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.
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
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
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
Reinforced Genetic Algorithm for Structure-based Drug Design
Tianfan Fu‡, Wenhao Gao‡, Connor W. Coley and Jimeng Sun
NeurIPS 2022
Autonomous Platforms for Data-driven Organic Synthesis
Wenhao Gao‡, Priyanka Raghavan, and Connor W. Coley
Nature Communications
Amortized Tree Generation for Bottom-up Synthesis Planning and Synthesizable Molecular Design
Wenhao Gao, Rocio Mercado, and Connor W. Coley
ICLR 2022
Differentiable Scaffolding Tree for Molecular Optimization
Tianfan Fu‡, Wenhao Gao‡, Cao Xiao, Jacob Yasonik, Connor W. Coley and Jimeng Sun
ICLR 2022
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
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
Deep Learning in Protein Structural Modeling and Design
Wenhao Gao, Sai P. Mahajan, Jeremias Sulam and Jeffrey J.Gray
Patterns
Synthesizability of Molecules Proposed by Generative Models
Wenhao Gao and Connor W. Coley
Journal of Chemical Informatics and Modeling
Scientific discovery in the age of artificial intelligence
Hanchen Wang‡, Tianfan Fu‡, Yuanqi Du‡, Wenhao Gao, ... Marinka Zitnik
Nature
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
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
Reinforced Genetic Algorithm for Structure-based Drug Design
Tianfan Fu‡, Wenhao Gao‡, Connor W. Coley and Jimeng Sun
NeurIPS 2022
Autonomous Platforms for Data-driven Organic Synthesis
Wenhao Gao‡, Priyanka Raghavan, and Connor W. Coley
Nature Communications
Amortized Tree Generation for Bottom-up Synthesis Planning and Synthesizable Molecular Design
Wenhao Gao, Rocio Mercado, and Connor W. Coley
ICLR 2022
Differentiable Scaffolding Tree for Molecular Optimization
Tianfan Fu‡, Wenhao Gao‡, Cao Xiao, Jacob Yasonik, Connor W. Coley and Jimeng Sun
ICLR 2022
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
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
Deep Learning in Protein Structural Modeling and Design
Wenhao Gao, Sai P. Mahajan, Jeremias Sulam and Jeffrey J.Gray
Patterns
Synthesizability of Molecules Proposed by Generative Models
Wenhao Gao and Connor W. Coley
Journal of Chemical Informatics and Modeling
Full Resume in PDF.