A multilineage screen identifies actionable synthetic lethal interactions in human cancers.

Cancers are driven by alterations in diverse genes, creating dependencies that can be therapeutically targeted. However, many genetic dependencies have proven inconsistent across tumors. Here we describe SCHEMATIC, a strategy to identify a core network of highly penetrant, actionable genetic interactions.

Gender and age biases in the assessment of speech accuracy: A study of speech-language clinicians' ratings of /s/ accuracy.

Purpose: Typically developing children assigned male at birth (AMAB) and children assigned female at birth (AFAB) produce the fricative /s/ differently: AFAB children produce /s/ with a higher spectral peak frequency. This study examined whether implicit knowledge of these differences affects speech-language pathologists'/speech and language therapists' (SLPs'/SLTs') ratings of /s/ accuracy, by comparing ratings made in conditions where SLPs/SLTs were blind to children's sex assigned at birth (SAB) to conditions in which they were told this information.

Methods: SLPs (n = 95) varying in clinical experience rated the accuracy of word-initial /s/ productions (n = 87) of eight children with speech sound disorder in one of four conditions: one in which no information about the children was revealed, one in which children's SAB was revealed, one in which children's age was revealed, and one in which both were revealed.

De novo generation of multi-target compounds using deep generative chemistry.

Polypharmacology drugs-compounds that inhibit multiple proteins-have many applications but are difficult to design. To address this challenge we have developed POLYGON, an approach to polypharmacology based on generative reinforcement learning. POLYGON embeds chemical space and iteratively samples it to generate new molecular structures; these are rewarded by the predicted ability to inhibit each of two protein targets and by drug-likeness and ease-of-synthesis.