Kyle Swanson

According to our database, Kyle Swanson authored at least 16 papers between 2019 and 2024.

Collaborative distances:

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

2024

Generative AI for designing and validating easily synthesizable and structurally novel antibiotics.

Kyle Swanson, Gary Liu, Denise B. Catacutan, Autumn Arnold, James Zou, Jonathan M. Stokes

Nat. Mac. Intell., 2024

ADMET-AI: a machine learning ADMET platform for evaluation of large-scale chemical libraries.

Kyle Swanson, Parker Walther, Jeremy Leitz, Souhrid Mukherjee, Joseph C. Wu, Rabindra V. Shivnaraine, James Zou

Bioinform., 2024

UniTox: Leveraging LLMs to Curate a Unified Dataset of Drug-Induced Toxicity from FDA Labels.

Jacob Silberg, Kyle Swanson, Elana Simon, Angela Zhang, Zaniar Ghazizadeh, Scott Ogden, Hisham Hamadeh, James Y. Zou

Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, 2024

2023

Towards Universal Cell Embeddings: Integrating Single-cell RNA-seq Datasets across Species with SATURN.

Yanay Rosen, Yusuf H. Roohani, Maria Brbic, Kyle Swanson, Ziang Li, Jure Leskovec

Dataset, December, 2023

Predicting Drug Solubility Using Different Machine Learning Methods - Linear Regression Model with Extracted Chemical Features vs Graph Convolutional Neural Network.

John Ho, Zhao-Heng Yin, Colin Zhang, Henry Overhauser, Kyle Swanson, Yang Ha

CoRR, 2023

2022

Predicting Immune Escape with Pretrained Protein Language Model Embeddings.

Kyle Swanson, Howard Chang, James Zou

Proceedings of the Machine Learning in Computational Biology, 21-22 November 2022, Online, 2022

Monte Carlo Tree Search for Interpreting Stress in Natural Language.

Kyle Swanson, Joy Hsu, Mirac Suzgun

Proceedings of the Second Workshop on Language Technology for Equality, 2022

2021

VMAF-based Bitrate Ladder Estimation for Adaptive Streaming.

Angeliki V. Katsenou, Fan Zhang, Kyle Swanson, Mariana Afonso, Joel Sole, David R. Bull

Proceedings of the Picture Coding Symposium, 2021

2020

Uncertainty Quantification Using Neural Networks for Molecular Property Prediction.

Lior Hirschfeld, Kyle Swanson, Kevin Yang, Regina Barzilay, Connor W. Coley

J. Chem. Inf. Model., 2020

Improving Molecular Design by Stochastic Iterative Target Augmentation.

Kevin Yang, Wengong Jin, Kyle Swanson, Regina Barzilay, Tommi S. Jaakkola

Proceedings of the 37th International Conference on Machine Learning, 2020

Rationalizing Text Matching: Learning Sparse Alignments via Optimal Transport.

Kyle Swanson, Lili Yu, Tao Lei

Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, 2020

2019

Correction to Analyzing Learned Molecular Representations for Property Prediction.

Kevin Yang, Kyle Swanson, Wengong Jin, Connor W. Coley, Philipp Eiden, Hua Gao, Angel Guzman-Perez, Timothy Hopper, Brian Kelley, Miriam Mathea, Andrew Palmer, Volker Settels, Tommi S. Jaakkola, Klavs F. Jensen, Regina Barzilay

J. Chem. Inf. Model., 2019

Analyzing Learned Molecular Representations for Property Prediction.

Kevin Yang, Kyle Swanson, Wengong Jin, Connor W. Coley, Philipp Eiden, Hua Gao, Angel Guzman-Perez, Timothy Hopper, Brian Kelley, Miriam Mathea, Andrew Palmer, Volker Settels, Tommi S. Jaakkola, Klavs F. Jensen, Regina Barzilay

J. Chem. Inf. Model., 2019

Deep Learning for Automated Classification and Characterization of Amorphous Materials.

Kirk Swanson, Shubhendu Trivedi, Joshua Lequieu, Kyle Swanson, Risi Kondor

CoRR, 2019

Building a Production Model for Retrieval-Based Chatbots.

Kyle Swanson, Lili Yu, Christopher Fox, Jeremy Wohlwend, Tao Lei

CoRR, 2019

Are Learned Molecular Representations Ready For Prime Time?

Kevin Yang, Kyle Swanson, Wengong Jin, Connor W. Coley, Philipp Eiden, Hua Gao, Angel Guzman-Perez, Timothy Hopper, Brian Kelley, Miriam Mathea, Andrew Palmer, Volker Settels, Tommi S. Jaakkola, Klavs F. Jensen, Regina Barzilay

CoRR, 2019