Areas of Expertise (8)
Machine Learning Methods
Physical Organic and Computational Chemistry
Transferable Atom Equivalents
Molecular Property Descriptors
Protein Chromatography Modeling
Curt Breneman was born in Santa Monica, California in 1956, and went on to earn a B.S. in Chemistry at UCLA in 1980 followed by a Ph.D. in Chemistry at UC Santa Barbara (with an emphasis on Physical Organic and Computational Chemistry) in 1987. Following two years of post-doctoral research at Yale University, Dr. Breneman joined the faculty of the Department of Chemistry at Rensselaer Polytechnic Institute (RPI) and began a program in molecular recognition and computational chemistry based on his concept of "Transferable Atom Equivalents", or TAEs, as building blocks for describing the electronic and reactive character of molecules. Dr. Breneman currently holds the rank of Full Professor in the RPI Department of Chemistry and Chemical Biology, and is the Director of the NIH RECCR Center. He later served as Head of the Department of Chemistry & Chemical Biology and now as Dean of the School of Science. The Breneman research group primarily specializes in the development of new molecular property descriptors and machine learning methods that can be applied to a diverse set of physical and biochemical problems. Of paramount interest are methods that can increase the information content of molecular descriptors, and machine learning techniques that can exploit this data for the creation of fully validated, predictive property models. Current application areas include pharmaceutical ADME prediction, virtual high-throughput screening of drug candidates, protein chromatography modeling (HIC and ion-exchange), as well as polymer property prediction.
UC Santa Barbara: Ph.D., Chemistry 1987
UCLA: B.S., Chemistry 1980
Media Appearances (4)
Research, businesses affected by helium shortage
WNYT News Channel 13 online
It's only going to get pricier, or so says Professor Curt Breneman, Dean of the School of Science at RPI. He said prices could double, triple or even quadruple because of the shortage.
Remembering RPI's George Low On Apollo 11 Anniversary
Thousands of people worked to get the astronauts to the moon and back — including one with strong ties to Rensselaer Polytechnic Institute. A 1948 RPI graduate in Aeronautical Engineering, George Low had a voice in NASA from the very beginning, helping to plan the organization in 1958. He was named the organization’s first Chief of Manned Space Flight, and RPI Dean of Science Curt Breneman says that ultimately gave Low direct involvement in Projects Mercury, Gemini, and, of course, Apollo.
Web Extra: RPI Dean of Science talks astronauts past and future
News10 ABC online
In this web extra, NEWS10’s Cassie Hudson speaks with Rensselaer Polytechnic Institute Dean of Science Curt Breneman about the legacy of RPI astronauts and what he sees for the future.
Apollo 11's trip became real under RPI engineer's guidance
Times Union print
Curt Breneman, RPI’s dean of sciences, recalled being a 13-year-old boy entranced by Apollo 11’s adventure, space flight and science. “This was totally inspiring for a 13-year-old. Watching the moon landing and thinking about what that represented; how audacious it had been to propose doing that. In fact, George Low was one of the individuals who provided that information to the Kennedy administration that we really could do this,” Breneman said.
CC Wang, G Pilania, SA Boggs, S Kumar, C Breneman, R Ramprasad
2014 The present contribution provides a perspective on the degree to which modern computational methods can be harnessed to guide the design of polymeric dielectrics. A variety of methods, including quantum mechanical ab initio methods, classical force-field based molecular dynamics simulations, and data-driven paradigms, such as quantitative structure–property relationship and machine learning schemes, are discussed. Strategies to explore, search and screen chemical and configurational spaces extensively are also proposed. Some examples of computation-guided synthesis and understanding of real polymer dielectrics are also provided, highlighting the anticipated increasing role of such computational methods in the future design of polymer dielectrics.
Thrimoorthy Potta, Zhuo Zhen, Taraka Sai Pavan Grandhi, Matthew D Christensen, James Ramos, Curt M Breneman, Kaushal Rege
2014 We describe the combinatorial synthesis and cheminformatics modeling of aminoglycoside antibiotics-derived polymers for transgene delivery and expression. Fifty-six polymers were synthesized by polymerizing aminoglycosides with diglycidyl ether cross-linkers. Parallel screening resulted in identification of several lead polymers that resulted in high transgene expression levels in cells. The role of polymer physicochemical properties in determining efficacy of transgene expression was investigated using Quantitative Structure–Activity Relationship (QSAR) cheminformatics models based on Support Vector Regression (SVR) and ‘building block’ polymer structures. The QSAR model exhibited high predictive ability, and investigation of descriptors in the model, using molecular visualization and correlation plots, indicated that physicochemical attributes related to both, aminoglycosides and diglycidyl ethers facilitated transgene expression. This work synergistically combines combinatorial synthesis and parallel screening with cheminformatics-based QSAR models for discovery and physicochemical elucidation of effective antibiotics-derived polymers for transgene delivery in medicine and biotechnology.
Curt M Breneman, L Catherine Brinson, Linda S Schadler, Bharath Natarajan, Michael Krein, Ke Wu, Lisa Morkowchuk, Yang Li, Hua Deng, Hongyi Xu
2013 Accelerated insertion of nanocomposites into advanced applications is predicated on the ability to perform a priori property predictions on the resulting materials. In this paper, a paradigm for the virtual design of spherical nanoparticle‐filled polymers is demonstrated. A key component of this “Materials Genomics” approach is the development and use of Materials Quantitative Structure‐Property Relationship (MQSPR) models trained on atomic‐level features of nanofiller and polymer constituents and used to predict the polar and dispersive components of their surface energies. Surface energy differences are then correlated with the nanofiller dispersion morphology and filler/matrix interface properties and integrated into a numerical analysis approach that allows the prediction of thermomechanical properties of the spherical nanofilled polymer composites. Systematic experimental studies of silica nanoparticles modified with three different surface chemistries in polystyrene (PS), poly(methyl methacrylate) (PMMA), poly(ethyl methacrylate) (PEMA) and poly(2‐vinyl pyridine) (P2VP) are used to validate the models. While demonstrated here as effective for the prediction of meso‐scale morphologies and macro‐scale properties under quasi‐equilibrium processing conditions, the protocol has far ranging implications for Virtual Design.