Graduation Semester and Year
Summer 2026
Language
English
Document Type
Dissertation
Degree Name
Doctor of Philosophy in Computer Science
Department
Computer Science and Engineering
First Advisor
Junzhou Huang
Second Advisor
Dajiang Zhu
Third Advisor
Sihong He
Fourth Advisor
Miao Yin
Abstract
Predicting biomolecular interactions, from immune recognition to drug–target binding, is a central problem in the life sciences and computational drug discovery. Deep learning has advanced this area, yet three challenges persist: the topology of large, highly imbalanced interaction networks; structural noise in computationally predicted protein models; and the integration of multimodal information such as functional text and taxonomic annotations. This dissertation develops a coherent set of models spanning immune complex prediction and small-molecule drug design: graph learning that addresses network topology and severe class imbalance; a noise-tolerant method that fuses predicted structures with evolutionary sequence features; and multimodal representation learning that aligns molecular structures with textual and taxonomic information for property and drug–target interaction prediction. Across public benchmarks, these methods consistently outperform strong baselines, bridging macromolecular modeling and small-molecule design to provide a practical, noise-tolerant toolkit for biomolecular research and drug discovery.
Keywords
Machine learning, Deep learning, Drug discovery, Graph neural networks, Multimodal learning, Drug-target interaction, Molecular representation learning, Bioinformatics
Disciplines
Computer Engineering | Computer Sciences
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Recommended Citation
Jiang, Feng, "AI for Life Sciences: From Geometric Protein Modeling to Multimodal Drug Design" (2026). Computer Science and Engineering Dissertations. 17.
https://mavmatrix.uta.edu/cse_dissertations2/17