2227936
Project Grant
Overview
Grant Description
Sbir Phase I: A physics-based machine learning platform for crystal structure prediction of small drug molecules - The broader impact of this Small Business Innovation Research (SBIR) Phase I project is to accelerate and reduce the cost of the early stages of small molecule pharmaceutical research.
The number of drugs a pharmaceutical company can bring to market is limited by the time, cost, and complexity involved in developing each drug. The research and development process typically takes around 10 years, and few drugs make it onto the market each year.
This technology may be especially impactful in improving the frequency at which small molecule drugs are developed for understudied diseases, which collectively impact over 30 million Americans. By reducing the cost and time to market for new pharmaceuticals, the project could advance the industry and bring life-changing therapeutics to underserved people who are suffering from illnesses where there are presently no drug options.
This project develops technologies to solve the crystal structure prediction (CSP) problem. The crystalline structure of small molecules and peptides determines many pharmacological characteristics including solubility, oral bioavailability, shelf-life stability, and toxicity. Experimental determination of the crystal structure is expensive and requires significant human labor to conduct, so a computational approach would reinvent the characterization of small molecule drugs.
The proposed technical innovation combines a novel energy prediction models based on quantum chemistry with a machine learning method for efficiently sampling the vast space of possible crystal structures. The resulting technology will help pharmaceutical companies de-risk their drug development process by allowing them to analyze crystal structures computationally before having to synthesize them in the lab.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. Subawards are not planned for this award.
The number of drugs a pharmaceutical company can bring to market is limited by the time, cost, and complexity involved in developing each drug. The research and development process typically takes around 10 years, and few drugs make it onto the market each year.
This technology may be especially impactful in improving the frequency at which small molecule drugs are developed for understudied diseases, which collectively impact over 30 million Americans. By reducing the cost and time to market for new pharmaceuticals, the project could advance the industry and bring life-changing therapeutics to underserved people who are suffering from illnesses where there are presently no drug options.
This project develops technologies to solve the crystal structure prediction (CSP) problem. The crystalline structure of small molecules and peptides determines many pharmacological characteristics including solubility, oral bioavailability, shelf-life stability, and toxicity. Experimental determination of the crystal structure is expensive and requires significant human labor to conduct, so a computational approach would reinvent the characterization of small molecule drugs.
The proposed technical innovation combines a novel energy prediction models based on quantum chemistry with a machine learning method for efficiently sampling the vast space of possible crystal structures. The resulting technology will help pharmaceutical companies de-risk their drug development process by allowing them to analyze crystal structures computationally before having to synthesize them in the lab.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. Subawards are not planned for this award.
Awardee
Funding Goals
THE GOAL OF THIS FUNDING OPPORTUNITY, "NSF SMALL BUSINESS INNOVATION RESEARCH (SBIR)/ SMALL BUSINESS TECHNOLOGY TRANSFER (STTR) PROGRAMS PHASE I", IS IDENTIFIED IN THE LINK: HTTPS://WWW.NSF.GOV/PUBLICATIONS/PUB_SUMM.JSP?ODS_KEY=NSF22551
Grant Program (CFDA)
Awarding / Funding Agency
Place of Performance
Atlanta,
Georgia
30306-3543
United States
Geographic Scope
Single Zip Code
Related Opportunity
22-551
Lavo Life Sciences was awarded
Project Grant 2227936
worth $274,990
from National Science Foundation in September 2023 with work to be completed primarily in Atlanta Georgia United States.
The grant
has a duration of 1 year and
was awarded through assistance program 47.084 NSF Technology, Innovation, and Partnerships.
SBIR Details
Research Type
SBIR Phase I
Title
SBIR Phase I:A physics-based machine learning platform for crystal structure prediction of small drug molecules
Abstract
The broader impact of this Small Business Innovation Research (SBIR) Phase I project is to accelerate and reduce the cost of the early stages of small molecule pharmaceutical research. The number of drugs a pharmaceutical company can bring to market is limited by the time, cost, and complexity involved in developing each drug. The research and development process typically takes around 10 years, and few drugs make it onto the market each year. This technology may be especially impactful in improving the frequency at which small molecule drugs are developed for understudied diseases, which collectively impact over 30 million Americans. By reducing the cost and time to market for new pharmaceuticals, the project could advance the industry and bring life-changing therapeutics to underserved people who are suffering from illnesses where there are presently no drug options._x000D_ _x000D_ This project develops technologies to solve the crystal structure prediction (CSP) problem. The crystalline structure of small molecules and peptides determines many pharmacological characteristics including solubility, oral bioavailability, shelf-life stability, and toxicity. Experimental determination of the crystal structure is expensive and requires significant human labor to conduct, so a computational approach would reinvent the characterization of small molecule drugs. The proposed technical innovation combines a novel energy prediction models based on quantum chemistry with a machine learning method for efficiently sampling the vast space of possible crystal structures. The resulting technology will help pharmaceutical companies de-risk their drug development process by allowing them to analyze crystal structures computationally before having to synthesize them in the lab._x000D_ _x000D_ This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
Topic Code
PT
Solicitation Number
NSF 22-551
Status
(Complete)
Last Modified 9/22/23
Period of Performance
9/15/23
Start Date
8/31/24
End Date
Funding Split
$275.0K
Federal Obligation
$0.0
Non-Federal Obligation
$275.0K
Total Obligated
Activity Timeline
Additional Detail
Award ID FAIN
2227936
SAI Number
None
Award ID URI
SAI EXEMPT
Awardee Classifications
Small Business
Awarding Office
491503 TRANSLATIONAL IMPACTS
Funding Office
491503 TRANSLATIONAL IMPACTS
Awardee UEI
FMD4QCQAWVY8
Awardee CAGE
None
Performance District
GA-05
Senators
Jon Ossoff
Raphael Warnock
Raphael Warnock
Budget Funding
| Federal Account | Budget Subfunction | Object Class | Total | Percentage |
|---|---|---|---|---|
| Research and Related Activities, National Science Foundation (049-0100) | General science and basic research | Grants, subsidies, and contributions (41.0) | $274,990 | 100% |
Modified: 9/22/23