start
2017-01-01
Budget End
2017-12-31
Support Year
1
Fiscal Year
2017
Total Cost
Indirect Cost
Name
University of Arkansas at Fayetteville
Department
Chemistry
Type
Schools of Arts and Sciences
DUNS #
191429745
City
Fayetteville
State
AR
Country
United States
Zip Code
72701
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