Deep learning describes a class of machine learning algorithms that are capable of
combining raw inputs into layers of intermediate features. These algorithms have recently …

Cancer is associated with significant morbimortality globally. Advances in screening,
diagnosis, management and survivorship were substantial in the last decades, however …

Activity and property prediction models are the central workhorses in drug discovery and
materials sciences, but currently, they have to be trained or fine-tuned for new tasks. Without …

Deep generative adversarial networks (GANs) are the emerging technology in drug
discovery and biomarker development. In our recent work, we demonstrated a proof-of …

Drug discovery and development is a notoriously risky process with high failure rates at
every stage, including disease modeling, target discovery, hit discovery, lead optimization …

As the field of artificial intelligence and machine learning (AI/ML) for drug discovery is rapidly
advancing, we address the question “What is the impact of recent AI/ML trends in the area of …

Clinical trials are crucial for drug development but often face uncertain outcomes due to
safety, efficacy, or patient-recruitment problems. We propose the Hierarchical Interaction …

Adverse side effects of drug–drug interactions induced by human cytochrome P450
(CYP450) inhibition is an important consideration in drug discovery. It is highly desirable to …

Predicting novel small molecule bioactivities for the target deconvolution, hit-to-lead
optimization in drug discovery research, requires molecular representation. Previous reports …

Undesired toxicity is a major hindrance to drug discovery and largely responsible for high
attrition rates in early stages. This calls for new, reliable, and interpretable molecular …