DeepAptamer: Advancing high-affinity aptamer discovery with a hybrid deep learning model.

ABSTRACT: Oligonucleotide aptamers are typically identified through a rigorous and time-consuming process known as systematic evolution of ligands by exponential enrichment (SELEX), which requires 20 to 30 iterative rounds to eliminate non/weak binding sequences and enrich tight binding sequences with high affinity. Moreover, inherent experimental biases and non-specific interactions within SELEX could inadvertently exclude high-affinity candidates, leading to a high failure rate. To address these challenges, we proposed DeepAptamer for identifying high-affinity sequences from unenriched early SELEX rounds. As a hybrid neural network model combining convolutional neural networks and bidirectional long short-term memory, DeepAptamer integrated sequence composition and structural features to predict a

SUBMITTER: Yang X 

PROVIDER: S-EPMC11787022 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature