RECURSION PHARMACEUTICALS, INC.

A system for determining a goodness of a deep learning model comprises a memory coupled with a processor. The processor accesses a first set of vectors representative of images of a biological assay. The vectors of the first set of vectors are outputs of a first deep learning model. The processor creates a first distribution of a first plurality of pairwise comparisons of vectors, of the first set of vectors, which were generated from image pairs with similar cell perturbations. The processor creates a second distribution of a second plurality of pairwise comparisons of vectors, of the first set of vectors, which were generated from image pairs with dissimilar cell perturbations. The processor determines a difference between the first distribution and the second distribution and uses the difference to make a determination of goodness of the deep learning model as applied to the biological assay.

A system for selecting CRISPR guides for knocking out one or more target genes in a target cell from a multiplicity of candidate guides comprises a memory and a processor. The processor determines whether the candidate guide meets a plurality of thresholds. The thresholds are associated with: a transcript support level; targeting a consensus sequence of a target gene; which exon of the target gene is targeted; targeting of a primary transcript, targeting of a common isoform; a precomputed prediction of editing outcomes; mapping to an expressed sequence; fraction of gene expression attributable to targeted transcripts; a common SNP overlap threshold; which exon of the target gene is targeted; overlap of a selected guide; predicted frameshift percentage; maximum and minimum GC content; off target score; where a coding sequence is targeted. In response to meeting the thresholds, the processor selects the candidate guide as a selected CRISPR guide.

Methods of measuring attributes of animal urine, using a test pad comprising multiple test patches and urine detection patches in a BAYER pattern is described. The pad comprises different test patches, each surrounded by urine detection patches. When a camera, electronics and software automatically detect fresh urine from a color change of a detection patch, nearby test patches are read with a color camera, after a specific time delay, and compared to color reference spots. Multiple layers and isolation zones in the test pad allow urine to enter the test and detection patches, while keeping urine puddles from spreading. Once used, detection and test patches are not used again. An array of many detection and test patches allows the test pad to be used for multiple urine samples in one vivarium cage before replacing. Embodiments use a mix of IR and white light, and IR cameras and color cameras.

The disclosure provides methods and systems for identifying a subset of compounds in a plurality of compounds. The identifying includes obtaining, for each compound, a vector including a set of elements, where each element includes a measurement of a different feature of an instance of a cell context upon exposure to the compound. The identifying includes repeating the obtaining for a plurality of cell contexts, to obtain a plurality of vectors for each compound across different cell contexts. The identifying includes combining the vectors for each compound to form a combined vector for each compound, thereby forming a plurality of combined vectors representing different compounds. The identifying includes pruning the plurality of compounds to the subset of compounds based on a similarity between respective combined vectors in the plurality of combined vectors corresponding to compounds in the plurality of compounds.

In various embodiments, an experiment analysis application detects executional artifacts in experiments involving microwell plates. The experiment analysis application computes one or more sets of spatial features based on one or more heat maps associated with a microwell plate. The experiment analysis application then aggregates the set(s) of spatial features to generate a feature vector. The experiment analysis application inputs the feature vector into a trained classifier. In response, the trained classifier generates a label indicating that the microwell plate is associated with a first executional artifact.

The present disclosure provides CRISPC-Cas systems for the creating mutated genes within cells within wells of an addressable well plate.

Evaluating an effect of one or more perturbations on cells of a first cell type is described. One method includes obtaining a screen definition for a screen, where the screen includes a cell-based assay that is run on a temporarily contiguous basis using a plurality of multi-well plates. The method includes obtaining control vectors including measurements of corresponding features of cells in control wells. The method includes obtaining test vectors including measurements of corresponding features of cells in test wells. The method includes forming a variability model based on a variance across the of control vectors, and embedding test vectors onto the variability model, thereby obtaining a set of variability model values. The method includes using the set of variability model values to resolve an effect of at least one data perturbation in the plurality of data perturbations on the first cell type.

In various embodiments, a molecule exploration application determines one or more potential drug candidates during a drug discovery process. The molecule exploration application generates derived molecule specifications based on a query molecule specification and edit heuristics. Subsequently, the molecule exploration application performs, via a mapping algorithm, one or more mapping operations on the derived molecule specifications to generate mapped molecule specifications. The molecule exploration application then performs one or more search operations on a mapped catalog of molecules based on the mapped molecule specifications to determine the one or more potential drug candidates. Advantageously, the molecule exploration application can be used to efficiently determine additional drug development candidates during a drug discovery process.

In various embodiments, an experiment analysis application detects executional artifacts in experiments involving microwell plates. The experiment analysis application computes one or more sets of spatial features based on one or more heat maps associated with a microwell plate. The experiment analysis application then aggregates the set(s) of spatial features to generate a feature vector. The experiment analysis application inputs the feature vector into a trained classifier. In response, the trained classifier generates a label indicating that the microwell plate is associated with a first executional artifact.

In various embodiments, a training application trains a classifier to detect executional artifacts in experiments involving microwell plates. The training application computes spatial information based on a heat map associated with a microwell plate. The training application then computes a set of features based on the spatial information. Subsequently, the training application executes one or more machine learning operations based, at least in part, on the set of features to generate a trained classifier. The trained classifier classifies sets of features associated with different microwell plates with respect to labels associated with executional artifacts. Advantageously, the trained classier can be used to accurately and consistently detect executional artifacts across different experiments and over time.