Agentic Intelligence for Structured Drug Design

Project Description

Drug discovery benefits from systems that can connect user intent with chemically meaningful candidate design. This project explores agentic intelligence for structured drug design, with an emphasis on interactive workflows that help users specify design goals, inspect candidate molecules, and refine results through feedback.

The project studies structured molecular design workflows where users can provide design preferences, review candidate molecules, and compare alternatives iteratively. Public notes are intentionally kept at a high level while implementation details remain shared through collaboration channels.

Key directions include:

  • Goal-guided design: Guide molecular exploration around user-provided design goals.
  • Multi-Property Constraints: Support candidate review across multiple high-level properties.
  • Interactive refinement: Help users compare, critique, and refine generated candidates through an iterative workflow.

Together, these directions aim to support interactive design systems that can reflect user-provided goals, reason over multiple properties, and present results in a way that is easier for humans to inspect.

Publications

  • Scaffold-Aware Generative Augmentation and Reranking for Enhanced Virtual Screening
    Xin Wang, Yu Wang, Yunchao Liu, Jens Meiler, Tyler Derr.
    SIAM International Conference on Data Mining (SDM), 2026.
    [Paper]

  • Advancements in Ligand-Based Virtual Screening through the Synergistic Integration of Graph Neural Networks and Expert-Crafted Descriptors
    Yunchao Liu, Rocco Moretti, Yu Wang, Ha Dong, Bobby Bodenheimer, Tyler Derr, Jens Meiler.
    Journal of Chemical Information and Modeling (JCIM), 2025.

  • WelQrate: Defining the Gold Standard in Small Molecule Drug Discovery Benchmarking
    Yunchao Liu, Ha Dong, Xin Wang, Rocco Moretti, Yu Wang, Zhaoqian Su, Jiawei Gu, Bobby Bodenheimer, Charles Weaver, Jens Meiler, Tyler Derr.
    Conference on Neural Information Processing Systems (NeurIPS), 2024.
    [Paper] [Code]

  • Interpretable Chirality-Aware Graph Neural Network for Quantitative Structure-Activity Relationship Modeling in Drug Discovery
    Yunchao Liu, Yu Wang, Oanh Vu, Rocco Moretti, Bobby Bodenheimer, Jens Meiler, Tyler Derr.
    AAAI Conference on Artificial Intelligence (AAAI), 2023.
    [Paper] [Code]

  • ChemicalX: A Deep Learning Library for Drug Pair Scoring
    Benedek Rozemberczki, Charles Tapley Hoyt, Anna Gogleva, Piotr Grabowski, Klas Karis, Andrej Lamov, Andriy Nikolov, Sebastian Nilsson, Michael Ughetto, Yu Wang, Tyler Derr, Benjamin M Gyori.
    ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2022.
    [Paper] [Code]

Preprints

Project manuscripts and technical reports will be added as they are released.

Tutorials and Workshops

Workshops

Related workshops will be added as they are released.

Tutorials

Related tutorials will be added as they are released.

Awards and Honors

Project awards and honors will be added as they are released.

Acknowledgements

We thank our academic and domain collaborators for discussions and support on structure-aware drug-design research.

Contact

Yu Wang: Yu.Wang6@uga.edu