Benford's Law in Medicinal Chemistry: Implications for Drug Design

Abstract

Aim: The explosion of data based technology has accelerated pattern mining. However, it is clear that quality and bias of data impacts all machine learning and modeling. Results & methodology: A technique is presented for using the distribution of first significant digits of medicinal chemistry features: logP, logS, and pK_a. experimental and predicted, to assess their following of Benford's law as seen in many natural phenomena. Conclusion: Quality of data depends on the dataset sizes, diversity, and magnitudes. Profiling based on drugs may be too small or narrow; using larger sets of experimentally determined or predicted values recovers the distribution seen in other natural phenomena. This technique may be used to improve profiling, machine learning, large dataset assessment and other data based methods for better (automated) data generation and designing compounds.

Plain Language Summary

Lay abstract Machine learning and other technology depends critically on quality of data

Benford's law can indicate data follows natural phenomena easy, fast, statistical

Drug design impacted by FSD of experiment and predicted logP, pKa, solubility distributions

Method suited for large datasets

Keywords: :

• bias
• chemical library
• data science
• distribution
• drug design
• drug discovery
• drug likeness
• filters
• machine learning

Supplementary data

To view the supplementary data that accompany this paper please visit the journal website at:www.tandfonline.com/doi/full/10.2217/epi-2016-0184

Financial & competing interests disclosure

A.T.G.-S. thanks Haridus- ja Teadusministeerium for grant no. IUT34-14. The authors have no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed.

No writing assistance was utilized in the production of this manuscript.