Abstract: Systems and methods for high spatial and temporal resolution of environmental observations. Improved resolution is achieved by using machine learning methods to build a function from observations from frequently measuring stationary sensors to another sensor in a different location at a corresponding time. Given values from the reference sensor, the learned function can impute sensor measurements in unobserved locations and times.

Abstract: Embodiments of the disclosure involve training machine learned models using DNA-encoded library experimental data outputs and for deploying the trained machine learned models for conducting a virtual compound screen, for performing a hit selection and analysis, or for predicting binding affinities between compounds and targets. Machine learned models are trained using one or more augmentations that selectively expand molecular representations of a training dataset. Furthermore, machine learned models are trained to account for confounding covariates, thereby improving the machine learned models' abilities to conduct a virtual screen, perform a hit selection, and to predict binding affinities.