Wenhao Gao

Ph.D. candidate, Massachusetts Institute of Technology

whgao [AT] mit.edu

Bio

Hello! I am Wenhao Gao (高文昊). I am a Ph.D. candidate at MIT, where I have the privilege of being advised by Connor W. Coley. My research is centered around accelerating the molecular discovery process by harnessing the power of artificial intelligence and robotics.

I am dedicated to achieving my goal, which is the realization of autonomous molecular discovery. This concept envisions a systematic methodology for molecular discovery requiring minimal human intervention in principle, making the process not only faster but also more scalable. In my previous work, I have concentrated on developing AI algorithms with broader practical applicability for designing small organic molecules, expanding upon the synthetic accessibility and sample efficiency. Recently, my efforts have been directed towards bridging the gap between synthesis planning software and robotic automation hardware, as well as devising pre-training strategies for molecular representation learning.

I am honored to be an MIT-Takeda Fellow, focusing on designing drug candidates that address pressing health challenges. I work closely with the Machine Learning for Pharmaceutical Discovery and Synthesis consortium, enabling me to better understand real-world requirements and industry demands. Additionally, I am a proud member of the MIT Climate and Sustainability Consortium, where I contribute my expertise in designing next-generation carbon-capture materials aimed at mitigating climate change and fostering sustainability.

You can find more information about my research, publications, and projects, as well as my professional and academic background below.

Thank you for visiting, and feel free to reach out to me with any questions or collaboration ideas.

News

  • [2022/09] Our paper on molecular optimization benchmark is accepted by NeurIPS 2022, Datasets and Benchmarks track [Code]
  • [2022/09] Our paper on machine learning assisted structure-based drug design is accepted by NeurIPS 2022 [Code]
  • [2022/08] I am houred to be selected as one of the MIT-Takeda fellows [Link]
  • [2022/08] I attended the ACS Fall conference and presented TDC and PMO [Link]
  • [2022/07] We organized AI for Science Workshop at ICML 2022 [Link]

    • Publications

    Most recent publications on Google Scholar.
    ‡ indicates equal contribution.

    Scientific discovery in the age of artificial intelligence

    Hanchen Wang‡, Tianfan Fu‡, Yuanqi Du‡, Wenhao Gao, ... Marinka Zitnik

    Nature

    Paper

    Artificial Intelligence Foundation for Therapeutic Science

    Kexin Huang‡, Tianfan Fu‡, Wenhao Gao‡, Yue Zhao, Yusuf Roohani, Jure Leskovec, Connor W. Coley, Cao Xiao, Jimeng Sun and Marinka Zitnik

    Nature Chemical Biology

    Paper Slides Code

    Sample Efficiency Matters: A Benchmark for Practical Molecular Optimization

    Wenhao Gao‡, Tianfan Fu‡, Jimeng Sun and Connor W. Coley

    NeurIPS 2022, Datasets and Benchmarks Track

    Paper Code

    Reinforced Genetic Algorithm for Structure-based Drug Design

    Tianfan Fu‡, Wenhao Gao‡, Connor W. Coley and Jimeng Sun

    NeurIPS 2022

    Paper Code

    Autonomous Platforms for Data-driven Organic Synthesis

    Wenhao Gao‡, Priyanka Raghavan, and Connor W. Coley

    Nature Communications

    Paper

    Amortized Tree Generation for Bottom-up Synthesis Planning and Synthesizable Molecular Design

    Wenhao Gao, Rocio Mercado, and Connor W. Coley

    ICLR 2022

    Paper Slides Code

    Differentiable Scaffolding Tree for Molecular Optimization

    Tianfan Fu‡, Wenhao Gao‡, Cao Xiao, Jacob Yasonik, Connor W. Coley and Jimeng Sun

    ICLR 2022

    Paper Code

    Pairwise Difference Regression: A Machine Learning Meta-algorithm for Improved Prediction and Uncertainty Quantification in Chemical Search

    Michael Tynes, Wenhao Gao, Daniel J. Burrill, Enrique R. Batista, Danny Perez, Ping Yang and Nicholas Lubbers

    Journal of Chemical Informatics and Modeling

    Paper

    Therapeutics Data Commons: Machine Learning Datasets and Tasks for Therapeutics

    Kexin Huang‡, Tianfan Fu‡, Wenhao Gao‡, Yue Zhao, Yusuf Roohani, Jure Leskovec, Connor W. Coley, Cao Xiao, Jimeng Sun and Marinka Zitnik

    NeurIPS 2021, Datasets and Benchmarks Track

    Paper Code

    Deep Learning in Protein Structural Modeling and Design

    Wenhao Gao, Sai P. Mahajan, Jeremias Sulam and Jeffrey J.Gray

    Patterns

    Paper

    Synthesizability of Molecules Proposed by Generative Models

    Wenhao Gao and Connor W. Coley

    Journal of Chemical Informatics and Modeling

    Paper Code

    Scientific discovery in the age of artificial intelligence

    Hanchen Wang‡, Tianfan Fu‡, Yuanqi Du‡, Wenhao Gao, ... Marinka Zitnik

    Nature

    Paper

    Artificial Intelligence Foundation for Therapeutic Science

    Kexin Huang‡, Tianfan Fu‡, Wenhao Gao‡, Yue Zhao, Yusuf Roohani, Jure Leskovec, Connor W. Coley, Cao Xiao, Jimeng Sun and Marinka Zitnik

    Nature Chemical Biology

    Paper Slides Code

    Sample Efficiency Matters: A Benchmark for Practical Molecular Optimization

    Wenhao Gao‡, Tianfan Fu‡, Jimeng Sun and Connor W. Coley

    NeurIPS 2022, Datasets and Benchmarks Track

    Paper Code

    Reinforced Genetic Algorithm for Structure-based Drug Design

    Tianfan Fu‡, Wenhao Gao‡, Connor W. Coley and Jimeng Sun

    NeurIPS 2022

    Paper Code

    Autonomous Platforms for Data-driven Organic Synthesis

    Wenhao Gao‡, Priyanka Raghavan, and Connor W. Coley

    Nature Communications

    Paper

    Amortized Tree Generation for Bottom-up Synthesis Planning and Synthesizable Molecular Design

    Wenhao Gao, Rocio Mercado, and Connor W. Coley

    ICLR 2022

    Paper Slides Code

    Differentiable Scaffolding Tree for Molecular Optimization

    Tianfan Fu‡, Wenhao Gao‡, Cao Xiao, Jacob Yasonik, Connor W. Coley and Jimeng Sun

    ICLR 2022

    Paper Code

    Pairwise Difference Regression: A Machine Learning Meta-algorithm for Improved Prediction and Uncertainty Quantification in Chemical Search

    Michael Tynes, Wenhao Gao, Daniel J. Burrill, Enrique R. Batista, Danny Perez, Ping Yang and Nicholas Lubbers

    Journal of Chemical Informatics and Modeling

    Paper

    Therapeutics Data Commons: Machine Learning Datasets and Tasks for Therapeutics

    Kexin Huang‡, Tianfan Fu‡, Wenhao Gao‡, Yue Zhao, Yusuf Roohani, Jure Leskovec, Connor W. Coley, Cao Xiao, Jimeng Sun and Marinka Zitnik

    NeurIPS 2021, Datasets and Benchmarks Track

    Paper Code

    Deep Learning in Protein Structural Modeling and Design

    Wenhao Gao, Sai P. Mahajan, Jeremias Sulam and Jeffrey J.Gray

    Patterns

    Paper

    Synthesizability of Molecules Proposed by Generative Models

    Wenhao Gao and Connor W. Coley

    Journal of Chemical Informatics and Modeling

    Paper Code

    Vitæ

    Full Resume in PDF.

    Acknowledgement
    This website was built with jekyll based on a template by Martin Saveski
    Wenhao Gao
    whgao [AT] mit.edu