Stanford Advisors

All Publications

  • Synthesis and evaluation of designed PKC modulators for enhanced cancer immunotherapy. Nature communications Hardman, C., Ho, S., Shimizu, A., Luu-Nguyen, Q., Sloane, J. L., Soliman, M. S., Marsden, M. D., Zack, J. A., Wender, P. A. 2020; 11 (1): 1879


    Bryostatin 1 is a marine natural product under investigation for HIV/AIDS eradication, the treatment of neurological disorders, and enhanced CAR T/NK cell immunotherapy. Despite its promising activity, bryostatin 1 is neither evolved nor optimized for the treatment of human disease. Here we report the design, synthesis, and biological evaluation of several close-in analogs of bryostatin 1. Using a function-oriented synthesis approach, we synthesize a series of bryostatin analogs designed to maintain affinity for bryostatin's target protein kinase C (PKC) while enabling exploration of their divergent biological functions. Our late-stage diversification strategy provides efficient access to a library of bryostatin analogs, which per our design retain affinity for PKC but exhibit variable PKC translocation kinetics. We further demonstrate that select analogs potently increase cell surface expression of CD22, a promising CAR T cell target for the treatment of leukemias, highlighting the clinical potential of bryostatin analogs for enhancing targeted immunotherapies.

    View details for DOI 10.1038/s41467-020-15742-7

    View details for PubMedID 32312992

  • Retrosynthetic Reaction Prediction Using Neural Sequence-to-Sequence Models ACS CENTRAL SCIENCE Liu, B., Ramsundar, B., Kawthekar, P., Shi, J., Gomes, J., Quang Luu Nguyen, Ho, S., Sloane, J., Wender, P., Pande, V. 2017; 3 (10): 1103–13


    We describe a fully data driven model that learns to perform a retrosynthetic reaction prediction task, which is treated as a sequence-to-sequence mapping problem. The end-to-end trained model has an encoder-decoder architecture that consists of two recurrent neural networks, which has previously shown great success in solving other sequence-to-sequence prediction tasks such as machine translation. The model is trained on 50,000 experimental reaction examples from the United States patent literature, which span 10 broad reaction types that are commonly used by medicinal chemists. We find that our model performs comparably with a rule-based expert system baseline model, and also overcomes certain limitations associated with rule-based expert systems and with any machine learning approach that contains a rule-based expert system component. Our model provides an important first step toward solving the challenging problem of computational retrosynthetic analysis.

    View details for PubMedID 29104927

    View details for PubMedCentralID PMC5658761