Endogenous intracellular allosteric modulators of GPCRs remain largely unexplored, with limited binding and phenotype data available. This gap arises from the lack of robust computational methods for ...

Molecular machine learning (ML) underpins critical workflows in drug discovery, material science, and catalyst optimization ...

Water is everywhere and comes in many forms: snow, sleet, hail, hoarfrost, and so on. However, despite water being so commonplace, scientists still do not fully understand the predominant physical ...

By creating a cohesive AI-enabled and physics-based molecular modeling and drug discovery pipeline built around film industry animation software, researchers at the Wyss Institute at Harvard ...

The effects of sequential cross-linking and scission of polymer networks formed in two states of strain are investigated using molecular dynamics simulations. Two-stage networks are studied in which a ...

Molecular dynamics (MD) simulations offer a promising avenue for understanding hydrate behavior and designing effective inhibition strategies. This review consolidates current knowledge on MD ...

Deep Learning, Personalized Pharmacotherapy, Pregnancy, Psychiatric Disorders, Electronic Health Records, Drug Efficacy Share and Cite: Filippis, R. and Foysal, A. (2025) Deep Learning for ...

When Sunset Park, Brooklyn residents compared both formats that visualized flooding, 92% preferred the dynamic 3D approach ... NOAA flood map versus a 3D simulation showing water rising to ...

In this work, we propose an active learning setting that incorporates an auction dynamics technique for the semi-supervised learning problem in a similarity graph-based framework. The experimental ...

Abstract: This paper proposes an automatic differential method to enable the effective Taylor-series based flexible integration algorithm for power electronics and electric machine systems simulation ...