Zhonglin Cao

According to our database, Zhonglin Cao authored at least 17 papers between 2019 and 2026.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2026

Scaling Atomistic Protein Binder Design with Generative Pretraining and Test-Time Compute.

Kieran Didi, Zuobai Zhang, Guoqing Zhou, Danny Reidenbach, Zhonglin Cao, Sooyoung Cha, Tomas Geffner, Christian Dallago, Jian Tang, Michael M. Bronstein, Martin Steinegger, Emine Küçükbenli, Arash Vahdat, Karsten Kreis

CoRR, March, 2026

Fold-CP: A Context Parallelism Framework for Biomolecular Modeling.

Dejun Lin, Simon K. S. Chu, Vishanth Iyer, Youhan Lee, John St. John, Kevin Boyd, Brian Roland, Xiaowei Ren, Guoqing Zhou, Zhonglin Cao, Polina Binder, Yuliya Zhautouskaya, Jakub Zakrzewski, Maximilian Stadler, Kyle Gion, Yuxing Peng, Xi Chen, Tianjing Zhang, Philipp Junk, Michelle Dimon, Pawel Gniewek, Fabian Ortega, McKinley Polen, Ivan Grubisic, Ali Bashir, Graham Holt, Danny Kovtun, Matthias Grass, Luca Naef, Rui Wang, Jian Peng, Anthony B. Costa, Saee Paliwal, Eddie Calleja, Timur Rvachov, Neha Tadimeti, Roy Tal, Emine Küçükbenli

CoRR, March, 2026

2025

La-Proteina: Atomistic Protein Generation via Partially Latent Flow Matching.

Tomas Geffner, Kieran Didi, Zhonglin Cao, Danny Reidenbach, Zuobai Zhang, Christian Dallago, Emine Küçükbenli, Karsten Kreis, Arash Vahdat

CoRR, July, 2025

A Deep-Learning-Based Targeted Interpolation Method for Seismic Data: A Consecutively Missing Trace VSP Case.

Wen Yang, Qianggong Song, Le Li, Xiaobin Li, Zhonglin Cao, Pengfei Duan

IEEE Geosci. Remote. Sens. Lett., 2025

Efficient Molecular Conformer Generation with SO(3)-Averaged Flow Matching and Reflow.

Zhonglin Cao, Mario Geiger, Allan dos Santos Costa, Danny Reidenbach, Karsten Kreis, Tomas Geffner, Franco Pellegrini, Guoqing Zhou, Emine Küçükbenli

Proceedings of the Forty-second International Conference on Machine Learning, 2025

Proteina: Scaling Flow-based Protein Structure Generative Models.

Tomas Geffner, Kieran Didi, Zuobai Zhang, Danny Reidenbach, Zhonglin Cao, Jason Yim, Mario Geiger, Christian Dallago, Emine Küçükbenli, Arash Vahdat, Karsten Kreis

Proceedings of the Thirteenth International Conference on Learning Representations, 2025

2024

Large-Scale Pretraining Improves Sample Efficiency of Active Learning-Based Virtual Screening.

Zhonglin Cao, Simone Sciabola, Ye Wang

J. Chem. Inf. Model., 2024

BioNeMo Framework: a modular, high-performance library for AI model development in drug discovery.

Peter St. John, Dejun Lin, Polina Binder, Malcolm Greaves, Vega Shah, John St. John, Adrian Lange, Patrick D. Hsu, Rajesh Illango, Arvind Ramanathan, Anima Anandkumar, David H. Brookes, Akosua Busia, Abhishaike Mahajan, Stephen Malina, Neha Prasad, Sam Sinai, Lindsay Edwards, Thomas Gaudelet, Cristian Regep, Martin Steinegger, Burkhard Rost, Alexander Brace, Kyle Hippe, Luca Naef, Keisuke Kamata, George Armstrong, Kevin Boyd, Zhonglin Cao, Han-Yi Chou, Simon Chu, Allan dos Santos Costa, Sajad Darabi, Eric Dawson, Kieran Didi, Cong Fu, Mario Geiger, Michelle Gill, Darren Hsu, Gagan Kaushik, Maria Korshunova, Steven Kothen-Hill, Youhan Lee, Meng Liu, Micha Livne, Zachary McClure, Jonathan Mitchell, Alireza Moradzadeh, Ohad Mosafi, Youssef Nashed, Saee Paliwal, Yuxing Peng, Sara Rabhi, Farhad Ramezanghorbani, Danny Reidenbach, Camir Ricketts, Brian Roland, Kushal Shah, Tyler Shimko, Hassan Sirelkhatim, Savitha Srinivasan, Abraham C. Stern, Dorota Toczydlowska, Srimukh Prasad Veccham, Niccolò Alberto Elia Venanzi, Anton Vorontsov, Jared Wilber, Isabel Wilkinson, Wei Jing Wong, Eva Xue, Cory Ye, Xin Yu, Yang Zhang, Guoqing Zhou, Becca Zandstein, Christian Dallago, Bruno Trentini, Emine Küçükbenli, Timur Rvachov, Eddie Calleja, Johnny Israeli, Harry Clifford, Risto Haukioja, Nicholas Haemel, Kyle Tretina, Neha Tadimeti, Anthony B. Costa

CoRR, 2024

EquiJump: Protein Dynamics Simulation via SO(3)-Equivariant Stochastic Interpolants.

Allan dos Santos Costa, Ilan Mitnikov, Franco Pellegrini, Ameya Daigavane, Mario Geiger, Zhonglin Cao, Karsten Kreis, Tess E. Smidt, Emine Küçükbenli, Joseph Jacobson

CoRR, 2024

2023

Large-scale Pretraining Improves Sample Efficiency of Active Learning based Molecule Virtual Screening.

Zhonglin Cao, Simone Sciabola, Ye Wang

CoRR, 2023

Neural Network Predicts Ion Concentration Profiles under Nanoconfinement.

Zhonglin Cao, Yuyang Wang, Cooper Lorsung, Amir Barati Farimani

CoRR, 2023

2022

Molecular contrastive learning of representations via graph neural networks.

Yuyang Wang, Jianren Wang, Zhonglin Cao, Amir Barati Farimani

Nat. Mach. Intell., 2022

MOFormer: Self-Supervised Transformer model for Metal-Organic Framework Property Prediction.

Zhonglin Cao, Rishikesh Magar, Yuyang Wang, Amir Barati Farimani

CoRR, 2022

2021

Deep reinforcement learning for predicting kinetic pathways to surface reconstruction in a ternary alloy.

Junwoong Yoon, Zhonglin Cao, Rajesh K. Raju, Yuyang Wang, Robert Burnley, Andrew J. Gellman, Amir Barati Farimani, Zachary W. Ulissi

Mach. Learn. Sci. Technol., 2021

MolCLR: Molecular Contrastive Learning of Representations via Graph Neural Networks.

Yuyang Wang, Jianren Wang, Zhonglin Cao, Amir Barati Farimani

CoRR, 2021

Deep Reinforcement Learning Optimizes Graphene Nanopores for Efficient Desalination.

Yuyang Wang, Zhonglin Cao, Amir Barati Farimani

CoRR, 2021

2019

Near-Field Communication Sensors.

Zhonglin Cao, Ping Chen, Zhong Ma, Sheng Li, Xingxun Gao, Rui-xin Wu, Lijia Pan, Yi Shi

Sensors, 2019