Exploring Path Complex Networks: Implications for Drug Development

Authors

Brian Lihari, Dartmouth CollegeFollow
Peter Chin, Dartmouth CollegeFollow
Sie Hendrata, Dartmouth CollegeFollow

Files

Student Class

2028

Student Affiliation

Thayer School First Year Research in Engineering

First Advisor

Peter Chin

First Advisor Department

Department of Engineering Sciences—Thayer School of Engineering

Description

This project explores an approach called Path Complex Networks part of a general technique called Graph Neural Network (GNN). In a recent paper by Professor Truong and Professor Chin, a PCN was developed that performed better than the Weisfeiler-Lehman Test. PCN prevents obscurity: PCN can distinguish between 2 molecules that might look similar that a typical GNN might not be able to distinguish. PCN can distinguish molecules that were previously imperceptible.

Publication Date

Spring 5-27-2025

Keywords

Graph Neural Networks, AI, PCN, GNN, Pharmaceuticals, Drug Development, Disease, Medicine

Disciplines

Nanomedicine | Other Chemicals and Drugs | Other Computer Engineering | Pharmaceutical Preparations | Pharmaceutics and Drug Design

Dartmouth Digital Commons Citation

Lihari, Brian; Chin, Peter; and Hendrata, Sie, "Exploring Path Complex Networks: Implications for Drug Development" (2025). Wetterhahn Science Symposium Posters 2025. 16.
https://digitalcommons.dartmouth.edu/wetterhahn_2025/16