Development of an AI-Powered RNA-Ligand Binding Prediction System for Accelerated Drug Discovery

RNA molecules, crucial as therapeutic targets such as bacterial ribosomes and human pre-mRNA, present significant challenges for drug targeting due to their flexible and dynamic structures. Limited experimental interaction data and the inadequacy of existing computational models impede accurate prediction of RNA-ligand interactions, thereby slowing drug discovery processes and increasing development costs.

A mid-to-large pharmaceutical research institute seeking advanced computational tools to predict RNA-ligand interactions for novel therapeutic target identification.

• Develop machine learning models capable of predicting RNA-ligand binding with high accuracy, aiming for AUROC scores surpassing 65-70%.
• Implement models that generalize well across different RNA targets, despite limited available interaction data.
• Reduce the time and resources required for initial drug screening by enabling rapid and reliable in silico predictions.
• Create a scalable, robust computational platform that supports high-throughput screening of potential RNA-targeted therapeutics.
• Improve the identification rate of active binders within the top-ranked compounds, aiming for EF10 scores significantly better than traditional methods.

• Data ingestion module to input RNA 3D structures, ligand structures, and interaction datasets.
• Feature engineering pipeline to generate chemical and interaction features at the nucleotide level, based on experimental structures.
• Custom neural network models employing transformer architectures capable of handling variable-length RNA sequences.
• Robust training and testing framework to evaluate model performance across multiple RNA targets, ensuring generalizability.
• Evaluation metrics dashboard displaying AUROC, EF10 scores, and other relevant performance indicators.
• User interface for visualizing prediction results, ranking compounds, and facilitating screening workflows.

• Structural biology databases for RNA and ligand structures
• Existing cheminformatics tools for feature extraction
• Laboratory data systems for experimental validation results

• High scalability to process large datasets and support extensive virtual screening campaigns
• Model accuracy with AUROC scores consistently exceeding 65-70% on test and validation datasets
• Fast inference times to enable rapid screening of thousands of compound-RNA pairs
• Secure data storage and compliance with data privacy standards
• User-friendly interface with visualization capabilities for researchers and drug discovery teams

The proposed platform aims to significantly enhance the accuracy of RNA-ligand interaction predictions, achieving AUROC scores between 65-70%—substantially outperforming current state-of-the-art methods. It will enable faster identification of promising compounds, thereby reducing drug discovery timelines and costs. The system is expected to improve active binder retrieval rates within top-ranked compounds, boosting the efficiency of RNA-targeted drug development and expanding therapeutic possibilities for previously undruggable diseases.