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 pKa. 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.

journal_name

Future Med Chem

authors

García-Sosa AT

doi

10.4155/fmc-2019-0006

subject

Has Abstract

pub_date

2019-09-01 00:00:00

pages

2247-2253

issue

17

eissn

1756-8919

issn

1756-8927

journal_volume

11

pub_type

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