Dean of the School of Science, Professor Chemistry and Chemical Biology
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.
Ph.D. in Chemistry at UC Santa Barbara (with an emphasis on Physical Organic and Computational Chemistry) 1987
- CC Wang, G Pilania, SA Boggs, S Kumar, C Breneman*, R Ramprasad* “Computational strategies for polymer dielectrics design” Polymer (2014) 55(4) 979–988.
- T Potta, Z Zhen, TSP Grandhi, MD Christensen, J Ramos, CM Breneman* “Discovery of antibiotics-derived polymers for gene delivery using combinatorial synthesis and cheminformatics modeling” Biomaterials (2014) 35(6), 1977-1988.
- Zaretzki, Jed; Bergeron, Charles; Huang, Tao-wei; Rydberg, Patrik; Swamidass, S. Joshua; Breneman, Curt M.* “RS-WebPredictor: a server for predicting CYP-mediated sites of metabolism on drug-like molecules” Bioinformatics (2013), 29(4), 497-498
- T Huang, J Zaretzki, C Bergeron, KP Bennett, CM Breneman* “DR-Predictor: Incorporating flexible docking with specialized electronic reactivity and machine learning techniques to predict CYP-mediated sites of metabolism” Journal of chemical information and modeling (2013) 53 (12), 3352-3366
- Curt Breneman*, L.C. Brinson, L.S. Schadler, B. Natarajan, M. Krein, K. Wu, L. Morkowchuk, Y. Li, H. Deng, H. Xu. "Stalking the Materials Genome: A Data-Driven Approach to the Virtual Design of Nanostructured Polymers”, Advanced Functional Materials (2013) 23 (46), 5746-5752.
- G. Pilania, CC Wang, K Wu, N Sukumar, C Breneman*, G Sotzing and R. Ramprasad* “New group IV chemical motifs for improved dielectric permittivity of polyethylene” Journal of chemical information and modeling (2013) 53 (4), 879-886.
- JM Shoulder, NS Alderman, CM Breneman, MC Nyman* “Polycyclic aromatic hydrocarbon reaction rates with peroxy-acid treatment: prediction of reactivity using local ionization potential” SAR and QSAR in Environmental Research (2013) 24 (8), 611-624.
- C Bergeron, G. Moore, J. Zaretzki, C.M. Breneman*, K.P Bennett* “A Fast Bundle Algorithm for Multiple Instance Learning” IEEE Transactions on Pattern Analysis & Machine Intelligence, (2012), 34(6), 1068-1079
- Sukumar, N.; Krein, Michael; Luo, Qiong; Breneman, Curt* “MQSPR modeling in materials informatics: a way to shorten design cycles?” Journal of Materials Science (2012), 47(21), 7703-7715.
- Zaretzki, Jed; Rydberg, Patrik; Bergeron, Charles; Bennett, Kristin P.; Olsen, Lars; Breneman, Curt M.* “RS-Predictor Models Augmented with SMARTCyp Reactivities: Robust Metabolic Regioselectivity Predictions for Nine CYP Isozymes” Journal of Chemical Information and Modeling (2012), 52(6), 1637-1659.