Enterprise AI Analysis
Advancing Drug-Drug Interaction Prediction with Biomimetic Improvements: Leveraging the Latest Artificial Intelligence Techniques to Guide Researchers in the Field
Published: January 5, 2026 | Authors: Ridwan Boya Marqas, Zsuzsa Simó, Abdulazeez Mousa, Fatih Özyurt, and Laszlo Barna Iantovics
Drug-drug interactions (DDIs) can cause adverse reactions or reduce the efficiency of a drug. Using computers to predict DDIs is now critical in pharmacology, as this reduces risks, improves drug outcomes and lowers healthcare costs. Clinical trials are slow, expensive, and require a lot of effort. The use of artificial intelligence (AI), primarily in the form of machine learning (ML) and its subfield deep learning (DL), has made DDI prediction more accurate and efficient when handling large datasets from biological, chemical, and clinical domains. Many ML and DL approaches are bio-inspired, taking inspiration from natural systems, and are considered part of the broader class of biomimetic methods. This review provides a comprehensive overview of AI-based methods currently used for DDI prediction. These include classical ML algorithms, such as logistic regression (LR) and support vector machines (SVMs); advanced DL models, such as deep neural networks (DNNs) and long short-term memory networks (LSTMs); graph-based models, such as graph convolutional networks (GCNs) and graph attention networks (GATs); and ensemble techniques. The use of knowledge graphs and transformers to capture relations and meaningful data about drugs is also investigated. Additionally, emerging biomimetic approaches offer promising directions for the future in designing AI models that can emulate the complexity of pharmacological interactions. These upgrades include using genetic algorithms with LR and SVM, neuroevaluation (brain-inspired model optimization) to improve DNN and LSTM architectures, ant-colony-inspired path exploration with GCN and GAT, and immune-inspired attention mechanisms in transformer models. This manuscript reviews the typical types of data employed in DDI (pDDI) prediction studies and the evaluation methods employed, discussing the pros and cons of each. There are useful approaches outlined that reveal important points that require further research and suggest ways to improve the accuracy, usability, and understanding of DDI prediction models.
Executive Impact Snapshot
Leveraging AI and biomimetic strategies in DDI prediction offers significant advantages, from reducing risks to optimizing healthcare costs.
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Reduction in DDI Risk
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Improvement in Prediction Accuracy
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Healthcare Cost Reduction
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Clinical Trial Efficiency Gains
Deep Analysis & Enterprise Applications
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Technology Overview
Key Findings
Challenges & Limitations
Methodology
Future Directions
AI & Biomimetic Approaches in DDI Prediction
This review comprehensively covers the range of AI methods employed for DDI prediction, from classical machine learning (ML) algorithms like Logistic Regression (LR) and Support Vector Machines (SVMs) to advanced deep learning (DL) architectures such as Deep Neural Networks (DNNs), Long Short-Term Memory (LSTMs), Graph Convolutional Networks (GCNs), and Graph Attention Networks (GATs). It highlights the increasing adoption of transformer-based models and ensemble techniques, alongside the critical role of Knowledge Graphs (KGs) and Physiologically Based Pharmacokinetics (PBPK) modeling for mechanistic understanding.
A key focus is on biomimetic strategies, which draw inspiration from natural systems. These include genetic algorithms for optimization, neuroevolution for brain-inspired model enhancements, ant-colony-inspired path exploration for graph models, and immune-inspired attention mechanisms for transformers. These bio-inspired methods are crucial for handling complex pharmacological interactions and improving model adaptability and efficiency.
| Approach | Key Methods | Performance (ROC-AUC) | Strengths | Limitations | Scalability |
|---|---|---|---|---|---|
| Traditional ML | LR, SVM, RF, XGBoost | 0.85-0.90 |
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Medium-sized datasets |
| Ensemble Methods | XGBoost, CatBoost, RF | 0.88-0.92 |
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Large structured datasets |
| Deep Learning | DNNs, CNNs, LSTMs, Transformers, Autoencoders | 0.87-0.92 |
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High |
| Graph-Based Models | GADNN, GAT, KG Embedding, GCN | 0.90-0.92 |
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With graph optimization |
| Transformer Models | DDI-Transform, RTS, PTB-DDI | 0.91-0.93 |
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With pretraining |
| Hybrid and Multimodal | Graph, Text Mining, Biomedical KG, Multi-modal Fusion | 0.90-0.92 |
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Multi-source integration |
| PBPK Models | PBPK Models, Enzyme and Transporter Networks | Mechanistic Evaluation |
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Simulation and clinical decision support |
| Fuzzy Logic and Rule-Based | Fuzzy Logic Models, Signal Detection | 0.87-0.89 |
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Rule-based knowledge systems |
Core Insights from DDI Prediction Research
The study reveals that while classical ML models provide a strong baseline, Deep Learning and Graph Neural Networks, especially when integrated with Knowledge Graphs, offer superior performance in handling complex, high-dimensional DDI data. These advanced models excel at capturing nonlinear patterns and relational semantics essential for accurate predictions. Hybrid and multimodal approaches, which combine various data types and model architectures, consistently achieve high accuracy and enhanced explainability.
PBPK models remain indispensable for providing mechanistic insights into drug metabolism and interactions, bridging the gap between computational predictions and physiological realities. The growing adoption of biomimetic strategies, such as neuroevolution and immune-inspired attention mechanisms, further refines these AI models, pushing the boundaries of adaptability and efficiency in predicting DDI events.
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References for Deep Learning (Transformers, Hybrid) Models (Fig 4)
Navigating Obstacles in DDI Prediction AI
Despite significant advancements, DDI prediction faces several persistent challenges. Data quality and standardization remain critical issues, with datasets often being inconsistent, noisy, and incomplete, hindering model accuracy and reliability. The computational intensity of advanced DL and GNN models requires substantial resources, posing scalability challenges for real-time clinical application.
Interpretability, or Explainable AI (XAI), is another major hurdle; while models achieve high accuracy, understanding why they make certain predictions is crucial for clinical trust and adoption. Issues such as class imbalance (few positive DDI examples) and the need for robust external validation to ensure generalizability across diverse patient populations also limit clinical translation. Addressing these limitations is paramount for widespread impact.
Overcoming Roadblocks in DDI Prediction AI
Problem: The current landscape of DDI prediction, while advanced, faces critical challenges that hinder its full potential in clinical and pharmacological settings. These include issues with data quality, computational resource demands, and the inherent complexity of integrating diverse biological and chemical information.
Solution Approach: Our analysis highlights the necessity for standardized data preprocessing, advanced biomimetic optimization techniques for model architectures, and robust explainable AI (XAI) methods. Implementing multi-omics data integration and leveraging knowledge graphs are key to building more reliable and clinically interpretable predictive systems.
Impact: Addressing these challenges will lead to more precise, scalable, and trustworthy DDI prediction models, ultimately enhancing drug safety, improving patient outcomes, and significantly reducing healthcare costs associated with adverse drug reactions.
Robust Methodology for DDI Prediction
The review adhered to PRISMA 2020 guidelines, systematically examining literature published between January 2019 and November 2025. Inclusion criteria focused on computational/AI models for DDI prediction using known DDI/ADR data sources (DrugBank, ChEMBL, KEGG, FAERS, SIDER, TWOSIDES) or novel curated datasets, with clearly defined ground truth.
A rigorous preprocessing pipeline is essential, encompassing data cleaning (entity normalization, duplicate removal), feature normalization and scaling (e.g., molecular weight, logP, IC50), handling missing values (imputation), data integration (aligning identifiers, standardizing formats), and adverse event signal filtering (mitigating reporting biases). This meticulous approach ensures data quality and consistency, laying a solid foundation for robust DDI prediction models.
Enterprise Process Flow
Data Cleaning
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Feature Normalization
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Handling Missing Data
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Data Integration
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Adverse Event Signal Filtering
Future Trajectories for DDI Prediction AI
Future research in DDI prediction is poised for significant advancements by further integrating biomimetic strategies, such as neuroevolution for model optimization, ant-colony-inspired path exploration for graph analysis, and immune-inspired attention mechanisms in transformer models. These approaches promise to enhance AI models' ability to emulate complex biological interactions and adapt to new data patterns more effectively.
Key directions include robust multi-omics data integration (genomics, proteomics, metabolomics) with chemical and clinical data, development of more interpretable AI (XAI) techniques, and a stronger focus on external validation and standardized benchmarks. The goal is to create DDI prediction models that are not only highly accurate but also clinically transparent, scalable, and directly translatable into improved patient safety and personalized medicine.
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Enhanced Adaptability & Efficiency from Biomimetic AI
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Your DDI AI Implementation Roadmap
A typical phased approach to integrate advanced AI and biomimetic strategies for superior DDI prediction.
Data Acquisition & Preprocessing (3-6 Months)
Initial phase focusing on gathering diverse datasets (DrugBank, ChEMBL, KEGG, FAERS) and applying rigorous cleaning, normalization, imputation, and signal filtering techniques. This ensures high-quality input for AI models and aligns with the transparency principles.
Model Selection & Training (4-8 Months)
Involves choosing and training appropriate AI models, ranging from classical ML to advanced DL, GNNs, and Transformer-based architectures. This phase emphasizes leveraging biomimetic optimizations (e.g., genetic algorithms for hyperparameter tuning) to enhance model efficiency and predictive power.
Validation & Interpretability Integration (2-4 Months)
Focus on validating model performance using various metrics (ROC-AUC, PR-AUC, PPV, LR+) and ensuring clinical relevance. Integration of Explainable AI (XAI) methods, such as attention mechanisms and graph-based rationales, is critical to gain clinician trust and facilitate deployment.
Deployment & Monitoring (1-2 Months)
Implementing the validated DDI prediction system into clinical workflows (e.g., EHR systems) and pharmacovigilance platforms. Continuous monitoring for performance, data drift, and feedback mechanisms for iterative refinement are essential for long-term effectiveness and patient safety.
Iterative Refinement & Expansion (Ongoing)
Ongoing process of updating models with new data, incorporating advanced biomimetic strategies (e.g., neuroevolution, immune-inspired attention), and expanding capabilities to include multi-omics data and novel drug targets, ensuring the system remains cutting-edge and adaptable.
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