Back

AptaBLE: A Deep Learning Platform for Aptamer Generation and Analysis

Patel, S.; Peng, F. Z.; Fraser, K.; Dwivedy, A.; Gandavadi, D.; Wang, X.; Chatterjee, P.; Yao, S.

2026-01-07 bioinformatics
10.64898/2026.01.06.698056 bioRxiv
Show abstract

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.

Matching journals

The top 7 journals account for 50% of the predicted probability mass.

1
Nature Communications
○ 5641 papers in training set
Top 7%
21.6%
Similar papers in this journal
2
Nature Biotechnology
○ 172 papers in training set
Top 0.5%
6.6%
Similar papers in this journal
3
Nature Chemical Biology
○ 119 papers in training set
Top 0.4%
5.4%
Similar papers in this journal
4
Nature Methods
○ 385 papers in training set
Top 2%
5.1%
Similar papers in this journal
5
Molecular Therapy Nucleic Acids
○ 39 papers in training set
Top 0.2%
5.1%
Similar papers in this journal
6
ACS Central Science
● 71 papers in training set
Top 0.2%
4.0%
7
Nature Machine Intelligence
○ 70 papers in training set
Top 0.8%
3.4%
50% of probability mass above
8
Cell Chemical Biology
○ 94 papers in training set
Top 0.4%
3.2%
9
Cell Reports Methods
○ 165 papers in training set
Top 0.7%
3.1%
10
Chemical Science
● 73 papers in training set
Top 0.6%
3.1%
11
Nature Biomedical Engineering
○ 47 papers in training set
Top 0.5%
2.4%
12
Nano Letters
● 71 papers in training set
Top 0.5%
2.3%
13
Advanced Science
○ 286 papers in training set
Top 4%
2.1%
14
Proceedings of the National Academy of Sciences
● 2444 papers in training set
Top 24%
2.1%
15
iScience
○ 1154 papers in training set
Top 16%
1.9%
16
mAbs
○ 32 papers in training set
Top 0.2%
1.9%
17
Science Advances
● 1243 papers in training set
Top 20%
1.7%
18
Communications Biology
○ 993 papers in training set
Top 16%
1.7%
19
Nucleic Acids Research
◐ 1281 papers in training set
Top 10%
1.5%
20
Cell
○ 431 papers in training set
Top 7%
1.4%
21
Cell Genomics
○ 172 papers in training set
Top 4%
1.1%
22
Cell Reports Medicine
○ 153 papers in training set
Top 4%
1.1%
23
Cell Reports
○ 1498 papers in training set
Top 23%
1.1%
24
eLife
● 5828 papers in training set
Top 60%
1.1%
25
Cell Systems
○ 201 papers in training set
Top 4%
1.0%
26
Briefings in Bioinformatics
◐ 354 papers in training set
Top 6%
1.0%
27
Science
● 477 papers in training set
Top 8%
1.0%
28
Scientific Reports
○ 3612 papers in training set
Top 73%
0.8%
29
ACS Synthetic Biology
● 287 papers in training set
Top 2%
0.8%

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.