Benford's law in medicinal chemistry: Implications for drug design

2019 ◽  
Vol 11 (17) ◽  
pp. 2247-2253 ◽  
Author(s):  
Alfonso T García-Sosa
Keyword(s):  
Machine Learning ◽  
Medicinal Chemistry ◽  
Pattern Mining ◽  
Benford’S Law ◽  
Log P ◽  
Data Generation ◽  
Natural Phenomena ◽  
Quality Of Data ◽  
Benford's Law ◽  
Predicted Values

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: log P, log S, and p Ka. 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.

Applications of Benford's Law

2015 ◽  
Author(s):  
Arno Berger ◽  
Theodore P. Hill
Keyword(s):  
Computer Science ◽  
Cross Section ◽  
Ad Hoc ◽  
Fraud Detection ◽  
Benford’S Law ◽  
Natural Phenomena ◽  
Pedagogical Tool ◽  
Practical Applications ◽  
Benford's Law ◽  
Main Ideas

This chapter provides a overview of the practical applications of Benford's law. These include fraud detection, detection of natural phenomena, diagnostics and design, computations and computer science, and as a pedagogical tool. In contrast to the rest of the book, this chapter is necessarily expository and informal. It has been organized into a handful of ad hoc categories, which the authors hope will help illuminate the main ideas. None of the conclusions of the experiments or data presented here have been scrutinized or verified by the authors of this book, since the intent here is not to promote or critique any specific application. Rather the goal is to offer a representative cross-section of the related scientific literature, in the hopes that this might continue to facilitate research in both the theory and practical applications of Benford's law.

Application of Benfordʼs Law to Images

Benford's Law ◽  
2015 ◽  
Author(s):  
Fernando Pérez-González ◽  
Tu-Thach Quach ◽  
Chaouki T. Abdallah ◽  
Gregory L. Heileman ◽  
Steven J. Miller
Keyword(s):  
Visual System ◽  
Human Visual System ◽  
Digital Camera ◽  
Benford’S Law ◽  
Natural Phenomena ◽  
Image Capture ◽  
Image Forensic ◽  
Benford's Law

This chapter analyzes the application of Benford's law to pictures taken from nature with a digital camera. Considering that many natural phenomena seem to follow Benford's law and that images are often nothing but “snapshots of nature,” it is pertinent to wonder whether images (at least those taken from nature) obey Benford's law. While the values output by the image capture device embedded in the camera, i.e., the pixels, do not follow Benford's law, this chapter shows that if they are transformed into a domain that better approximates the human visual system then the resulting values satisfy a generalized form of Benford's law. This can be used for image forensic applications, such as detecting whether an image has been modified to carry a hidden message (steganography) or has been compressed with some loss of quality.

scholarly journals Can we rely on COVID-19 data? An assessment of data from over 200 countries worldwide

2021 ◽  
Vol 104 (2) ◽  
pp. 003685042110212
Author(s):  
Noah Farhadi
Keyword(s):  
The United States ◽  
Reliable Data ◽  
Benford’S Law ◽  
Quality Of Data ◽  
Local Authorities ◽  
Strong Indicator ◽  
The World ◽  
Benford's Law ◽  
Islamic Republic Of Iran ◽  
Increasing Demand

To fight COVID-19, global access to reliable data is vital. Given the rapid acceleration of new cases and the common sense of global urgency, COVID-19 is subject to thorough measurement on a country-by-country basis. The world is witnessing an increasing demand for reliable data and impactful information on the novel disease. Can we trust the data on the COVID-19 spread worldwide? This study aims to assess the reliability of COVID-19 global data as disclosed by local authorities in 202 countries. It is commonly accepted that the frequency distribution of leading digits of COVID-19 data shall comply with Benford’s law. In this context, the author collected and statistically assessed 106,274 records of daily infections, deaths, and tests around the world. The analysis of worldwide data suggests good agreement between theory and reported incidents. Approximately 69% of countries worldwide show some deviations from Benford’s law. The author found that records of daily infections, deaths, and tests from 28% of countries adhered well to the anticipated frequency of first digits. By contrast, six countries disclosed pandemic data that do not comply with the first-digit law. With over 82 million citizens, Germany publishes the most reliable records on the COVID-19 spread. In contrast, the Islamic Republic of Iran provides by far the most non-compliant data. The author concludes that inconsistencies with Benford’s law might be a strong indicator of artificially fabricated data on the spread of SARS-CoV-2 by local authorities. Partially consistent with prior research, the United States, Germany, France, Australia, Japan, and China reveal data that satisfies Benford’s law. Unification of reporting procedures and policies globally could improve the quality of data and thus the fight against the deadly virus.

scholarly journals Are COVID-19 Data Reliable? A Quantitative Analysis of Pandemic Data from 182 Countries

COVID ◽  
2021 ◽  
Vol 1 (1) ◽  
pp. 137-152
Author(s):  
Noah Farhadi ◽  
Hooshang Lahooti
Keyword(s):  
Reliable Data ◽  
Benford’S Law ◽  
Global Health Security ◽  
Quality Of Data ◽  
Health Security ◽  
The Past ◽  
Six Nations ◽  
Benford's Law ◽  
The Uk

When it comes to COVID-19, access to reliable data is vital. It is crucial for the scientific community to use data reported by independent territories worldwide. This study evaluates the reliability of the pandemic data disclosed by 182 countries worldwide. We collected and assessed conformity of COVID-19 daily infections, deaths, tests, and vaccinations with Benford’s law since the beginning of the coronavirus pandemic. It is commonly accepted that the frequency of leading digits of the pandemic data shall conform to Benford’s law. Our analysis of Benfordness elicits that most countries partially distributed reliable data over the past eighteen months. Notably, the UK, Australia, Spain, Israel, and Germany, followed by 22 different nations, provided the most reliable COVID-19 data within the same period. In contrast, twenty-six nations, including Tajikistan, Belarus, Bangladesh, and Myanmar, published less reliable data on the coronavirus spread. In this context, over 31% of countries worldwide seem to have improved reliability. Our measurement of Benfordness moderately correlates with Johns Hopkin’s Global Health Security Index, suggesting that the quality of data may depend on national healthcare policies and systems. We conclude that economically or politically distressed societies have declined in conformity to the law over time. Our results are particularly relevant for policymakers worldwide.

scholarly journals Potential of Benford's Law and Machine Learning Based Verification in Agricultural Logistics

2020 ◽  
Author(s):  
Stanislav Levičar
Keyword(s):  
Machine Learning ◽  
Supply Chains ◽  
General Purpose ◽  
Third Parties ◽  
Machine Learning Algorithms ◽  
Benford’S Law ◽  
Food Supply Chains ◽  
Business Operations ◽  
Verification Tool ◽  
Benford's Law

Food supply chains are becoming increasingly more complex, contributing to emergence of new threats and risks for the involved stakeholders. Additionally, the information technology accelerated development of new and more productive ways of collaboration among organizations (members of supply chains) and helped to optimize their processes. Tighter collaboration among those companies is only possible if sufficient level of trust is established among them, which is often an obstacle that is not easily overcome. Since individual companies (which are part of supply chain) are unable to verify and rely on the data that is provided by third parties, the potential advantages are not fully realized. In this article we try to identify a possibility to remove one important element of this obstacle by using Benford’s law as the basis for general-purpose verification tool that is additionally enhanced by statistics based methods of machine learning algorithms that can be implemented in IT supported business operations. The potential usefullness of those methods lies in the fact that they are able to identify the patterns and correlations without explicit users’ input.

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