Wenhao Gao

Ph.D. candidate, Massachusetts Institute of Technology

whgao [AT] mit.edu

Bio

Hello! I am Wenhao Gao (高文昊), a researcher in AI and molecular science, currently completing my Ph.D. at MIT with Professor Connor W. Coley. My current research focuses on advancing molecular design by developing AI-driven models that integrate generative modeling with chemical synthesis, aimed at enabling scalable, synthesizable, and efficient exploration of chemical space. My long-term goal is to systematize molecular design methodologies to enable the discovery of novel functional molecules in scale, with potential applications in drug discovery, sustainability, and beyond.

I am grateful to have my research supported by the Google PhD Fellowship and the Takeda Fellowship, and I am honored to be a recipient of the D.E. Shaw Research Fellowship and recognized among CAS Future Leaders Top 100.

This fall, I am actively seeking postdoctoral and faculty positions! 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 please feel free to reach out with questions, opportunities, or collaboration ideas.

Vitæ

Full Resume in PDF.

Publications

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

Generative Artificial Intelligence for Navigating Synthesizable Chemical Space

Wenhao Gao‡, Shitong Luo‡ and Connor W. Coley

ArXiv

Paper Code

Projecting Molecules into Synthesizable Chemical Spaces

Shitong Luo‡, Wenhao Gao‡, Zuofan Wu‡, Jian Peng, Connor W. Coley and Jianzhu Ma

ICML 2024

Paper Code

Double-Ended Synthesis Planning with Goal-Constrained Bidirectional Search

Kevin Yu, Jihye Roh, Ziang Li, Wenhao Gao, Runzhong Wang and Connor W Coley

NeurIPS 2024 (Spotlight)

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 Code

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

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 (Spotlight)

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

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

Generative Artificial Intelligence for Navigating Synthesizable Chemical Space

Wenhao Gao‡, Shitong Luo‡ and Connor W. Coley

ArXiv

Paper Code

Projecting Molecules into Synthesizable Chemical Spaces

Shitong Luo‡, Wenhao Gao‡, Zuofan Wu‡, Jian Peng, Connor W. Coley and Jianzhu Ma

ICML 2024

Paper Code

Double-Ended Synthesis Planning with Goal-Constrained Bidirectional Search

Kevin Yu, Jihye Roh, Ziang Li, Wenhao Gao, Runzhong Wang and Connor W Coley

NeurIPS 2024 (Spotlight)

Paper Code

Efficient Evolutionary Search over Chemical Space with Large Language Models

Haorui Wang, Marta Skreta, Yuanqi Du, Wenhao Gao, Lingkai Kong, Cher Tian Ser, Felix Strieth-Kalthoff, Chenru Duan, Yuchen Zhuang, Yue Yu, Yanqiao Zhu, Alan Aspuru-Guzik, Kirill Neklyudov and Chao Zhang

ArXiv

Paper Code

Syntax-Guided Procedural Synthesis of Molecules

Michael Sun, Alston Lo, Wenhao Gao, Minghao Guo, Veronika Thost, Jie Chen, Connor W. Coley and Wojciech Matusik

ArXiv

Paper

TDC-2: Multimodal Foundation for Therapeutic Science

Alejandro Velez-Arce, Kexin Huang, Michelle Li, Xiang Lin, Wenhao Gao, Tianfan Fu, Manolis Kellis, Bradley L Pentelute and Marinka Zitnik

ArXiv

Paper

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

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

ArXiv

Paper Code

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

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 (Spotlight)

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

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