Machine learning–based regression analysis of cyclopentane and cyclohexane molecular graphs
Muhammad Irfan, Nabeela Bashir, M. Umair Shahzad, Kamal M. Othman, Abdulfattah Noorwalli, AbdulGuddoos S. A. Gaid, Esam Y. O. Zafar · 2026
Read Machine learning–based regression analysis of cyclopentane and cyclohexane molecular graphs by Muhammad Irfan, Nabeela Bashir, M. Umair Shahzad, Kamal M. Othman, Abdulfattah Noorwalli, AbdulGuddoos S. A. Gaid, Esam Y. O. Zafar (2026) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/PMC13534739.txt.
Description
Muhammad Irfan and colleagues apply in 2026 machine learning to molecular graphs of two ring compounds used in industry. Number summaries drawn from a graph can predict material traits, yet a fair comparison for these rings was missing. The team computed several indices based on atom links and nearby links for both structures.
Those indices fed models that link molecular shape to measured physical traits. Learning-based fits then judged how strongly each summary tracks real property data across both rings. Readers gain a fast way to estimate traits and a base for models of larger polymer networks in future work.
Catalog record
| CREATOR | Muhammad Irfan, Nabeela Bashir, M. Umair Shahzad, Kamal M. Othman, Abdulfattah Noorwalli, AbdulGuddoos S. A. Gaid, Esam Y. O. Zafar |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 205218 |
| LICENSE | https://creativecommons.org/licenses/by/4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |