Because undesirable pharmacokinetics and toxicity are significant reasons for the failure of drug development in the costly late stage, it has been widely recognized that drug ADMET properties should be considered as early as possible to reduce failure rates in the clinical phase of drug discovery. Concurrently, drug recalls have become increasingly common in recent years, prompting pharmaceutical companies to increase attention toward the safety evaluation of preclinical drugs. In vitro and in vivo drug evaluation techniques are currently more mature in preclinical applications, but these technologies are costly. In recent years, with the rapid development of computer science, in silico technology has been widely used to evaluate the relevant properties of drugs in the preclinical stage and has produced many software programs and in silico models, further promoting the study of ADMET in vitro. In this review, we first introduce the two ADMET prediction categories (molecular modeling and data modeling). Then, we perform a systematic classification and description of the databases and software commonly used for ADMET prediction. We focus on some widely studied ADMT properties as well as PBPK simulation, and we list some applications that are related to the prediction categories and web tools. Finally, we discuss challenges and limitations in the preclinical area and propose some suggestions and prospects for the future.

Background: Valproic acid (VPA) is recommended as a first-line treatment for children with epilepsy. GABRG2 polymorphism is found to be associated with epilepsy susceptibility and therapeutic response of anti-seizure medications (ASM); however, the role of GABRG2 in VPA treatment still remains unknown. Objective: The purpose of this study was to explore the association of GABRG2 gene polymorphism with the drug response and adverse drug reactions (ADRs) related to VPA. Methods: A retrospective study including 96 Chinese children with epilepsy treated by VPA was carried out. The ADRs were collected during VPA therapy and GABRG2 rs211037 in enrolled patients was genotyped using Sequenom MassArray system. A network pharmacological analysis involved protein–protein interaction and enrichment analysis was constructed to investigate the potential targets and pathways of GABRG2 on VPA-related ADRs. Results: Among 96 patients, 41 individuals were defined as seizure together with 49 patients with seizure-free and 6 patients unclassified. Carriers of homozygote GABRG2 rs211037 CC genotype exhibited seizure-free to VPA (P = 0.042), whereas those with CT genotype showed seizure. Furthermore, CC genotype had predisposition to digestive ADRs (P = 0.037) but was a protective factor for VPA-associated weight gain (P = 0.013). Ten key genes related to digestive ADRs and weight gain induced by VPA were identified by network pharmacological analysis and mainly involved in “GABAergic synaptic signaling”, “GABA receptor signaling”, and “taste transduction” pathways/processes through enrichment analysis. Conclusion: This study revealed that GABRG2 variation exerted a predictable role in the efficacy and safety of VPA treatment for Chinese children with epilepsy.