2017/12/12 by Farshad Harirchi, Harirchi, Farshad, Omar A. Khalil +9
Chemistry · Computer Science · Materials Science · #Computational Drug Discovery Methods #Dynamical Systems (math.DS) #FOS: Computer and information sciences #FOS: Mathematics #Free Radicals and Antioxidants #Machine Learning (cs.LG) #Machine Learning in Materials Science #Optimization and Control (math.OC)
paper · pdf · doi:10.48550/arxiv.1712.04493
openalex publication_date 2017/12/12 · openalex created_date 2017/12/22 · openalex updated_date 2026/07/28
In this paper, we propose an optimization-based sparse learning approach to identify the set of most influential reactions in a chemical reaction network. This reduced set of reactions is then employed to construct a reduced chemical reaction mechanism, which is relevant to chemical interaction network modeling. The problem of identifying influential reactions is first formulated as a mixed-integer quadratic program, and then a relaxation method is leveraged to reduce the computational complexity of our approach. Qualitative and quantitative validation of the sparse encoding approach demonstrates that the model captures important network structural properties with moderate computational load.