Machine learning phenotypic DE novo drug design
Active Project Grant Awarded Last action
- Award ID
- R43MH134716
- Recipient
-
Psychogenics Inc. UEI YXMNA8N7LFG4
- Awarding agency
-
National Institute of Mental Health
National Institutes of Health, Department of Health and Human Services
- Obligated
- $1,499,838
Award facts
- Assistance listing
- 93.242 Mental Health Research Grants
- Completion date 80% complete
- Originally
- Place of performance
- New Jersey, USA
Award details come from USAspending.gov.
Description
- Abstract
- The high rate of failure in CNS drug discovery, in particular of the first-in-class therapeutics with new modes of action, highlights a clear unmet need to improve the success rate in drug discovery for psychiatric disorders. One well-known issue is the poor ability of current bioassays and animal models to predict the efficacy and side- effects of compounds. Another important issue is the lack of clear targets for CNS disorders, which are complex and require polypharmacology. Phenotypic screening platforms are well-suited for drug discovery of compounds in a target-agnostic manner, allowing for the discovery and development of poly pharmacological agents. Suitable proven in vivo phenotypic screens, however, are rare with the exception of psychogenics smartcube® platform, which has been used to screened ~8000 compounds and reference drugs. Compound availability for phenotypic screening, however, restrict discovery to known chemical spaces. Novel machine learning methods are now available to design novel drugs that can be used to poke unexplored chemical spaces. The combination of a machine learning model capturing structure-to-phenotype relationships and a model that can generate novel drug-like compounds promises to deliver a truly novel platform. Our aims therefore are 1) to generate a structure- to-phenotype machine learning model ("phencheml") using our collection of more than 8000 compounds and drugs screened in psychogenics' smartcube® phenotypic in vivo platform, and 2) to combine such model with collaboration pharma DE novo drug design generative machine learning model megasyn®, and generate novel CNS drug-like compounds for testing in vivo. The success of this phase i sbir project will result in phencheml, a novel phenotypic machine learning-based drug discovery platform that can generate novel chemotypes and predict their therapeutic value. If our phase i project is successful, we will extend IT in a phase II application through the design and synthesis of novel molecules for test in smartcube® and validation in second tier assays focusing on psychiatric disorders (depression, anxiety, psychosis, and bipolar disorder). We will also explore the use of the platform for generation of novel compounds with potential therapeutic effects in model systems of psychiatric, neurodevelopmental, and neurodegenerative disease (e.g., rett, ASD, HD, PD, etc). If successful, this platform will be an innovative and unique drug design method, offered by as fee-for-service or used in drug development by PGI and its partners.
Transactions
| Action | Obligated | Date |
|---|---|---|
| Amendment | $0 | |
| Amendment | $0 | |
| Amendment | $0 | |
| Amendment | $0 | |
| Amendment | $499,946 | |
| Amendment | $499,946 | |
| Original award | $499,946 |
Subawards
| Subrecipient | Amount | Awarded |
|---|---|---|
| Collaborations Pharmaceuticals, Inc. | $164,949 |
Quick answers
Who received award R43MH134716?
R43MH134716 was awarded to Psychogenics Inc. by National Institute of Mental Health (National Institutes of Health) on August 15, 2023.
How much is award R43MH134716 worth?
$1,499,838 has been obligated on R43MH134716.
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