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. This study sought to improve DTI predictive models using a Bidirectional Encoder Representations from the Transformers (BERT)-pretrained model, ChemBERTa, for chemical compounds. Pretraining features the use of a simplified molecular-input line-entry system (SMILES). We also employ the pretrained ProBERT for target proteins (pretraining employed the amino acid sequences). The BIOSNAP, DAVIS, and BindingDB databases (DBs) were used (alone or together) for learning. The final model, taught by both ChemBERTa and ProtBert and the integrated DBs, afforded the best DTI predictive performance to date based on the receiver operating characteristic area under the curve (AUC) and precision-recall-AUC values compared with previous models. The performance of the final model was verified using a specific case study on 13 pairs of subtrates and the metabolic enzyme cytochrome P450 (CYP). The final model afforded excellent DTI prediction. As the real-world interactions between drugs and target proteins are expected to exhibit specific patterns, pretraining with ChemBERTa and ProtBert could teach such patterns. Learning the patterns of such interactions would enhance DTI accuracy if learning employs large, well-balanced datasets that cover all relationships between drugs and target proteins.

The recent advancements in large language models (LLMs) have brought significant changes to the field of education, particularly in the generation and evaluation of feedback. LLMs are transforming education by streamlining tasks like content creation, feedback generation, and assessment, reducing teachers’ workload and improving online education efficiency. This study aimed to verify the consistency and reliability of LLMs as evaluators by conducting automated evaluations using various LLMs based on five educational evaluation criteria. The analysis revealed that while LLMs were capable of performing consistent evaluations under certain conditions, a lack of consistency was observed both among evaluators and across models for other criteria. Notably, low agreement among human evaluators correlated with reduced reliability in LLM evaluations. Furthermore, variations in evaluation results were influenced by factors such as prompt strategies and model architecture, highlighting the complexity of achieving reliable assessments using LLMs. These findings suggest that while LLMs have the potential to transform educational systems, careful selection and combination of models are essential to improve their consistency and align their performance with human evaluators in educational settings.

Intrinsic clearance is a key pharmacokinetic parameter in drug development because it influences systemic exposure and dose selection. Conventional in vitro-in vivo extrapolation (IVIVE) approaches are useful but often require additional experimental resources and may be difficult to apply in the earliest stages of compound screening. This study aimed to develop a biologically informed computational framework for intrinsic clearance prediction by integrating drug-target interaction (DTI)-derived features from 23 hepatic metabolic proteins with physicochemical properties, using an IVIVE-based endpoint harmonization approach to unify heterogeneous endpoint labels. A multi-source human clearance dataset was assembled and harmonized into a common intrinsic clearance endpoint. A pretrained DTI model based on ChemBERTa and ProtBERT was used to generate precomputed interaction features for CYP, UGT, and SULT family proteins. These features were combined with compound structure and physicochemical descriptors in downstream clearance prediction models using either a multilayer perceptron (MLP) or a transformer encoder. Internal evaluation included repeated random 5-fold cross-validation and scaffold-split analysis, and an independent external evaluation set of 185 compounds was additionally examined. Feature settings incorporating DTI-derived information showed improved regression performance compared with the descriptor-only baseline across most evaluated metrics. Under the augmented setting, the MLP model with DTI + LogP/Fup showed the highest overall regression performance among the evaluated models (r²_m = 0.2505 ± 0.0792, r² = 0.2364 ± 0.0879), while the highest CI was observed in the transformer encoder model with DTI + LogP/Fup (0.6915 ± 0.0144). On the external evaluation set, the MLP model showed an AFE of 1.116, with 52.970% and 70.792% of predictions falling within 2-fold and 3-fold error, respectively. Comparisons with published IVIVE-related studies should be interpreted as contextual rather than direct head-to-head validation. The proposed framework supports intrinsic clearance estimation by combining biologically informed DTI-derived features with physicochemical information in an interpretable prediction setting. Although the predictive performance remains modest, the results suggest potential utility as a supportive early-stage prioritization tool when standardized experimental clearance data are limited. Further work is needed to expand the dataset, strengthen external validation with independent cohorts, and incorporate additional elimination pathways such as renal clearance and transporter-mediated disposition.