AI-Driven Framework conDitar-dev Generates Developable 3D Drug Molecules with Experimental Validation

Jul 15, 2026

Researchers present conDitar-dev, a conditional diffusion-based framework for structure-based drug design that generates ligands with strong binding affinities and improved ADMET properties. The model outperforms state-of-the-art baselines on a benchmark of human disease targets, achieving an average binding score of -8.85 kcal/mol and up to 73% improvement in ADMET properties. Experimental validation on PD-L1 and CSF1R targets yielded molecules with micromolar binding affinities and nanomolar IC50 values, demonstrating real-world applicability.

Why it matters: This work advances AI-driven drug discovery by demonstrating a method that generates molecules with both computational and experimental validation, potentially accelerating the development of new therapeutics.

Full story at: arXiv Machine Learning ↗