At the last OPIG meeting, I gave a talk about compound selectivity and machine learning approaching to predict whether a compound might be selective. As promised, I hereby provide a list publications I would hand to a beginner in the field of compound selectivity and machine learning.
Concept of compound selectivity (based on structural or ligand features)? Read these publication!
- Haupt, V. J., Daminelli, S. & Schroeder, M. Drug Promiscuity in PDB: Protein Binding Site Similarity Is Key. PLoS One 8, 1–15 (2013).
- Sturm, N., Desaphy, J., Quinn, R. J., Rognan, D. & Kellenberger, E. Structural insights into the molecular basis of the ligand promiscuity. J. Chem. Inf. Model. 52, 2410–2421 (2012).
- Hu, Y. & Bajorath, J. Compound promiscuity: What can we learn from current data? Drug Discov. Today 18, 644–650 (2013).
- Baell, J. & Walters, M. A. Chemistry: Chemical con artists foil drug discovery. Nature 513, 481–483 (2014).
More about ‘Chemical Probes’ or ‘Tool Compounds’, compounds which are highly selective towards a specific target? Read these publications!
- Arrowsmith, C. H. et al. The promise and peril of chemical probes. Nat. Chem. Biol. 11, 536–541 (2015).
- Wang, Y. et al. Evidence-Based and Quantitative Prioritization of Tool Compounds in Phenotypic Drug Discovery. Cell Chem. Biol. 23, 862–874 (2016).
- Butler, K. V, Macdonald, I. A., Hathaway, N. A. & Jin, J. Report and Application of a Tool Compound Data Set. J. Chem. Inf. Model 57, 2699−2706 (2017).
Machine Learning to determine compound selectivity? Read these publications!
- Sorgenfrei, F. A., Fulle, S. & Merget, B. Kinome-Wide Profiling Prediction of Small Molecules. ChemMedChem 1–6 (2017).
- Giblin, K., Hughes, S., Boyd, H., Hansson, P. & Bender, A. Prospectively Validated Proteochemometric Models for the Prediction of Small Molecule Binding to Bromodomain Proteins. J. Chem. Inf. Model. XXXX, XXX, XXX-XXX acs.jcim.8b00400 (2018).
- Subramanian, V., Prusis, P., Pietilä, L. O., Xhaard, H. & Wohlfahrt, G. Visually interpretable models of kinase selectivity related features derived from field-based proteochemometrics. J. Chem. Inf. Model. 53, 3021–3030 (2013).
Interested in more machine learning as an extension to the publications above? These publications might be a good starting point…
- Cortés-Ciriano, I. et al. Polypharmacology modelling using proteochemometrics (PCM): recent methodological developments, applications to target families, and future prospects. Med. Chem. Commun. 6, 24–50 (2015).
- Krstajic, D., Buturovic, L. J., Leahy, D. E. & Thomas, S. Cross-validation pitfalls when selecting and assessing regression and classification models. J. Cheminform. 6, 1–15 (2014).
- Sun, J. et al. Applying Mondrian Cross-Conformal Prediction to Estimate Prediction Confidence on Large Imbalanced Bioactivity Data Sets. J. Chem. Inf. Model. 57, 1591–1598 (2017).