text: FRELEX was developed in 2016 by [https://neoaptamers.com/ NeoVentures Biotechnology Inc] to allow the selection of aptamers without immobilizing the target or the oligonucleotide library. Immobilization is a necessary component of SELEX; however, it has the potential to inhibit key epitopes, and thus weaken the likelihood of successful binding, particularly when working with small molecules. FRELEX follows a similar overall methodology to SELEX; however, instead of immobilizing the target, the researcher introduces a series of random and blocker oligonucleotides to an immobilization field before introduction to the target. This allows the researcher to better target small molecules that may be lost during partitioning. It also can be used in some circumstances to select an aptamer library without knowing the target.
  Most modern aptamer selection methods strive to improve the conventional SELEX aptamer search method. Despite the publication of various methods aimed at increasing the affinity and specificity of aptamers, experimental approaches face limitations in the number and variety of sequences that can be examined and selected. Library capacity for SELEX experiments is practically limited to 10 candidates, whereas, assuming there is a 4-monomeric repertoire from which pools can be created, there are ~1.6 × 10 unique sequences in sequence space limited to a 100-residue matrix, which is clearly beyond experimental capabilities. The library of oligonucleotides must be extremely diverse and not contain linear, incapable of providing a stable spatial arrangement, and double-stranded structures; due to these limitations, oligonucleotide libraries can cover the diversity of only ~10 sequences. This means that existing aptamers may not fully cover the diversity of target molecules or may not have optimal properties due to limitations of the underlying method. To yield the best possible aptamers one must maximize the effectiveness of the discovery process and the library itself.
  RNA and DNA secondary structure prediction by dynamic programming algorithms such as RNAfold (ViennaRNA)  and by machine learning models such as SPOT-RNA, MXfold2  provides the opportunity to assess the ability of sequences in the primary library to fold into complex structures, allowing for the selection of only the most promising sequences from the entire pool. However, these algorithms are low-performance, making them poorly suited for this task. For this reason, algorithms like Ufold from the University of California  and AliNA from Nanobiorobots Inc.  have been developed, which demonstrate a significant increase in computational speed due to their faster architecture, and can be applied for preliminary in silico analysis of these libraries.