Research

My doctoral research lies at the intersection of graph neural networks, computational biology, and large-scale data analytics. I focus on developing novel deep learning architectures for drug discovery and molecular property prediction, leveraging big data to accelerate scientific breakthroughs.

Publications

Papers and ongoing research work

FiLM-DTI: Target-Conditioned Graph Message Passing for Large-Scale Drug-Target Interaction Prediction

Completed

Kalu K. Okonkwo — Colorado Technical University

This paper introduces FiLM-DTI, a graph neural network for drug-target interaction prediction that conditions every message-passing layer on the protein target via Feature-wise Linear Modulation (FiLM). Evaluated on 425,845 pairs from BindingDB 2024 with Murcko scaffold splitting, FiLM-DTI achieves ROC-AUC of 0.854 and PR-AUC of 0.915. A systematic ablation study isolates the contribution of early-layer target conditioning, and FiLM gamma vectors provide intrinsic per-target interpretability of chemical feature importance.

Drug-Target Interaction Graph Neural Networks FiLM Conditioning BindingDB Molecular Property Prediction Interpretability

Research Interests

Core areas that drive my doctoral research and future work

Graph Neural Networks

Developing novel GNN architectures for molecular representation learning and property prediction

Drug Discovery & DTI

Computational methods for predicting drug-target interactions to accelerate pharmaceutical research

Deep Learning

Feature-wise modulation, attention mechanisms, and conditional architectures for structured data

Big Data Analytics

Scalable methods for processing and analyzing large-scale datasets across distributed systems

Model Interpretability

Explainable AI techniques for understanding model decisions in safety-critical scientific domains

Computational Biology

Applying machine learning to biological data including molecular graphs and protein structures