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drug repurposing

4 articles · Page 1 of 1
HyperAI Super Neural
HyperAI Super Neural
Jun 1, 2026 · Artificial Intelligence

AI‑Computational Chemistry Workflow Cuts Diabetic Wound‑Healing Drug R&D Time by 70%

Singapore’s National University of Singapore presents an AI‑computational chemistry (AI‑CC) pipeline that integrates large‑language‑model literature mining with multi‑stage molecular simulations, enabling a closed‑loop analysis of drug‑protein nanoscale interactions, accelerating diabetic wound‑healing drug repurposing and shortening the development cycle by more than 70%.

AI‑CCcomputational chemistrydiabetic wound healing
0 likes · 12 min read
AI‑Computational Chemistry Workflow Cuts Diabetic Wound‑Healing Drug R&D Time by 70%
Data Party THU
Data Party THU
May 27, 2026 · Artificial Intelligence

AI Scientific Assistants Rise: Google’s Co‑Scientist and FutureHouse’s Robin

Two groundbreaking Nature papers introduce Google DeepMind’s multi‑agent Co‑Scientist and FutureHouse’s Robin, AI systems that combine literature search, hypothesis generation, experimental design and data analysis to accelerate drug repurposing for leukemia and age‑related macular degeneration, demonstrating how AI is evolving from a tool into a collaborative scientific partner.

AIDeepMindFutureHouse
0 likes · 8 min read
AI Scientific Assistants Rise: Google’s Co‑Scientist and FutureHouse’s Robin
DataFunTalk
DataFunTalk
Dec 25, 2022 · Artificial Intelligence

GraphSynergy: A Network‑Inspired Deep Learning Model for Predicting Anti‑Cancer Drug Combinations

GraphSynergy integrates network science and graph convolutional networks to predict synergistic anti‑cancer drug combinations by modeling protein‑protein interaction networks, computing therapy and toxicity scores, and outperforming baseline methods on DrugCombDB and Oncology‑Screen datasets, while offering interpretable mechanisms for drug repurposing.

AI drug discoveryGraph Neural Networkscancer therapy
0 likes · 19 min read
GraphSynergy: A Network‑Inspired Deep Learning Model for Predicting Anti‑Cancer Drug Combinations