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 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.

Publications

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

Paper

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

Paper

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

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

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

Synthesizability of Molecules Proposed by Generative Models

Wenhao Gao and Connor W. Coley

Journal of Chemical Informatics and Modeling

Paper Code

Substrate Scope Contrastive Learning: Repurposing Human Bias to Learn Atomic Representations

Wenhao Gao, Priyanka Raghavan, Ron Shprints and Connor W. Coley

ArXiv

Paper

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

Paper

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

Paper

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
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Wenhao Gao
whgao [AT] mit.edu