ChEMBL
PulseAugur coverage of ChEMBL — every cluster mentioning ChEMBL across labs, papers, and developer communities, ranked by signal.
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AI Co-Scientist Workflow Targets EGFR Inhibitor Resistance
Researchers have developed an AI co-scientist workflow to discover new EGFR inhibitors, specifically targeting the C797S mutation that causes resistance to existing treatments. The process involves using ChEMBL and UniP…
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New AI pipeline ensures factual accuracy against external authorities
A new pipeline has been developed to address the limitations of AI extractors in regulated domains, particularly in pharmaceuticals. Unlike typical systems that focus on matching extracted facts to source text, this app…
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Genetic evidence strongly linked to drug approval rates, study finds
A new observational analysis of 26,278 target-disease pairs has found that drug targets with genetic associations show a significantly higher approval rate compared to those without. The study, which utilized data from …
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GraphDETR framework tackles subgraph detection as set prediction
Researchers have developed GraphDETR, a novel deep learning framework for end-to-end subgraph detection. This approach treats subgraph detection as a set prediction problem, similar to object detection in images, using …
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GNNs Fall Short in Predicting Drug Toxicity, Study Finds
A new study published on arXiv explores the limitations of Graph Neural Networks (GNNs) in predicting drug toxicity, specifically focusing on acetylsalicylic acid (Aspirin). The research found that molecular structure a…
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Machine learning models predict Alzheimer's drug candidates from natural compounds
Researchers have developed a machine learning approach to identify potential Alzheimer's disease treatments from natural compounds. The study utilized cheminformatics to extract molecular descriptors and trained various…