ATTMOS INC — Department of Health and Human Services SBIR Phase I: 400
ATTMOS INC — SBIR Phase I award from Department of Health and Human Services.
Phase I SBIR feasibility signal
- Phase I awards fund proof-of-concept work. For capture teams, they mark early interest from Department of Health and Human Services in a technical approach.
- Watch for Phase II follow-ons from the same firm/topic family — that conversion path is where budgets and transition pressure rise.
- Obligated amount $275,431. Cross-check similar awards in the same agency and technology tags for going-rate context.
- Topic code 400 links this award to a solicitation family — search the same topic stem for incumbents and recompete timing.
- Amount
- $275,431
- Agency
- Department of Health and Human Services · National Institutes of Health
- Program / Phase
- SBIR · Phase I
- Topic
- 400
- Solicitation
- PA22-176
- NAICS
- —
- Place of performance
- MI
- Period
- 2023-09-30 → 2024-03-30
Description
Project AbstractOur overarching goal is to provide reliable and efficient tools that can be used in structure based drug discovery (SBDD). One crucial component of SBDD is to predict the structure of a drug molecule that binds to a protein involved in a certain disease. This is usually achieved using computer tools and the process consists of two steps, namely hit identification and lead optimization. The latter step requires high accuracy and is presently achieved by computing relative binding free energies (RBFE) using alchemical methods and molecular mechanics (MM) forcefields. Unfortunately, due to deficiencies in MM forcefields, predicted drug candidates using the SBDD process are sometimes unreliable, which is only realized at the later stages of the drug discovery process involving experimental studies or even clinical trials. To address this issue, we will create a novel, flexible and user-friendly computational infrastructure named Automated Force Field Developer and Optimizer (AFFDO) that will allow scientists to quickly generate high-quality training datasets through high-throughput ab initio calculations and transform them into fast and accurate models which can then be used for RBFE calculations. We will engineer a commercial quality code and deploy it on an existing web-based, user-friendly, drug development platform that is widely popular among the industrial community (OpenEye’s Orion platform).