We discover aptamers that can specifically recognize the intact target molecule, thereby ensuring consistent binding performance and a significantly higher success rate for small molecules.

By leveraging advanced computational simulations, we can predict and preserve the essential binding sites while removing non-critical regions. This makes it possible to design shorter, more cost-effective aptamers without compromising binding performance.

We employ machine learning techniques to train our models on existing aptamer sequences and binding data, thereby predicting novel sequence variants that offer stronger binding.