Computer-Aided Drug Discovery Core

Computer-Aided Drug Discovery Core


The CADD core is established for facilitating the identification and development of novel therapeutic agents and fostering close collaborations among molecular biologists, structural biologists, medicinal chemists, and computational scientists at UTHSA, UTSA, and beyond. 

The facility provides high-level computational support to help launch and accelerate drug discovery projects by employing a variety of computational approaches including cutting-edge commercial software, open-source software, and our in-house software. In particular, with support from the Texas Advanced Computing Center (TACC), a world leader in academic supercomputing, the CADD core conducts computational analyses such as long-time molecular dynamic simulation, super large-scale virtual screening, de novo design, high-level free energy estimation, protein design, and quantum chemistry calculation.

Services Provided

  •  Protein Structure Modelling
    • Protein structure prediction.
    • Protein conformation prediction: Predict ensemble of possible conformations for protein. 
    • Function analysis: Use long-time molecular dynamics simulation to investigate the structural basis of protein function.
    • Mutation prediction: Predict key mutations to alter the function or stability of proteins. Quantum chemistry-based methods are used to help improve the catalytic efficiency of enzyme.
    • Protein and antibody design: Design proteins and antibodies with specific functional attributes.
  • Protein-Protein/Protein-Peptide Interactions
    • Protein Docking: Identify potential binding mode of interacting proteins and peptides.
    • Peptidomimetics design: Design peptidomimetics based on delineated protein-protein interactions.
    • Biomolecular nanoparticle/complex: Use coarse-grained model to characterize large biomolecular system.
  • Drug Discovery
    • Structure-based drug discovery
      • Binding site prediction: Predict potential small molecular ligand binding site, or the allosteric ligand binding site.
      • Virtual screening: The CADD core uses unique high-accuracy energy estimation workflow as well as AI-based binder predictor to improve the accuracy of conventional virtual screening process to help identify molecules with specificity for each target protein.
      • AI-based super large-scale virtual screening: Screen molecules from Make-on-Demand database with more than 20 billions of synthesis-ready compounds. With this approach, it is possible to identify binders with nanomolar or even sub-nanomolar activity.
      • De novo design: Design millions of novel diverse molecules without dependence on known ligand or fragment, which could achieve higher atomic efficiency comparing with virtual screening approaches.
      • Lead optimization: Guide the optimization of known active compounds.
      • Mimic/combination design: Design novel compounds that inherit merits from multiple known active compounds. This will help drive the design of mimicry inhibitors with novel scaffolds.
      • Multitarget drug design: Design molecules that could target multiple distinct proteins. It could also be employed to design ligands targeting IDP proteins.
    • Non-structure-based drug discovery

      • Similarity based searching: Screen analogues of known compounds from the Make-on-Demand database.

      • Pharmacophore/shape-based discovery: Screen compounds that possess similar pharmacophore or shape properties to active compounds.

      • AI-based predictor: Design or screen compounds with trained AI models, including GENTRL and our in-house SVM model.

      • Wet-experiment combined approaches: Provide computational support to the wet-experiment combined approaches, such as image-based screening and expression profile-based screening.

    • Analysis of identified active compounds

      • Binding site prediction: Predict potential binding site and binding mode of identified active compounds.

      • Quantitative structure-activity relationship analysis (Q-SAR): Analysis of the SAR for multiple active compounds and guide further structure optimization.

      • Binding free energy estimation: Predict the theoretical free energy of active compounds to validate the predicted binding mode and identify key binding moiety to guide further structure optimization.

      • Molecular dynamics: Use molecular dynamics simulation to investigate the binding properties of active compounds.

    • Bioinformatic/Chemoinformatic

      • Bioinformatics: The CADD core enables various bioinformatic analyses, for example, evolutionary tree, co-evolution analysis, gene ontology, sequence analysis, and biology network analysis.

      • Quantum chemistry analysis: Calculate the properties of molecules or mechanism of reaction based on ab initio principle.

      • Molecular descriptor: Calculate the molecular descriptor for chem-space analysis, diversity analysis, and database mining.

      • Property prediction model: Predict specific property of target ligand, such as BBB, ADME, and toxicity. The CADD core can also generate new prediction model for required properties.

      • Drug combination analysis: Calculate the synergistic effect of drug combinations.

    • Other Support

      • Application support: Provide support to deploy and apply other computational software to meet research needs.

    • Algorithm development support: The CADD core is always open to developing new computational approaches for addressing challenging research problems.

Contact Information

Yaxia Yuan: