Absorption Distribution Metabolism Excretion and Toxicity Property Prediction Utilizing a Pre-Trained Natural Language Processing Model and Its Applications in Early-Stage Drug Development.

Machine learning techniques are extensively employed in drug discovery, with a significant focus on developing QSAR models that interpret the structural information of potential drugs. In this study, the pre-trained natural language processing (NLP) model, ChemBERTa, was utilized in the drug discovery process. We proposed and evaluated four core model architectures as follows: deep neural network (DNN), encoder, concatenation (concat), and pipe.

Fine-tuning of BERT Model to Accurately Predict Drug-Target Interactions.

The identification of optimal drug candidates is very important in drug discovery. Researchers in biology and computational sciences have sought to use machine learning (ML) to efficiently predict drug-target interactions (DTIs). In recent years, according to the emerging usefulness of pretrained models in natural language process (NLPs), pretrained models are being developed for chemical compounds and target proteins.

Dose Optimization of Vancomycin Using a Mechanism-based Exposure-Response Model in Pediatric Infectious Disease Patients.

Purpose: This study aimed to determine the appropriate vancomycin dosage, considering patient size and organ maturation, by simulating the bacterial count and biomarker level for drug administration in pediatric patients with gram-positive bacterial (GPB) infections.

Methods: Natural language processing for n-gram analysis was used to detect appropriate pharmacodynamic (PD) markers in infectious disease patients. In addition, a mechanism-based model was established to describe the systemic exposure and evaluate the PD marker simultaneously in pediatric patients.

Application of Size and Maturation Functions to Population Pharmacokinetic Modeling of Pediatric Patients.

Traditionally, dosage for pediatric patients has been optimized using simple weight-scaled methods, but these methods do not always meet the requirements of children. To overcome this discrepancy, population pharmacokinetic (PK) modeling of size and maturation functions has been proposed. The main objective of the present study was to evaluate a new modeling method for pediatric patients using clinical data from three different clinical studies.