The Pseudo Projection Operator: Applications of Deep Learning to Projection Based Filtering in Non-Trivial Frequency Regimes. - Matthew L. Weiss; Nathan C. Frey; Siddharth Samsi; Randy C. Paffenroth; Vijay Gadepally (CoRR, 2021)

Scalable Geometric Deep Learning on Molecular Graphs. - Nathan C. Frey; Siddharth Samsi; Joseph McDonald; Lin Li; Connor W. Coley; Vijay Gadepally (CoRR, 2021)

Bringing Atomistic Deep Learning to Prime Time. - Nathan C. Frey; Siddharth Samsi; Bharath Ramsundar; Connor W. Coley; Vijay Gadepally (CoRR, 2021)

Benchmarking Resource Usage for Efficient Distributed Deep Learning. - Nathan C. Frey; Baolin Li; Joseph McDonald; Dan Zhao 007; Michael Jones 001; David Bestor; Devesh Tiwari; Vijay Gadepally; Siddharth Samsi (HPEC, 2022)

Energy-aware neural architecture selection and hyperparameter optimization. - Nathan C. Frey; Dan Zhao 007; Simon Axelrod; Michael Jones 001; David Bestor; Vijay Gadepally; Rafael Gómez-Bombarelli; Siddharth Samsi (IPDPS Workshops, 2022)

The MIT Supercloud Workload Classification Challenge. - Benny J. Tang; Qiqi Chen; Matthew L. Weiss; Nathan C. Frey; Joseph McDonald; David Bestor; Charles Yee; William Arcand; William Bergeron; Chansup Byun; Daniel Edelman; Michael Houle 001; et al. (IPDPS Workshops, 2022)

Loss Curve Approximations for Fast Neural Architecture Ranking & Training Elasticity Estimation. - Dan Zhao 007; Nathan C. Frey; Vijay Gadepally; Siddharth Samsi (IPDPS Workshops, 2022)

A Green(er) World for A.I. - Dan Zhao 007; Nathan C. Frey; Joseph McDonald; Matthew Hubbell; David Bestor; Michael Jones 001; Andrew Prout; Vijay Gadepally; Siddharth Samsi (IPDPS Workshops, 2022)

Great Power, Great Responsibility: Recommendations for Reducing Energy for Training Language Models. - Joseph McDonald; Baolin Li; Nathan C. Frey; Devesh Tiwari; Vijay Gadepally; Siddharth Samsi (NAACL-HLT (Findings), 2022)

FastFlows: Flow-Based Models for Molecular Graph Generation. - Nathan C. Frey; Vijay Gadepally; Bharath Ramsundar (CoRR, 2022)

The MIT Supercloud Workload Classification Challenge. - Benny J. Tang; Qiqi Chen; Matthew L. Weiss; Nathan C. Frey; Joseph McDonald; David Bestor; Charles Yee; William Arcand; Chansup Byun; Daniel Edelman; Matthew Hubbell; Michael Jones 001; et al. (CoRR, 2022)

A Pareto-optimal compositional energy-based model for sampling and optimization of protein sequences. - Natasa Tagasovska; Nathan C. Frey; Andreas Loukas; Isidro Hötzel; Julien Lafrance-Vanasse; Ryan Lewis Kelly; Yan Wu 027; Arvind Rajpal; Richard Bonneau; Kyunghyun Cho; Stephen Ra; Vladimir Gligorijevic (CoRR, 2022)

Graph Contrastive Learning for Materials. - Teddy Koker; Keegan Quigley; Will Spaeth; Nathan C. Frey; Lin Li (CoRR, 2022)

Roughness of Molecular Property Landscapes and Its Impact on Modellability. - Matteo Aldeghi; David E. Graff; Nathan C. Frey; Joseph A. Morrone; Edward O. Pyzer-Knapp; Kirk E. Jordan; Connor W. Coley (J. Chem. Inf. Model., 2022)

SELFIES and the future of molecular string representations. - Mario Krenn; Qianxiang Ai; Senja Barthel; Nessa Carson; Angelo Frei; Nathan C. Frey; Pascal Friederich; Théophile Gaudin; Alberto Alexander Gayle; Kevin Maik Jablonka; Rafael F. Lameiro; Dominik Lemm; et al. (Patterns, 2022)

Protein Design with Guided Discrete Diffusion. - Nate Gruver; Samuel Stanton; Nathan C. Frey; Tim G. J. Rudner; Isidro Hötzel; Julien Lafrance-Vanasse; Arvind Rajpal; Kyunghyun Cho; Andrew Gordon Wilson (NeurIPS, 2023)

SupSiam: Non-contrastive Auxiliary Loss for Learning from Molecular Conformers. - Michael Maser; Ji Won Park; Joshua Yao-Yu Lin; Jae Hyeon Lee; Nathan C. Frey; Andrew M. Watkins (CoRR, 2023)

Neural scaling of deep chemical models. - Nathan C. Frey; Ryan Soklaski; Simon Axelrod; Siddharth Samsi; Rafael Gómez-Bombarelli; Connor W. Coley; Vijay Gadepally (Nat. Mac. Intell., 2023)

Protein Discovery with Discrete Walk-Jump Sampling. - Nathan C. Frey; Daniel Berenberg; Karina Zadorozhny; Joseph Kleinhenz; Julien Lafrance-Vanasse; Isidro Hötzel; Yan Wu 027; Stephen Ra; Richard Bonneau; Kyunghyun Cho; Andreas Loukas; Vladimir Gligorijevic; et al. (ICLR, 2024)

Closed-Form Test Functions for Biophysical Sequence Optimization Algorithms. - Samuel Stanton; Robert G. Alberstein; Nathan C. Frey; Andrew M. Watkins; Kyunghyun Cho (CoRR, 2024)

Antibody DomainBed: Out-of-Distribution Generalization in Therapeutic Protein Design. - Natasa Tagasovska; Ji Won Park; Matthieu Kirchmeyer; Nathan C. Frey; Andrew Martin Watkins; Aya Abdelsalam Ismail; Arian Rokkum Jamasb; Edith Lee; Tyler Bryson; Stephen Ra; Kyunghyun Cho (CoRR, 2024)

LLMs are Highly-Constrained Biophysical Sequence Optimizers. - Angelica Chen; Samuel Don Stanton; Robert G. Alberstein; Andrew M. Watkins; Richard Bonneau; Vladimir Gligorijevic; Kyunghyun Cho; Nathan C. Frey (CoRR, 2024)

Concept Bottleneck Language Models For protein design. - Aya Abdelsalam Ismail; Tuomas P. Oikarinen; Amy Wang; Julius Adebayo; Samuel Stanton; Taylor Joren; Joseph Kleinhenz; Allen Goodman; Héctor Corrada Bravo; Kyunghyun Cho; Nathan C. Frey (CoRR, 2024)

Concept Bottleneck Language Models For Protein Design. - Aya Abdelsalam Ismail; Tuomas P. Oikarinen; Amy Wang; Julius Adebayo; Samuel Don Stanton; Héctor Corrada Bravo; Kyunghyun Cho; Nathan C. Frey (ICLR, 2025)

Strain Problems Got You in a Twist? Try StrainRelief: A Quantum-Accurate Tool for Ligand Strain Calculations. - Ewan R. S. Wallace; Nathan C. Frey; Joshua A. Rackers (J. Chem. Inf. Model., 2025)

Tokenized and continuous embedding compressions of protein sequence and structure. - Amy X. Lu; Wilson Yan; Kevin K. Yang; Vladimir Gligorijevic; Kyunghyun Cho; Pieter Abbeel; Richard Bonneau; Nathan C. Frey (Patterns, 2025)