The National Science Foundation Division of Chemistry has awarded a 3-year, $390,000 Project Grant to the University of Georgia Research Foundation to develop new machine learning tools that facilitate the identification of molecular structure from vacuum ultraviolet (VUV) spectroscopy data. The project, titled "Machine Learning Models for Interpreting Molecular Structure from Vacuum Ultraviolet Spectra", aims to create ML models capable of over 95% accuracy in predicting key details...
The National Science Foundation Division of Chemistry awarded a $650,000 Project Grant to the Massachusetts Institute of Technology from March 1, 2022 to February 28, 2027. The grant funds the development of novel domain-tailored machine learning tools for organic reaction development and discovery under the Mathematical and Physical Sciences program (CFDA 47.049). The grant supports research to increase the store of scientific knowledge and enhance understanding of major problems confronting...
Under a $293,308 Project Grant awarded by the National Science Foundation (NSF) Division of Chemistry through the Mathematical and Physical Sciences program (CFDA 47.049), Purdue University is conducting research to develop new chiral catalysts for enantioselective photoredox-catalyzed carbon-carbon bond-forming reactions. The project aims to design efficient photochemical reactions to synthesize specific enantiomers of chiral organic molecules, which have important applications in...
Professors Olaf Wiest and Paul Helquist at the University of Notre Dame are developing computational methods for the fast and accurate prediction of enantioselectivity in metal-catalyzed reactions under a $620,000 National Science Foundation project grant from the Mathematical and Physical Sciences program (CFDA 47.049). The project will continue work on a virtual screening tool that uses quantum-guided molecular mechanics to predict specific catalysts that will produce desired enantiomeric...
The National Institute of General Medical Sciences (NIGMS) awarded a 5-year, $357,056 Project Grant under the Biomedical Research and Research Training program (CFDA 93.859) to Wayne State University, located in Detroit, Michigan. The project aims to address limitations in high-throughput experimentation in organic synthesis by developing innovative materials and macromolecular analogs to expand the capabilities of automated liquid handling platforms. Key efforts include: Developing colloidal...
This $575,000 Project Grant award from the National Science Foundation's (NSF) Mathematical and Physical Sciences program (CFDA 47.049) supports a collaborative research project led by Professors Keary Engle of the Scripps Research Institute and Peng Liu of the University of Pittsburgh. The project focuses on developing new chemical reactions using earth-abundant nickel catalysts to efficiently synthesize valuable compounds, including medicinally relevant molecules, agrochemicals, and functional...
This $600,000 Project Grant awarded by the National Science Foundation's (NSF) Division of Chemistry under the Mathematical and Physical Sciences Federal Grant Program (CFDA 47.049) supports a collaboration between researchers at the University of Wisconsin-Madison and GlaxoSmithKline (GSK) to develop new oxidative reactions and synthetic methods for DNA-encoded chemical libraries (DECLs). The key objectives are: 1) developing oxidative cyclization reactions that can be conducted on DNA, 2)...
This National Science Foundation Project Grant of $418,678 awarded on August 15, 2022 will support the development of computational tools to model complex chemical reactions through dimensionality reduction and machine learning techniques. Under the federal Mathematical and Physical Sciences program (CFDA 47.049), Dmitrij Rappoport of the University of California, Irvine will create low-dimensional representations of potential energy surfaces using nonlinear dimensionality reduction and...
This three-year, $480,000 Project Grant from the National Science Foundation's Division of Chemistry and Mathematical and Physical Sciences program will support the development of sustainable catalysts and dehydrocoupling chemistry at Arizona State University. Principal Investigator Ryan J. Trovitch will study trends in earth-abundant metal catalysts to synthesize value-added chemicals and polymers more efficiently. Initial work will target precursors for semiconductor manufacturing. The project...
This three-year, $544,758 project grant from the National Science Foundation Division of Chemistry supports the development of new optical sensing methodologies to accelerate and broaden chiral compound analysis at Georgetown University. The university's Chemistry Department will introduce widely applicable small-molecule probes, novel molecular recognition strategies, artificial intelligence tools, and automated high-throughput experimentation technology. This will help overcome current...
VIRTUAL APPROACHES TO NEW CHEMISTRIES - PROJECT SUMMARY/ABSTRACT TWO NEW VIRTUAL CHEMISTRY TECHNOLOGIES WILL BE ADDED TO THE NCATS ASPIRE PROJECT AS SEPARATE MODULES. THE FIRST MODULE WILL ENABLE NEW CHEMISTRIES TO BE MODELLED AND SELECTED FROM CUTTING EDGE (DEEP) MACHINE LEARNING TECHNOLOGY USING THE LATEST STRUCTURE/ACTIVITY DATA TAKEN DIRECTLY FROM INSTRUMENTS. THE SECOND MODULE WILL BE A NOVEL INFORMATICS SYSTEM FOR CAPTURING CHEMISTRY-RICH DATA IN A SEMANTIC TEMPLATE AS MACHINE-READABLE REACTIONS WHICH WILL INCREASE THE UTILITY OF CHEMICAL REACTIONS IN ELECTRONIC LAB NOTEBOOKS AND ALLOW MORE PRECISE INTERROGATION AND AUTOMATION OF REACTION ANALYSES (AND THEIR CORRESPONDING REACTION PRODUCTS). THE DEEP LEARNING TECHNOLOGY IN MODULE 1 IS BASED ON OUR NEW CHEMICALLY RICH VECTOR (CRV) METHODOLOGY, WHICH IS ABLE TO COMPRESS INFORMATION ABOUT CHEMICAL STRUCTURES INTO A VECTOR OF 64 NUMBERS WITH AN EFFICIENCY THAT ALLOWS THE ENCODING PROCESS TO BE REVERSED: NOT ONLY CAN A CRV BE CONVERTED BACK INTO ITS ORIGINAL STRUCTURE WITH HIGH SUCCESS (>90% EXACT MATCH), BUT A MODIFIED CRV CAN BE CONVERTED INTO A STRUCTURE THAT IS REPRESENTATIVE OF THAT POINT IN CHEMICAL SPACE. CRVS MAKE EXCELLENT DESCRIPTORS FOR SAR/QSAR ITERATION BECAUSE THEY CONTAIN MUCH MORE CHEMICAL INFORMATION IN A SMALL SPACE, ALLOWING THE AUTOMATION OF STRUCTURE-ACTIVITY MODELS TO BE MORE STREAMLINED, RELATIVE TO CONVENTIONAL DESCRIPTORS. THE RESULTING MODELS WILL EXPLORE THE MULTI-DIMENSIONAL SPACE VIA AN INTERACTIVE VISUAL INTERFACE (HUMAN-DIRECTED) OR A BACK-END ALGORITHM TO CONSTANTLY SEARCH FOR NEW AND BETTER STRUCTURES (MACHINE-DIRECTED). BOTH INTERACTIVE AND AUTOMATED PROCESSES WILL BE CONNECTED BACK INTO THE ASPIRE AUTOMATION CYCLE SO THAT THEY CAN BE SYNTHESIZED AND MEASURED (HYPOTHESIS EVALUATION AND ITERATIVE OPTIMIZATION). THE SECOND MODULE, MACHINE-READABLE REACTIONS, DRAWS FROM OUR EXTENSIVE EXPERIENCE DEVELOPING THE BIOHARMONY ANNOTATOR (FORMERLY: BIOASSAY EXPRESS) WHICH USES NATURAL LANGUAGE MODELS TO ASSIGN SEMANTIC ONTOLOGY TERMS TO BIOLOGICAL ASSAY PROTOCOLS, TURNING THEM FROM UNSTRUCTURED TEXT INTO MACHINE-READABLE DATA. EXTRACTING THE FULL CONTENT OF REACTIONS FROM PROTOCOLS AND CHEMICAL STRUCTURE DIAGRAMS IS REMARKABLY DIFFICULT GIVEN THE UNSTRUCTURED NATURE OF TEXT, ABBREVIATIONS, SHORTCUTS AND ASSUMPTIONS THAT GO INTO DIAGRAMS. IT IS FURTHER COMPLICATED BY THE NEED TO CONNECT THE MATERIALS IN THE SCHEME WITH THE REACTION TEXT DESCRIPTION (E.G. REAGENTS, SOLVENTS, THE SEQUENCES INVOLVED IN THE RECIPE, REACTION WORKUP, AND PRODUCT CHARACTERIZATION). AS AN ALTERNATIVE, WE WILL MODULARIZE THE CDD STOICHIOMETRIC SKETCHER, WHICH WILL ALLOW US TO EXTRACT THIS DATA. WE WILL WORK WITH NCATS TO IDENTIFY IMPORTANT FIELDS TO CAPTURE, CREATING A MACHINE READABLE CHEMICAL REACTION TEMPLATE.