While the primary sequences of human proteins have been cataloged for over a decade,
determining how these are organized into a dynamic collection of multiprotein assemblies …

Machine learning-based drug discovery success depends on molecular representation. Yet
traditional molecular fingerprints omit both the protein and pointers back to structural …

Computational pharmacogenomics can potentially identify new indications for already
approved drugs and pinpoint compounds with similar mechanism-of-action. Here, we used …

A drug Mechanism of Action (MoA) is a complex biological phenomenon that describes how
a bioactive compound produces a pharmacological effect. The complete knowledge of MoA …

Fluorescent-based microscopy screens carry a broad range of phenotypic information about
how compounds affect cellular biology. From changes in cellular morphology observed in …

High-throughput drug screening--using cell imaging or gene expression measurements as
readouts of drug effect--is a critical tool in biotechnology to assess and understand the …

Summary Background Although Deep Neural Networks (DDNs) have been successful in
predicting the efficacy of cancer drugs, the lack of explainability in their decision-making …

Understanding the mechanisms of actions (MOAs) of compounds is crucial in drug
discovery. A common step in drug MOAs annotation is to query the dysregulated gene …

Motivation Molecular core structures and R-groups are essential concepts in drug
development. Integration of these concepts with conventional graph pre-training approaches …

Agents that stimulate the endoplasmic reticulum (ER) stress pathway are being exploited
pharmacologically to induce cancer cell death. Cytotoxic ER stress is typically regulated by …