Chemical reaction networks provide a comprehensive framework for understanding complex reaction systems, in which reaction path exploration is a critical component. In this study, molecular structures are represented as bond-electron matrices, and reaction candidates are systematically enumerated through matrix transformations. Starting from more than 1,000 reactant molecules, diverse reaction pathways were generated and validated using DFT calculations, resulting in OrgReact, a dataset comprising 9,649 reactions. The dataset includes reactant, product, and transition-state structures, together with associated energetic information, and is intended to support data-driven studies of organic reaction pathways and machine learning models for molecular energies and forces.
Automated explorations of chemical reaction networks (CRNs) guided by quantum chemistry (QC) calculations are undergoing rapid development that enables numerous applications. However, CRNs are accessible primarily through specialized quantum chemistry software without interface for cheminformatic exploitation of the ge...
E. Petrus, Diego Garay-Ruiz, Thomas Weymuth et al.· Journal of Chemical Informat...· 0 citations
Chemical reaction systems hold great promise for mimicking the fundamental processes of life and as the basis for new, sophisticated chemical technologies. These systems are conventionally viewed from a molecular perspective, in which their structure and behavior are controlled through molecular design. Achieving the...
The proposed framework demonstrates that combining language-based knowledge extraction with structure-informed retrosynthetic reasoning enables scalable construction of CCRNs and supports the systematic exploration of candidate circular chemical pathways for subsequent economic and environmental evaluation.
Sunghoon Kim, Avan Kumar, H. Kodamana et al.· ACS Sustainable Chemistry &a...· 0 citations
UniTS, a unified framework for automated transition state generation that combines a diverse transition state dataset with equivariant diffusion learning to accelerate mechanistic exploration, is developed.
Li-Cheng Xu, Jun-Yi An, Wei-Qi Liu et al.· Nature Communications· 0 citations
Understanding the chemical degradation of active pharmaceutical ingredients is critical for formulation development and regulatory compliance, yet computational tools either rely on predefined reaction rules that limit discovery of unanticipated pathways or cannot distinguish energetically accessible products from form...
Julius Seumer, M. Rasmussen, Oriana Brea et al.· Angewandte Chemie· 1 citation
This work introduces a reaction center-aware negative sampling strategy (RCNS) and a hierarchical embedding approach combining molecule, reaction and hypergraph level features, establishing ChemHGNN as a superior alternative for reaction virtual screening and discovery.
Xiaobao Huang, Yi-Hong Ma, Anjali Gurajapu et al.· Proceedings of the 32nd ACM...· 1 citation
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