Hunting for chemicals with favorable pharmacological, toxicological, and pharmacokinetic
properties remains a formidable challenge for drug discovery. Deep learning provides us with …

A machine-learning model trained on the whole Cambridge Structural Database was developed
to assist high-throughput cocrystal screening. With only 2D structures taken as inputs, …

Developing new drugs remains prohibitively expensive, time-consuming, and often involves
safety issues. Accurate prediction of drug-target interactions (DTIs) can guide the drug …

Motivation Identifying compound–protein interaction (CPI) is a crucial task in drug discovery
and chemogenomics studies, and proteins without three-dimensional structure account for a …

The success of artificial intelligence (AI) models has been limited by the requirement of large
amounts of high-quality training data, which is just the opposite of the situation in most drug …

The lack of functionalities such as degradability, antibacterial, and flame retardancy is the key
to limit the further wide application of conventional epoxy thermosets. In this work, a novel …

Rationale: Lung-protective ventilatory strategies have been widely used in patients with acute
respiratory distress syndrome (ARDS), but the ARDS mortality rate remains unacceptably …

The type 2 diabetes risk gene TCF7L2 is the effector of the Wnt signaling pathway. We
found previously that in gut endocrine L-cell lines, TCF7L2 controls transcription of the …

The kinome-wide virtual profiling of small molecules with high-dimensional structure–activity
data is a challenging task in drug discovery. Here, we present a virtual profiling model …

Purpose: The aim of this study is to investigate the antimyeloma activity of a novel Bcl-2 family
inhibitor, ABT-737, in preclinical treatment of multiple myeloma. Experimental Design: The …