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GERMANTOWN, MD, United States

Arora P.,Iowa State University | Slipchenko L.V.,Purdue University | Webb S.P.,Verachem, Llc | Defusco A.,Iowa State University | Gordon M.S.,Iowa State University
Journal of Physical Chemistry A | Year: 2010

The simplest variational method for treating electronic excited states, configuration interaction with single excitations (CIS), has been interfaced with the effective fragment potential (EFP) method to provide an effective and computationally efficient approach for studying the qualitative effects of solvents on the electronic spectra of molecules. Three different approaches for interfacing a non-self-consistent field (SCF) excited-state quantum mechanics (QM) method and the EFP method are discussed. The most sophisticated and complex approach (termed fully self consistent) calculates the excited-state electron density with fully self-consistent accounting for the polarization (induction) energy of effective fragments. The simplest approach (method 1) includes a strategy that indirectly adds the EFP perturbation to the CIS wave function and energy via modified Hartree-Fock molecular orbitals, so that there is no direct EFP interaction with the excited-state density. An intermediate approach (method 2) accomplishes the latter in a noniterative perturbative manner. Theoretical descriptions of the three approaches are presented, and test results of solvent-induced shifts using methods 1 and 2 are compared with fully ab initio values. These comparisons illustrate that, at least for the test cases examined here, modification of the ground-state Hartree-Fock orbitals is the largest and most important factor in the calculated solvent-induced shifts. Method 1 is then employed to study the aqueous solvation of coumarin 151 and compared with experimental measurements. © 2010 American Chemical Society. Source


Grant
Agency: Department of Health and Human Services | Branch: | Program: SBIR | Phase: Phase I | Award Amount: 140.80K | Year: 2010

DESCRIPTION (provided by applicant): Many drugs are small molecules that act by binding to a specific protein and thus blocking or altering its actions. For example, the HIV protease inhibitors are important AIDS treatments that work by binding in the active site of the protease enzyme and preventing it from helping to make new viruses. When scientists identify a protein, like HIV protease, as being important in a disease process, a next step often is to determine its three-dimensional structure in great detail. This structure then provides valuable guidance to chemists trying to design a small molecule that will bind the protein tightly. However, even when they know the structure of the protein, there is still a lot of trial and error in designing a drug. Many researchers have worked on computer programs to help predict whether a given molecule will bind a given protein, but without much success. Now, new software that VeraChem has been developing over the last few years is giving very good results for this problem. However, the software takes a long time to run and would be far more useful if it were much faster. For example, if chemists had an idea for a new compound to try, they could get the answer in a minutes instead of a few days. They could use the method to quickly and cheaply test thousands of compounds in chemical catalogs. And they could check whether a compound that works against their protein would keep working against mutant forms of the protein and thereby avoid drug-resistance. Thus, a fast version of VM2 would be very useful and would be a valuable commercial product. Speeding up VeraChem's method, VM2, is not as simple as running it on a faster computer, because individual computers have not been getting much faster in recent years. What is changing, though, is that computers are being made with more and more processors. The goal of this project is to speed up VM2 enormously by spreading its computational work across large numbers of separate processors in a single computer, in a cluster of computers, and even in a video card. This is not a simple task, but researchers have been able to speed up related molecular calculations in this way, and we are confident the same can be done for VM2. PUBLIC HEALTH RELEVANCE: We want to let scientists design new medicines more quickly with a computer program. The problem is that the program takes too long to do its calculations. This project is to speed up the calculations by changing the program so that it can make a large number of computer processors to work together to calculate the answers in a short time.


Grant
Agency: Department of Health and Human Services | Branch: | Program: SBIR | Phase: Phase I | Award Amount: 147.88K | Year: 2014

Project Summary Host molecules, such as cyclodextrins and cucurbiturils, can 'capture' smaller molecules and affect their physical and chemical behavior. The stronger the host molecule holds onto, i.e. binds, its smaller 'guest' the larger the effect can be. Host molecules themselves can also be chemically altered (i.e. derivatized), which can change how strongly they bind guest molecules, as well as their own physical properties. Scientists are discovering many human health-related applications for host-guest technology, including improvement of the properties of drugs to make them more effective and safer, potential scavengers for chemical warfare agent removal, and clean-up of environmental chemical pollutants. The amount of basic research as well as applied/industrial RandD in this area is expanding rapidly. Given a particular 'guest' molecule (e.g. drug candidate, chemical pollutant) key pieces of information RandD scientists require is the host-guest binding affinity and the association/dissociation rates.


Grant
Agency: Department of Health and Human Services | Branch: | Program: SBIR | Phase: Phase I | Award Amount: 149.05K | Year: 2005

DESCRIPTION (provided by applicant): Discovering a compound that will bind a protein therapeutic target with high affinity, while retaining favorable pharmacological properties, can be a time-consuming and expensive bottleneck in the drug-discovery process. Computational methods can speed this ligand discovery step, but are not yet reliable enough to circumvent extensive experimental testing, even in the favorable instance where the 3D structure of the protein is known. The central goal of this SBIR project is to develop and commercialize a new computational method for assessing the affinities of drug candidates for a protein target of known 3D structure, accounting efficiently for changes in translational, rotational and conformational entropy upon binding. This method will speed discovery of new medications to improve human health, and will generate substantial sales in the pharmaceutical and biotechnology industries, as well as from government and academic labs. During Phase I, we aim to demonstrate successful initial implementation of the method on a single computer and in parallel on a commodity compute cluster, and to show that it yields converged free energies in nontrivial test cases. We also aim to show that properly computed free energies can yield significantly different ligand rankings, relative to more elementary models.


Grant
Agency: Department of Health and Human Services | Branch: | Program: SBIR | Phase: Phase II | Award Amount: 1.98M | Year: 2006

DESCRIPTION (provided by applicant): Discovering a compound that will bind a protein therapeutic target with high affinity, while retaining favorable pharmacological properties, can be a time-consuming and expensive bottleneck in the drug-discovery process. Computational methods can speed this ligand discovery step, but are not yet reliable enough to circumvent extensive experimental testing, even in the favorable instance where the 3D structure of the protein is known. The central goal of this SBIR project is to develop and commercialize a new computational method for assessing the affinities of drug candidates for a protein target of known 3D structure, accounting efficiently for changes in translational, rotational and conformational entropy upon binding. This method will speed discovery of new medications to improve human health, and will generate substantial sales in the pharmaceutical and biotechnology industries, as well as from government and academic labs. During Phase I, we aim to demonstrate successful initial implementation of the method on a single computer and in parallel on a commodity compute cluster, and to show that it yields converged free energies in nontrivial test cases. We also aim to show that properly computed free energies can yield significantly different ligand rankings, relative to more elementary models.

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