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.
Computer scientists have studied artificial intelligence and machine learning since computers were invented. There have been three waves of developments, in this field. First development, in the 1950s, computer chess and reversiTM, were realized. In the second development, “expert systems” attracted attention and started the “5th Generation Computer Project.” in 1980s. However, the results of both the developments were insufficient (for example, 5th Generation Computer was not created), and the enthusiasm for expecting new type of computers such as artificial intelligence quickly diminished. We are now in the third wave of the AI developments. Will it produce satisfactory results? Will it revolutionize life sciences? In this article, I have tried to answer these questions.
The fragment molecular orbital (FMO) method enables quantum mechanical calculations for macromolecules by dividing the target into fragments. However, most calculations, even for metalloproteins, have been performed by removing metal ions from the structures registered in the Protein Data Bank (PDB). For more realistic and useful calculations, FMO calculations must be performed without removing the metal ions. In this study, we discuss the results obtained from FMO calculations performed using 6-31G* and model core potentials (MCPs) for metal proteins containing Zn and Mg ions. Subsequently, we analyze the differences in atomic charges and interactions.