Peng, F. Z. | bioRxiv Science

bioRxiv2026

AptaBLE: A Deep Learning Platform for Aptamer Generation and Analysis

Aptamers are single-stranded oligonucleotides that bind molecular targets with high affinity and specificity. However, their discovery remains time-consuming, expensive, and susceptible to experimental biases. Here we present AptaBLE, a deep learning framework for predicting aptamer-protein binding. Additionally, we demonstrate two de novo generation methods that produce novel aptamers with desired specificity profiles and Kds as low as 31 nM to-date. AptaBLE represents a significant advance towards therapeutic and diagnostic aptamer development.

bioinformatics↗

bioRxiv2025

EVOFLOW-RNA: GENERATING AND REPRESENTINGNON-CODING RNA WITH A LANGUAGE MODEL

RNA plays a critical role across numerous biological functions. Recent advances in language modeling show promise with representing RNA, but the possibility of large-scale RNA design and optimization has not been fully explored. We propose EvoFlow-RNA, a bidirectional non-coding RNA language model leveraging a masked discrete diffusion model (MDM) formulation for both generative modeling and representation learning. EvoFlow-RNA bridges the gap between RNA sequence representation and design. It outperforms leading RNA models on three BEACON tasks critical to understanding RNA function, spanning from structure prediction to gene editing. For unconditional generation, it synthesizes diverse RNA sequences with native-like structural and binding properties. Additionally, EvoFlow-RNA can globally redesign aptamer sequences around preserved binding recognition sites with enhanced functionality. Our results demonstrate the effectiveness of EvoFlow-RNA in RNA modeling, highlighting the capability and potential of masked discrete diffusion for both recapitulating and enhancing existing RNAs.

synthetic biology↗