Daniel Jesus Diaz — accepted AI-conference papers

2026

Triangle Multiplication is All You Need for Biomolecular Structure Representations

Jeffrey Ouyang-Zhang, Pranav Murugan, Daniel Jesus Diaz, Gianluca Scarpellini, Richard Strong Bowen, Nate Gruver +4

ICLR 2026poster

AlphaFold has transformed protein structure prediction, but emerging applications such as virtual ligand screening, proteome-wide folding, and de novo binder design demand predictions at a massive scale, where runtime and memory costs become prohibitive. A major bottleneck lies in the Pairformer bac…

Cited by 0 Source ↗code ↗Scholar ↗

2025

Ambient Proteins - Training Diffusion Models on Noisy Structures

Giannis Daras, Jeffrey Ouyang-Zhang, Krithika Ravishankar, Constantinos Costis Daskalakis, Adam Klivans, Daniel Jesus Diaz

NeurIPS 2025spotlight

We present Ambient Protein Diffusion, a framework for training protein diffusion models that generates structures with unprecedented diversity and quality. State-of-the-art generative models are trained on computationally derived structures from AlphaFold2 (AF), as experimentally determined structur…

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2025

Distilling Structural Representations into Protein Sequence Models

Jeffrey Ouyang-Zhang, Chengyue Gong, Yue Zhao, Philipp Kraehenbuehl, Adam Klivans, Daniel Jesus Diaz

ICLR 2025poster

Protein language (or sequence) models, like the popular ESM2, are now widely used tools for extracting evolution-based protein representations and have achieved significant success on core downstream biological tasks. A major open problem is how to obtain representations that best capture both the s…

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2024

Evolution-Inspired Loss Functions for Protein Representation Learning

Chengyue Gong, Adam Klivans, James Madigan Loy, Tianlong Chen, qiang liu, Daniel Jesus Diaz

ICML 2024poster

AI-based frameworks for protein engineering use self-supervised learning (SSL) to obtain representations for downstream mutation effect predictions. The most common training objective for these methods is wildtype accuracy: given a sequence or structure where a wildtype residue has been masked, pred…

Cited by 6 Source ↗PDF ↗Scholar ↗

2023

HotProtein: A Novel Framework for Protein Thermostability Prediction and Editing

Tianlong Chen, Chengyue Gong, Daniel Jesus Diaz, Xuxi Chen, Jordan Tyler Wells, qiang liu +4

ICLR 2023poster

The molecular basis of protein thermal stability is only partially understood and has major significance for drug and vaccine discovery. The lack of datasets and standardized benchmarks considerably limits learning-based discovery methods. We present \texttt{HotProtein}, a large-scale protein datas…

Cited by 27 Source ↗PDF ↗code ↗Scholar ↗

2023

Predicting a Protein's Stability under a Million Mutations

Jeffrey Ouyang-Zhang, Daniel Jesus Diaz, Adam Klivans, Philipp Kraehenbuehl

NeurIPS 2023poster

Stabilizing proteins is a foundational step in protein engineering. However, the evolutionary pressure of all extant proteins makes identifying the scarce number of mutations that will improve thermodynamic stability challenging. Deep learning has recently emerged as a powerful tool for identifying…

Cited by 19 Source ↗PDF ↗code ↗Scholar ↗