Hello! I am Wenhao Gao (高文昊). I am a chemistry and AI researcher, currently pursuing my Ph.D. at MIT under the guidance of Professor Connor W. Coley. My current research focuses on the development of synthesizable molecular design algorithms with generative AI and forward synthesis planning. My long-term goal is to enhance, accelerate and scale up the process of molecular discovery by developing AI algorithms that assist in decision-making. I am honored to have my research supported by Google PhD Fellowship and Takeda Fellowship. Additionally, I am a recipient of the D.E. Shaw Research Fellowship and have been recognized as one of the CAS Future Leaders Top 100.
This fall, I will be on job market looking for postdoc and faculty positions! You can find more information about my research, publications, and projects, my professional and academic background below. Thank you for visiting, and feel free to reach out with questions, opportunities or collaboration ideas.
Most recent publications on Google Scholar.
‡ indicates equal contribution.
Substrate Scope Contrastive Learning: Repurposing Human Bias to Learn Atomic Representations
Wenhao Gao, Priyanka Raghavan, Ron Shprints and Connor W. Coley
ArXiv
Closing the Execution Gap in Generative AI for Chemicals and Materials: Freeways or Safeguards
Akshay Subramanian, Wenhao Gao, ... Rafael Gomez-Bombarelli
An MIT Exploration of Generative AI
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
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
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
Synthesizability of Molecules Proposed by Generative Models
Wenhao Gao and Connor W. Coley
Journal of Chemical Informatics and Modeling
Substrate Scope Contrastive Learning: Repurposing Human Bias to Learn Atomic Representations
Wenhao Gao, Priyanka Raghavan, Ron Shprints and Connor W. Coley
ArXiv
AUTODIFF: Autoregressive Diffusion Modeling for Structure-based Drug Design
Xinze Li, Penglei Wang, Tianfan Fu, Wenhao Gao, Chengtao Li, Leilei Shi and Junhong Liu
ArXiv
Closing the Execution Gap in Generative AI for Chemicals and Materials: Freeways or Safeguards
Akshay Subramanian, Wenhao Gao, ... Rafael Gomez-Bombarelli
An MIT Exploration of Generative AI
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.