ArticleProceedings of the National Academy of Sciences of the United States of America2019
Ligand biological activity predicted by cleaning positive and negative chemical correlations.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
What it found
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
13 citing papers in PubMed, 39 citations in OpenAlex.
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- AI-based identification of therapeutic agents targeting GPCRs: introducing ligand type classifiers and systems biology.Chemical science · 2023Article
- Discovering NDM-1 inhibitors using molecular substructure embeddings representations.Journal of integrative bioinformatics · 2023Article
- Machine learning approaches for biomolecular, biophysical, and biomaterials research.Biophysics reviews · 2022Review
- DeepReac+: deep active learning for quantitative modeling of organic chemical reactions.Chemical science · 2021Article
- Ab Initio Machine Learning in Chemical Compound Space.Chemical reviews · 2021Review
- Article
- Exploring chemical compound space with quantum-based machine learning.Nature reviews. Chemistry · 2020Review
- Artificial Intelligence in Drug Discovery: A Comprehensive Review of Data-driven and Machine Learning Approaches.Biotechnology and bioprocess engineering : BBE · 2020Review
- Analyzing Learned Molecular Representations for Property Prediction.Journal of chemical information and modeling · 2019Article
- Discovery of Selective M4 Muscarinic Acetylcholine Receptor Agonists with Novel Carbamate Isosteres.ACS medicinal chemistry letters · 2019Article
- Deep Learning for Deep Chemistry: Optimizing the Prediction of Chemical Patterns.Frontiers in chemistry · 2019Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Predicting ligand biological activity is a key challenge in drug discovery. Ligand-based statistical approaches are often hampered by noise due to undersampling: The number of molecules known to be active or inactive is vastly less than the number of possible chemical features that might determine binding. We derive a statistical framework inspired by random matrix theory and combine the framework with high-quality negative data to discover important chemical differences between active and inactive molecules by disentangling undersampling noise. Our model outperforms standard benchmarks when tested against a set of challenging retrospective tests. We prospectively apply our model to the human muscarinic acetylcholine receptor M1, finding four experimentally confirmed agonists that are chemically dissimilar to all known ligands. The hit rate of our model is significantly higher than the state of the art. Our model can be interpreted and visualized to offer chemical insights about the molecular motifs that are synergistic or antagonistic to M1 agonism, which we have prospectively experimentally verified.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.