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PubChem for drug discovery in the age of big data and artificial intelligence
- 1. PubChem for Drug Discovery in the Age of Big Data and Artificial Intelligence Sunghwan Kim, Ph.D., M.Sc.
- 2. 1. What is PubChem?
- 3. ▪ https://pubchem.ncbi.nlm.nih.gov ▪ Public chemical chemical database at NIH. ▪ Contains information on various chemical entities: • Small molecules • siRNAs & miRNAs • Carbohydrates • Lipids • Peptides • Chemically modified macromolecules • …… PubChem is a Public Chemical Information Resource
- 4. PubChem is a Data Aggregator PubChem Sources: https://pubchem.ncbi.nlm.nih.gov/sources Gov’t agencies Academic institutions Publishers Pharma companies Chemical vendors Scientific databases 790+ Data sources Users o Biomedical Researchers • Chemical biology • Medicinal chemistry • Drug design & discovery • Cheminformatics o Data scientists o Patent agents/examiners o Chemical safety officers o Educators/librarians o Students
- 5. 0 1 2 3 4 5 6 Unique Monthly Users (millions) Time Monthly Usage Statistics (Interactive Users Only) Source: Google Analytics ▪ 5 million unique interactive users per month at peak (Oct. 2021) ▪ Programmatic requests are not included. ▪ These statistics are lower-bound.
- 6. 2. What does PubChem have?
- 7. PubChem Data Content Structures and properties
- 8. Structures and properties Spectra PubChem Data Content
- 9. Structures and properties Spectra Chemical health & safety PubChem Data Content
- 10. Structures and properties Spectra Chemical health & safety Bioactivity PubChem Data Content
- 11. Structures and properties Spectra Chemical health & safety Bioactivity Chemical vendors & synthesis PubChem Data Content
- 13. Clinical trials Drugs PubChem Data Content
- 14. Clinical trials Patents Drugs PubChem Data Content
- 15. Clinical trials Patents Drugs Scientific articles PubChem Data Content
- 34. 34
- 35. Gene/Protein Target Page ➢ Suppose that you want to: o Retrieve ALL active compounds against a given protein/gene target (e.g., HMGCR=3-hydroxy-3-methylglutaryl-CoA reductase). • To identify common chemical scaffolds responsible for bioactivity. • To build a quantitative structure-activity relationship (QSAR) model. →Gene/Protein Target page • Provides a target-centric view of PubChem data. • Organizes all data available in PubChem for a given gene/protein.
- 43. Patent View Page ➢ Suppose that you want to: o Retrieve ALL chemicals mentioned in a given patent document. →Patent View page • Provides a list of chemicals “mentioned” in the patent application/grant. • No information on why they are mentioned. (e.g., as a subject matter or as a prior art?) • Other information, including: - Title, abstract, date, inventor, … - International patent classification (IPC) codes
- 50. 4. Programmatic Access to PubChem
- 51. ➢ PubChem users have very diverse backgrounds/interests. ➢ PubChem’s web interfaces are optimized to perform commonly requested tasks interactively.
- 52. ➢ PubChem users have very diverse backgrounds/interests. ➢ PubChem’s web interfaces are optimized to perform commonly requested tasks interactively. ➢ Everything you can do with PubChem through the web browser can be automated through PubChem’s programmatic interfaces.
- 53. ➢ PubChem users have very diverse backgrounds/interests. ➢ PubChem’s web interfaces are optimized to perform commonly requested tasks interactively. ➢ Everything you can do with PubChem through the web browser can be automated through PubChem’s programmatic interfaces. ➢ Programmatic access enables one to do much more complicated tasks that cannot be done through the web browser.
- 54. ➢ Multiple programmatic access routes ➢ Two major programmatic access methods o PUG-REST (primarily for computed properties). https://pubchemdocs.ncbi.nlm.nih.gov/pug-rest o PUG-View (primarily for text information). https://pubchemdocs.ncbi.nlm.nih.gov/pug-view ➢ Request volume limitation: o No more than 5 requests per second (See more at: https://pubchemdocs.ncbi.nlm.nih.gov/programmatic- access$_RequestVolumeLimitations) o Violators/abusers may be blocked for a certain period of time. Entrez Utilites (E-Utils) Power User Gateway (PUG) PUG-SOAP PUG-REST PubChem RDF REST PUG_View
- 55. ➢ Bulk Download o PubChem FTP Site ftp://ftp.ncbi.nlm.nih.gov/pubchem o PubChem RDF (Resource Description Network) https://pubchemdocs.ncbi.nlm.nih.gov/rdf
- 56. 5. Showcase: Bioactivity Prediction Model Building with PubChem Data
- 57. ▪ Involved in regulation of gene expression in various biological processes. ▪ Potential roles in: • metabolic signaling pathways • skin alopecia (spot baldness) • dermal cysts • cardiac development • insulin sensitization • …… ▪ Let’s build binary classifiers (i.e, active vs. inactive) for chemical modulators of RXRA Retinoid X Receptor (RXRA) PDB ID: 1FBY
- 58. Tox21 (AID 1159531) • Quantitative HTS (qHTS) data for 10K compounds • Predominantly inactive Data sets
- 59. Tox21 (AID 1159531) Training (4916 compounds) Test (547 compounds) • 471 actives • 4,445 inactives • 53 actives • 494 inactives Preprocessing • Quantitative HTS (qHTS) data for 10K compounds • Predominantly inactive 90% 10% Data sets
- 60. Tox21 (AID 1159531) ChEMBL (45 Assays) NCGC (2 Assays) Training (4916 compounds) Test (547 compounds) External 1 (222 compounds) External 2 (489 compounds) • 471 actives • 4,445 inactives • 53 actives • 494 inactives • 205 actives • 17 inactives • 20 actives • 469 inactives Preprocessing • Quantitative HTS (qHTS) data for 10K compounds • Predominantly inactive • Data extracted from journal articles • Predominantly active • qHTS data • Predominantly inactive • Some overlap w/ Tox21 Preprocessing Preprocessing 90% 10% Data sets
- 61. Tox21 (AID 1159531) ChEMBL (45 Assays) NCGC (2 Assays) Training (4916 compounds) Test (547 compounds) External 1 (222 compounds) External 2 (489 compounds) • 471 actives • 4,445 inactives • 53 actives • 494 inactives • 205 actives • 17 inactives • 20 actives • 469 inactives Preprocessing • Quantitative HTS (qHTS) data for 10K compounds • Predominantly inactive • Data extracted from journal articles • Predominantly active • qHTS data • Predominantly inactive • Some overlap w/ Tox21 Preprocessing Preprocessing 90% 10% Data sets All data Available in PubChem.
- 62. Tox21 (AID 1159531) ChEMBL (45 Assays) NCGC (2 Assays) Training (4916 compounds) Test (547 compounds) External 1 (222 compounds) External 2 (489 compounds) • 471 actives • 4,445 inactives • 53 actives • 494 inactives • 205 actives • 17 inactives • 20 actives • 469 inactives Preprocessing • Quantitative HTS (qHTS) data for 10K compounds • Predominantly inactive • Data extracted from journal articles • Predominantly active • qHTS data • Predominantly inactive • Some overlap w/ Tox21 Preprocessing Preprocessing 90% 10% Data sets
- 63. Tox21 (AID 1159531) ChEMBL (45 Assays) NCGC (2 Assays) Training (4916 compounds) Test (547 compounds) External 1 (222 compounds) External 2 (489 compounds) • 471 actives • 4,445 inactives • 53 actives • 494 inactives • 205 actives • 17 inactives • 20 actives • 469 inactives Preprocessing • Quantitative HTS (qHTS) data for 10K compounds • Predominantly inactive • Data extracted from journal articles • Predominantly active • qHTS data • Predominantly inactive • Some overlap w/ Tox21 Preprocessing Preprocessing 90% 10% 471 Data sets
- 64. ❑ Molecular descriptors • Generated using PaDEL [Yap CW (2011). J. Comput. Chem., 32 (7): 1466-1474] Model Building Abbreviation Name Length AP AtomPairs 2D Fingerprint 780 ESTAT Estate fingerprint 79 EXTFP* CDK Extended Fingerprint 1,024 FP* CDK fingerprint 1,024 GOFP* CDK graph only fingerprint 1,024 KR Klekota-Roth fingerprint 4,860 MACCS MACCS fingerprint 166 PUB PubChem fingerprint 881 SUB Substructure fingerprint 307 * Hashed fingerprints
- 65. ❑ Machine-learning algorithms (implemented in scikit-learn) Abbreviation Name Hyperparameters optimized NB Naï ve Bayes (10-10 ~ 1) DT Decision tree max_depth_range (3 ~ 7) min_samples_split_range (3 ~ 7) min_samples_leaf_range (2 ~ 6) kNN K-Nearest neighbors weights (uniform, minkowski, jaccard) n_neighbors (1 ~ 25) RF Random forest n_estimators (10 ~ 200) SVM Support vector machine C ( 2-10 ~ 210); ( 2-10 210) NN Neural network solver (lbfgs or adam); (10-7 107) ▪ 10-fold cross-validation was used for hyperparameter optimization. Model Building
- 66. Model Performance Evaluation ▪ Area under the Receiver operating characteristic curve (AUC) → Used for hyperparameter optimization. ▪ 𝐵𝑎𝑙𝑎𝑛𝑐𝑒𝑑 𝐴𝑐𝑐𝑢𝑟𝑎𝑐𝑦 𝐵𝐴𝐶𝐶 = 1 2 𝑇𝑃 𝑇𝑃 + 𝐹𝑁 + 𝑇𝑁 𝑇𝑁 + 𝐹𝑃 = 1 2 𝑆𝐸𝑁𝑆 + 𝑆𝑃𝐸𝐶 ▪ 𝑆𝑒𝑛𝑠𝑖𝑡𝑖𝑣𝑖𝑡𝑦 (𝑆𝐸𝑁𝑆) = 𝑇𝑃 𝑇𝑃+𝐹𝑁 ▪ 𝑆𝑝𝑒𝑐𝑖𝑓𝑖𝑐𝑖𝑡𝑦 𝑆𝑃𝐸𝐶 = 𝑇𝑁 𝑇𝑁+𝐹𝑃
- 67. ❑ Performance of the models ▪ AUC scores of 0.7 were observed for models developed using: PubChem/MACCS/CDK-FP with NN/SVM/RF/kNN ▪ Maximum AUC score (0.77): PubChem fingerprint with RF ▪ Similar trend was observed for the performance in terms of BACC scores (not shown here). Area under ROC curve (AUC) Model Performance Evaluation
- 68. Area under ROC curve (AUC), Inactive-to-active ratio = 1 NCGC ChEMBL General applicability of the models
- 69. • PubChem is the largest source of publicly available chemical information, collected from hundreds of data sources. • In addition to bioactivity data generated through high-throughput screenings, PubChem contains a substantial amount of bioactivity information extracted from scientific articles. • Chemical vendor and patent information for compounds in PubChem helps prioritize hit compounds for further screening. Summary
- 70. • PubChem supports programmatic access to its data, allowing for building an automated virtual screening pipeline. • PubChemRDF allows users to download PubChem data on a local computing facility and integrate them with their own data. • PubChem data can be used for developing computational prediction models for bioactivity or toxicity of molecules. Summary
- 71. Acknowledgements ▪ The PubChem Team ▪ Funding Evan Bolton Jia He Thiessen Paul Zhi Sun Jie Chen Siqian He Bo Yu Tiejun Chung Qingliang Li Leonid Zaslavsky Asta Gindulyte Ben Shoemaker Jian Zhang Intramural Research Program of the National Library of Medicine
- 72. Thank you for your attention. Questions? Sunghwan Kim (kimsungh@ncbi.nlm.nih.gov)
- 73. ❑ References ▪ Getting the most out of PubChem for virtual screening S. Kim, Expert Opin. Drug Discov. 2016, 11(9), 843-855. ▪ PubChem in 2021: new data content and improved web interfaces S. Kim et al., Nucleic Acids Res. 2021, 49(D1):D1388–D1395. ▪ An update on PUG-REST: RESTful interface for programmatic access to PubChem S. Kim et al., Nucleic Acids Res. 2018, 46(W1):W563-W570. ▪ PUG-SOAP and PUG-REST: web services for programmatic access to chemical information in PubChem S. Kim et al., Nucleic Acids Res. 2015, 43(W1):W605-W611.