Abstract: The present disclosure relates to a closed loop aptamer development system that leverages in vitro experiments and in silico computation and artificial intelligence-based techniques to iteratively improve a process for identifying binders that can bind a molecular target. Particularly, aspects of the present disclosure are directed to obtaining, using an experimental assay, experimental data for a set of aptamers. The experimental data includes multiple pairs of data, each pair of data having: (i) an aptamer sequence for an aptamer from a set of aptamers, and (ii) a measurement for the characteristic of the aptamer with respect to a given target. A reward model is fine-tuned, using the experimental data, to predict a function-approximation metric for the characteristic of each aptamer in the set of aptamers. A decoder model is fine-tuned for generating novel aptamer sequences based on the function-approximation metric generated by the reward model for the novel aptamer sequences.

Abstract: Some techniques relate to projecting aptamer representations into an embedding space and clustering the representations. A cluster-specific binding metric can be defined for each cluster based on aptamer-specific binding metrics of aptamers associated with the cluster. A subset of the clusters can be selected based on the cluster-specific binding metrics. Identifications of aptamers assigned to the subset of clusters can then be output.

Abstract: A multi-dimensional latent space (defined by an Encoder model) corresponds to projections of sequences of aptamers. An architecture of the Encoder model, a hyperparameter of the Encoder model, or a characteristic of a training data set used to train the Encoder model was selected using an assessment of an encoding-efficiency of the Encoder model that is based on: a predicted extents to which representations in an embedding space are indicative of specific aptamer sequences to which a probability distribution of the embedding space differs from a probability distribution of a source space that represents individual base-pairs; generating projections in the latent space using representations of aptamers and the Encoder model; identifying one or more candidate aptamers for the particular target using the projections and the Decoder model; and outputting an identification of the one or more candidate aptamers.