This Case Shows Specific Expertise. Find the Companies with the Skills Your Project Demands!

You're viewing one of tens of thousands of real cases compiled on Many.dev. Each case demonstrates specific, tangible expertise.

But how do you find the company that possesses the exact skills and experience needed for your project? Forget generic filters!

Our unique AI system allows you to describe your project in your own words and instantly get a list of companies that have already successfully applied that precise expertise in similar projects.

Create a free account to unlock powerful AI-powered search and connect with companies whose expertise directly matches your project's requirements.

Development of Advanced Machine Learning Models for Predicting RNA-Ligand Interactions in Drug Discovery

appsilon.com

Medical

Pharmaceuticals

Biotechnology

Challenges in Predicting RNA-Ligand Interactions for Drug Development

RNA's flexible structures, limited experimental datasets, and limitations of existing computational models hinder accurate prediction of RNA-ligand interactions. Current methods struggle with variable-length RNA sequences and generalization across diverse RNA targets, delaying RNA-targeted drug development.

About the Client

Leading research institute focused on molecular biology and RNA-targeted drug discovery

Objectives for Developing RNA-Ligand Interaction Prediction Models

Improve prediction accuracy beyond state-of-the-art methods (AUROC >65%)
Enable efficient screening of ligand candidates using EF10 metrics
Develop models generalizable across diverse RNA structures
Accelerate drug discovery timelines through rapid model deployment

Core System Functionalities for RNA-Ligand Interaction Prediction

Transformer-based neural networks for variable-length RNA sequence analysis
Automated feature engineering pipeline for nucleotide-level interaction data
Cross-RNA validation framework for robustness testing
EF10 scoring implementation for candidate ranking

Technologies for RNA Interaction Modeling

Python (TensorFlow/PyTorch)

Transformer architecture

3D structural data processing tools

Bioinformatics pipelines (e.g., fingeRNAt)

System Integrations for Drug Discovery Workflow

Experimental RNA-ligand interaction databases
Molecular docking software (e.g., AutoDock)
High-throughput screening platforms

Performance and Scalability Requirements

Scalability for large 3D structural datasets
Model accuracy maintenance across RNA classes
Processing speed for rapid iteration cycles
Data security for proprietary interaction datasets

Expected Impact on Pharmaceutical Drug Discovery

Enables 30% faster identification of active binders through improved EF10 scores, reduces experimental screening costs by 40%, and expands druggable RNA targets by 200%. Accelerates development of therapies for previously undruggable diseases while enhancing understanding of RNA biology.