This $280,000 Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program supports the development of comprehensive generative AI methodologies and computational tools to expedite drug discovery, enhance cost efficiency, and improve success rates. The project aims to create a holistic generative AI framework capable of generating high-quality drug candidates with multiple desired properties, with the potential to transform...
This Project Grant award of $500,000 from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program will support research at the University of Michigan to develop extrapolation-aware conditional generative modeling and experimental design methods. The goal is to address the challenge of generating novel molecules with desired properties, which is often hindered by data scarcity and the inability to accurately model rare, exceptional properties that...
This Project Grant award of $305,000.00 from the National Science Foundation's (NSF) Technology, Innovation, and Partnerships (CFDA 47.084) program aims to revolutionize drug discovery by accelerating molecular dynamics simulations through advanced AI techniques. The project, awarded to Giwotech Inc. in Dorchester, MA, seeks to develop a robust neural network model capable of simulating protein systems up to 500 times faster than current GPU-based classical molecular dynamic simulators while...
This Project Grant award from the National Science Foundation's (NSF) Mathematical and Physical Sciences program (CFDA 47.049) provides $296,023 to the Regents of the University of Minnesota to establish the mathematical foundations of two models that underpin generative artificial intelligence (AI) methodologies in scientific contexts: score-based generative models and transformer-based foundation models. The primary goals of this 5-year project are to: 1) study the role of fine data structures...
This $400,000 Project Grant awarded by the National Science Foundation (NSF) under the Computer and Information Science and Engineering (CFDA 47.070) program supports the development of a new framework and tools for advancing data-centric artificial intelligence (AI) through generative approaches to feature space reconstruction. The project aims to transform the traditional way of constructing feature spaces by using deep generative learning instead of manual or classical discrete search...
This $244,960 federal Project Grant award from the National Science Foundation's (NSF) Mathematical and Physical Sciences program (CFDA 47.049) will fund research at the University of Minnesota to develop new computational methods for discovering novel block polymer materials. The project aims to use generative artificial intelligence techniques to propose new block polymer structures, and then apply machine learning to identify the appropriate polymer chemistries and processing conditions to...
This Project Grant award of $233,332 from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program will support collaborative research to accelerate protein engineering using evolution-guided generative AI and an automated biofoundry. The research aims to combine cutting-edge AI methods with advanced laboratory automation to greatly speed up the discovery of new proteins with enhanced properties, offering significant potential impact across...
This $499,742 Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program, with an award date of April 15, 2025 and an ultimate completion date of March 31, 2027, aims to develop a novel AI framework for designing new non-addictive pain relief medications to address the opioid crisis. The primary objectives are to create a generative AI system that integrates scientific knowledge and grammar-based molecular encodings to...
This Project Grant award of $255,237 from the National Science Foundation's (NSF) Mathematical and Physical Sciences program (CFDA 47.049) supports collaborative research by the Regents of the University of Minnesota to develop acceleration and preconditioning methods to improve the training efficiency of deep learning models. The research aims to leverage insights from numerical methods and linear algebra to speed up the computationally intensive and resource-demanding process of training large...
This Project Grant award from the National Institute of General Medical Sciences (NIGMS) Biomedical Research and Research Training program (CFDA 93.859) provides $435,620 to The Regents of the University of California, San Francisco (UCSF) to develop and test algorithms for iteratively docking subsets of ultra-large small molecule libraries against molecular targets. The goal is to create a more efficient prioritization approach for virtual screening of trillion-scale make-on-demand small...
This Project Grant award from the National Science Foundation (CFDA 47.070 - Computer and Information Science and Engineering) to the Regents of the University of Minnesota will provide $220,000 to develop advanced generative AI methodologies and computational tools to expedite drug discovery and enhance cost efficiency.
The key objectives are to create a holistic generative AI framework capable of generating high-quality drug candidates with multiple desired properties, as well as a direct multi-property optimization framework to improve the quality and adaptability of generated molecules. The research activities will focus on developing a conditional diffusion model for 3D molecule generation, creating the direct multi-property optimization framework, and conducting rigorous evaluations and validations. This project aims to significantly reduce the time, cost, and resources required for drug discovery while increasing its success rates, ultimately benefiting public health. No sub-awards are planned under this award, which has an ultimate completion date of July 31, 2027.