Difficulties and prospects of data curation for ADME in silico modeling

Abstract

The cost and time required for drug discovery must be reduced. Recent in silico models have focused on accelerating seed compound discovery based solely on chemical structure. Estimating pharmacokinetic characteristics, including absorption, distribution, metabolism, and excretion (ADME), is essential in the early stage of drug discovery. Therefore, in silico models have used artificial intelligence (AI) techniques to predict the ADME properties of potential compounds. Large experimental data are necessary when constructing in silico models for ADME prediction. However, it remains difficult for one pharmaceutical company or academic laboratory to collect enough data for modeling. Therefore, collecting data from open databases with the assistance of dry scientists is one of the most effective strategies utilized by researchers. However, incorrect values are occasionally included in open databases because of human errors. Furthermore, to construct high-performance ADME in silico models, data curation must include not only chemical structure but also experimental conditions, which requires expert knowledge of pharmacokinetic experiments. Trials to ease the difficulties of data curation have been developed as reported. These tools enable the effective collection and checking of published data. Additionally, they accelerate collaboration between dry and wet scientists, enabling them to collect vast amounts of data to construct high-performance and widespread chemical space ADME in silico models. Collecting much accurate data for constructing ADME in silico models is an expectation of the new era of efficient drug discovery when entirely using AI technology.