RL Aptamer Generation Using Omics Feedback for Binding Specificity
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Solution Overview
Problem
Current protein structural prediction platforms fail to capture dynamic ligand-protein interactions and off-target binding, leading to high false positive rates in virtual screening, and existing language models suffer from limited in vivo stability and specificity of aptamer drug conjugates.
Innovation Solution
A reinforcement learning (RL)-fine-tuned genomics-proteomics Large Language Model (LLM) is used to generate aptamer sequences optimized for high-affinity binding and nuclease resistance, incorporating feedback from multimodal models like OmniBioTA and molecular dynamics simulations to predict binding energy and stability, followed by quantum mechanical evaluation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current protein structural prediction platforms are used, then structural predictions can be obtained, but dynamic ligand-protein interactions and off-target binding cannot be captured, leading to high false positive rates
Solution Approach 1:
The patent combines multiple prediction models (OmniBioTA for binding energy, molecular dynamics simulations for temporal dynamics, quantum simulations for electronic interactions) into an integrated reinforcement learning framework. This merging allows the system to capture both static structural predictions and dynamic interaction behaviors, reducing false positives by validating predictions across multiple computational scales and methodologies.
Solution Approach 2:
The system implements feedback loops where prediction results from OmniBioTA and molecular dynamics simulations are fed back into the reinforcement learning model to iteratively refine binding energy predictions. This feedback mechanism allows continuous improvement of prediction accuracy by learning from both successful and failed virtual screening cases, thereby reducing false positive rates over time.
2Productivity
If existing language models are used to generate aptamer sequences, then sequence generation can be performed, but in vivo stability and specificity are limited
Solution Approach 1:
The reinforcement learning model uses feedback from multimodal predictions (binding affinity, stability scores, specificity metrics) to iteratively optimize aptamer sequence generation. The model learns from predicted outcomes to generate sequences that simultaneously achieve high productivity in sequence generation while improving in vivo stability and specificity through repeated refinement cycles.
Solution Approach 2:
The system optimizes multiple sequence parameters simultaneously (nucleotide composition, secondary structure propensity, GC content, motif distribution) using reinforcement learning. By changing and optimizing these parameters in coordination rather than independently, the model generates aptamers with enhanced in vivo stability and specificity while maintaining efficient sequence generation capability.
Data Source
AI summary
Method, system and computer-accessible medium can be provided for generating one or more drug conjugates of one or more small molecules. For example, with such exemplary method, system and computer-accessible medium, a multimodal discriminative model can be trained to predict at least one peptide-ligand binding for one or more DNA ligands, a generative nucleotide model can be trained to generate a plurality of compounds. Further, a feedback can be provided from the multimodal discriminative model to fine-tune the generative nucleotide model so as to facilitate the generation of the drug conjugate(s).