WYOMING AND COLORADO Pharmacophore Analysis Driven Deep Neural Networks to Discover Novel Anticancer Drug Combinations
JSHS · 2023
Overview
Drug combination therapies have shown effective performance in treating cancer through increased efficacy and circumvention of drug resistance through drug synergy. Two avenues can be used to discover drug combinations: a novel approach that utilizes natural products in the form of Traditional Chinese Medicine (TCM) herbs compared with the textbook approach of utilizing existing chemotherapy drug combinations. TCM herbs achieve efficacy due to synergistic interactions between the active ingredients. Therefore, the pharmacophore relationships in herbal compounds which synergize can potentially be applied to chemotherapy drugs to drive combination discovery. Machine learning approaches have been developed to identify drug combinations, especially deep neural networks (DNN), which have achieved state-of-the-art performance in many drug discovery tasks. Here, we developed a drug protein interaction (DPI) prediction DNN, DeepDPI, to employ DPI drug representations and achieved state-of-the-art performance. Two DNNs were also developed to predict novel drug combinations: DeepTCM, which predicts combinations in herbs, and DeepCombo, which predicts synergy in chemotherapy drugs. We used an ensemble architecture enhanced with a novel similarity- based weight adjustment (SBWA) approach and both models accurately predicted drug combinations for both known and unknown drugs. Lastly, a screening was conducted using each model where DeepTCM predicted combinations where drugs had similar targets, while DeepCombo predicted combinations where one agent potentiated the other, with both models’ predicted combinations investigated through a network-based analysis and identifying as a synergistic combinations in literature. DeepTCM illustrates how natural products such as TCM are a novel path where new drug combinations can be discovered. The Genomic Evolution of the SARS-CoV-2 Virus and Its Effects Kaelyn de Villiers SkyView Academy, Highlands Ranch, CO
Competition history
- JSHS 2023
Resources
Related projects
ISEF · 2026
The Virtual Cell 2.0: Expanding Precision and Personalized Cancer Therapy Through AI-Powered Simulation
ISEF · 2026
From SMILES to Synergy: Biologically-Enriched Drug Screening for Synergistic Combination Therapy in Tamoxifen Resistant ER+ Breast Cancer
ISEF · 2014
Rational Discovery and Optimization of Synergistic Chemotherapy Combinations: A Novel Framework Integrating Gene Perturbation Analysis and Machine Learning Algorithms
ISEF · 2016
Predictive Modeling of Optimal Cancer Therapies
JSHS · 2024
Equivariant Graph Attention Networks with Structural Motifs for Predicting Cell Line -Specific Synergistic Drug Combinations
ISEF · 2025
TETRA-C: Accelerating Cancer Therapy Through AI-Optimized Telomerase Inhibition, Enzymatic Targeting, and the Suppression of Metastasis
ISEF · 2023
Using Artificial Intelligence Neural Networks to Repurpose Pre-Existing Drugs and Analyze Drug Relationships, Year 3
ISEF · 2024
BioVision: A Novel in silico Drug Discovery Pipeline to Predict Drug Candidates for Non-Small Cell Lung Cancer
Closest projects by meaning, across every fair and year in the corpus.