ChemProt-3.0: a global chemical biology diseases mapping

By Jens Vindahl Kringelum, S. K. Kjærulff, S. Brunak, Ole Lund, Tudor I. Oprea, O. Taboureau. 2016

168 citations recorded by the source corpus.

ChemProt is a publicly available compilation of chemical-protein-disease annotation resources that enables the study of systems pharmacology for a small molecule across multiple layers of complexity from molecular to clinical levels. In this third version, ChemProt has been updated to more than 1.7 million compounds with 7.8 million bioactivity measurements for 19 504 proteins. Here, we report the implementation of global pharmacological heatmap, supporting a user-friendly navigation of chemogenomics space. This facilitates the visualization and selection of chemicals that share similar structural properties. In addition, the user has the possibility to search by compound, target, pathway, disease and clinical effect. Genetic variations associated to target proteins were integrated, making it possible to plan pharmacogenetic studies and to suggest human response variability to drug. Finally, Quantitative Structure–Activity Relationship models for 850 proteins having sufficient data were implemented, enabling secondary pharmacological profiling predictions from molecular structure. Database URL: http://potentia.cbs.dtu.dk/ChemProt/

Subfields: Computational Theory and Mathematics, Molecular Biology, Public Health, Environmental and Occupational Health

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Citations in the corpus

Don’t Stop Pretraining: Adapt Language Models to Domains and Tasks (2020, neutral): “Sources: CHEMPROT (Kringelum et al., 2016), RCT (Dernoncourt and Lee, 2017), ACL-ARC (Jurgens et al.”
ELLE: Efficient Lifelong Pre-training for Emerging Data (2022, methodology): “…we select a representative task that is relatively stable, i.e., MNLI (Williams et al., 2018), H YPER P ARTISAN (Kiesel et al., 2019), H ELPFULLNESS (McAuley et al., 2015), C HEM P ROT (Kringelum et al., 2016) and ACL-ARC (Jurgens et al., 2018) for WB, N S , R EV , B IO and CS, respectively.”
Lifelong Pretraining: Continually Adapting Language Models to Emerging Corpora (2021, background): “…also report the log perplexity of masked language modeling (MLM) in In contrast, the memory-replay based approach (ER) allows training the full parameters of the model and has been shown to be highly effective in continual learning of classiﬁcation tasks (Wang et al., 2019; Chaudhry et al., 2019).”
MSˆ2: Multi-Document Summarization of Medical Studies (2021, methodology): “…SPECTER (Cohan et al., 2020)), NLP tasks like NER (Nye et al., 2018; Li et al., 2016), relation extraction (Jain et al., 2020; Luan et al., 2018; Kringelum et al., 2016), QA (Abacha et al., 2019), NLI (Romanov and Shivade, 2018; Khot et al., 2018), summarization (Cachola et al., 2020;…”
Merging Models with Fisher-Weighted Averaging (2021, methodology): “Namely, we experimented with the ChemProt (Kringelum et al., 2016) relation classification task on the BioMed domain.”
Continual Pre-training of Language Models (2023, background): “Their corresponding end-task classiﬁcation datasets are: 10 Restaurant 11 , Phone (Ding et al., 2008; Hu & Liu, 2004), Camera (Ding et al., 2008; Hu & Liu, 2004), ACL (ACL-ARC in (Jurgens et al., 2018)), AI (SCIERC in (Luan et al., 2018)), and PubMed (CHEMPORT in (Kringelum et al., 2016)).”
Machine learning approaches and databases for prediction of drug–target interaction: a survey paper (2020, methodology): “ChemProt [253, 255, 256] was proposed as a disease chemical biology database that integrated data from multiple chemical– protein annotation databases and disease-associated PPI.”
Data Selection for Language Models via Importance Resampling (2023, unknown): “, 2018)), Biomedicine (ChemProt (Kringelum et al., 2016), RCT (Dernoncourt and Lee, 2017)) News (AGNews (Zhang et al.”
Improving language models fine-tuning with representation consistency targets (2022, methodology): “We also assembled non-GLUE benchmark from six distinct tasks which include biomedical relation extraction on CHEMPROT (Kringelum et al., 2016), sentiment classification on YELP (Zhang et al.”
Should We Be Pre-training? An Argument for End-task Aware Training as an Alternative (2021, methodology): “We choose CHEMPROT (Kringelum et al., 2016) as the classification task from the BIOMED domain.”