Early-stage drug discovery requires evaluating thousands of compounds for pharmacokinetic properties, but traditional ADMET screening depends on time-consuming in vitro and in vivo experiments. This project develops a transformer-based framework that predicts multiple ADMET properties—such as absorption, metabolism, distribution, excretion, and toxicity—directly from molecular structure, significantly reducing experimental workload.
Key features of this approach:
- Unified property prediction – A single model predicts multiple ADMET properties, eliminating the need for separate models.
- No handcrafted descriptors – The model learns directly from molecular structures, making it adaptable to different drug-like molecules.
- Scalable high-throughput screening – Enables the rapid evaluation of thousands of compounds, prioritizing promising candidates while filtering out those with poor pharmacokinetics.
This method accelerates drug screening, cutting costs and reducing reliance on experimental assays while maintaining accuracy in pharmacokinetic evaluations.
Khambhawala, A., Lee, C.H., Pahari, S. and Kwon, J.S.I., 2025. Minimizing late-stage failure in drug development with transformer models: Enhancing drug screening and pharmacokinetic predictions. Chemical Engineering Journal, p.160423.https://doi.org/10.1016/j.cej.2025.160423