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

Benford's Law and distributions for better drug design

INTRODUCTION

Modern drug discovery incorporates various tools and data, heralding the beginning of the data-driven drug design (DD) era. The distributions of chemical and physical data used for Artificial Intelligence (AI)/Machine Learning (ML) and to drive DD have thus become highly important to be understood and used effectively.

AREAS COVERED

The authors perform a comprehensive exploration of the statistical distributions driving the data-intensive era of drug discovery, including Benford's Law in AI/ML-based DD.

EXPERT OPINION

As the relevance of data-driven discovery escalates, we anticipate meticulous scrutiny of datasets utilizing principles like Benford's Law to enhance data integrity and guide efficient resource allocation and experimental planning. In this data-driven era of the pharmaceutical and medical industries, addressing critical aspects such as bias mitigation, algorithm effectiveness, data stewardship, effects, and fraud prevention are essential. Harnessing Benford's Law and other distributions and statistical tests in DD provides a potent strategy to detect data anomalies, fill data gaps, and enhance dataset quality. Benford's Law is a fast method for data integrity and quality of datasets, the backbone of AI/ML and other modeling approaches, proving very useful in the design process.

Androgen Receptor Binding Category Prediction with Deep Neural Networks and Structure-, Ligand-, and Statistically Based Features

Substances that can modify the androgen receptor pathway in humans and animals are entering the environment and food chain with the proven ability to disrupt hormonal systems and leading to toxicity and adverse effects on reproduction, brain development, and prostate cancer, among others. State-of-the-art databases with experimental data of human, chimp, and rat effects by chemicals have been used to build machine-learning classifiers and regressors and to evaluate these on independent sets. Different featurizations, algorithms, and protein structures lead to different results, with deep neural networks (DNNs) on user-defined physicochemically relevant features developed for this work outperforming graph convolutional, random forest, and large featurizations. The results show that these user-provided structure-, ligand-, and statistically based features and specific DNNs provided the best results as determined by AUC (0.87), MCC (0.47), and other metrics and by their interpretability and chemical meaning of the descriptors/features. In addition, the same features in the DNN method performed better than in a multivariate logistic model: validation MCC = 0.468 and training MCC = 0.868 for the present work compared to evaluation set MCC = 0.2036 and training set MCC = 0.5364 for the multivariate logistic regression on the full, unbalanced set. Techniques of this type may improve AR and toxicity description and prediction, improving assessment and design of compounds. Source code and data are available on github.

Benford's law and metabolomics: A tale of numbers and blood

The Newcomb-Benford law - also known as the "law of anomalous numbers" or, more commonly, Benford's law - predicts that the distribution of the first significant digit of random numbers obtained from mixed probability distributions follows a predictable pattern and reveals some universal behavior. Specifically, given a dataset of empirical measures, the likelihood of the first digit of any number being 1 is ∼30 %, ∼18 % for 2, 12.5 % for 3 and so on, with a decreasing probability all the way to number 9. If the digits were distributed uniformly, all the numbers 1 through 9 would have the same probability to appear as the first digit in any given empirical random measurement. However, this is not the case, as this law defies common sense and seems to apply seamlessly to large data. The use of omics technologies and, in particular, metabolomics has generated a wealth of big data in the field of transfusion medicine. In the present meta-analysis, we focused on previous big data from metabolomics studies of relevance to transfusion medicine: one on the quality of stored red blood cells, one on the phenotypes of transfusion recipients, i.e. trauma patients suffering from trauma and hemorrhage, and one of relevance to the 2020 SARS-COV-2 global pandemic. We show that metabolomics data follow a Benford's law distribution, an observation that could be relevant for future application of the "law of anomalous numbers" in the field of quality control processes in transfusion medicine.