Graph machine learning (GML) is receiving growing interest within the pharmaceutical and
biotechnology industries for its ability to model biomolecular structures, the functional …

Artificial intelligence (AI), and, in particular, deep learning as a subcategory of AI, provides
opportunities for the discovery and development of innovative drugs. Various machine …

Limitations on the number of unique protein and DNA molecules that can be characterized
microscopically in a single tissue specimen impede advances in understanding the …

Cancer is a devastating disease that takes the lives of hundreds of thousands of people
every year. Due to disease heterogeneity, standard treatments, such as chemotherapy or …

Animal models have historically played a critical role in the exploration and characterization
of disease pathophysiology, target identification, and in the in vivo evaluation of novel …

In recent decades, in silico absorption, distribution, metabolism, excretion (ADME), and
toxicity (T) modelling as a tool for rational drug design has received considerable attention …

Dynamic mapping of the cell-generated force of cardiomyocytes will help provide an intrinsic
understanding of the heart. However, a real-time, dynamic, and high-resolution mapping of …

Highlights•Phenotypic screening is a renewed approach for lead discovery.•Phenotypic
screen approach may improve the success rate of drug approval.•New drug targets can be …

The reproducibility of experiments has been a long standing impediment for further scientific
progress. Computational methods have been instrumental in drug discovery efforts owing to …

Moving from cell monolayers to three-dimensional (3D) cultures is motivated by the need to
work with cellular models that mimic the functions of living tissues. Essential cellular …