Wenhao Gao

AI & Chemistry Researcher.

gaowh19 [AT] gmail.com

Bio

Hello! I am Wenhao Gao (高文昊), an incoming Assistant Professor in the Department of Chemical and Biomolecular Engineering at the University of Pennsylvania, and a member of Innovation in Data Engineering and Science (IDEAS). I will be starting in January 2027 and will begin recruiting Ph.D. students from the December 2025 application cycle. Before joining Penn, I will be a postdoctoral researcher at Stanford University with Professor Grant Rotskoff and Professor Stefano Ermon.

I am interested in the intersection of chemistry and AI, particularly in how AI can transform molecular discovery. My research focuses on building systematic methodologies that enable scalable, effective, and efficient molecular discovery for applications such as drug design and sustainable materials. The main approach is to integrate chemical and physical priors with modern computational techniques to enhance the modeling and design of molecules and materials with targeted functionalities, with a recent emphasis on AI and machine learning. I am also interested in applying these methods to real-world problems, including therapeutic discovery and sustainable chemistry.

I recently received my PhD in Chemical Engineering from MIT, where I was advised by Professor Connor W. Coley. My PhD research was supported by the Google PhD Fellowship and the Takeda Fellowship. I am honored to be recognized among the CAS Future Leaders 2025, a D. E. Shaw Research Fellow, and a Forbes 30 Under 30 Asia honoree in Healthcare and Science. You can find more information about my research, publications, and projects, as well as my professional and academic background, below.

Group

I'll be recruiting PhD and Master students in the December 2025 application cycle. If you're interested in joining my group, please apply to the CBE, CIS, or Chemistry PhD programs at Penn and list my name in your application. I'll also be recruiting postdocs. If you're interested in working with me, please email your CV along with a brief statement of your research interests and background. Please also include your professional goals and what you hope to learn and accomplish specifically while working in the group. If you have independent funding, kindly mention it in your email. If you're currently a PhD student at Penn and are interested in my research, feel free to reach out! Please note that due to the high volume of emails, I may not be able to respond to every message I receive.

Vitæ

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

PNAS

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Projecting Molecules into Synthesizable Chemical Spaces

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

ICML 2024

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

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

JACS

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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

Sample Efficiency Matters: A Benchmark for Practical Molecular Optimization

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

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

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

PNAS

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)

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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

ICLR 2025

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

JACS

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
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Wenhao Gao
gaowh19 [AT] gmail.com