Therapeutic peptide representations
Developing SMILES-based and structural-ensemble representations for therapeutic peptides with an end-goal of creating novel designs.
Computational biochemistry · Austin, Texas
Teaching machines to reason about molecules.
I am Aaron Feller, Charles W. Smith Jr. Graduate Fellow at The University of Texas at Austin. I build deep learning systems for biological and therapeutic discovery.
Peptide design
Protein engineering
Molecular discovery
PhD Candidate
UT Austin
01 / About
Designing models tailored for molecular understanding.
I specialize in the development of deep learning architectures to guide molecular design. My experience spans therapeutic peptide design, protein engineering, and natural product discovery, where I develop modeling approaches that accurately represent complex physical systems.
I have learned that developing algorithms for biological data requires careful consideration of the underlying reality. Only by bridging the gap between biological intuition and machine learning can we design architectures that drive real discovery.
02 / Research
From molecule
to model.
Protein engineering
Evaluating and adapting protein foundation models using deep mutational scanning data to engineer proteins for targeted tasks.
Molecular discovery
Using chemical embeddings and genome-scale bioinformatics to discover useful small molecules, natural products, and antibiotic peptide scaffolds.
03 / Publications
Research in print
A selection of peer-reviewed work spanning molecular machine learning, peptide chemistry, and natural product discovery.
Google Scholar- 2026StabilityArc: Decoding Protein Sequence Embeddings into Generalizable Stability LandscapesNeurIPS Workshop on Representations for Physical Sciences
- 2026EnsembleEGNN: Set-Based Graph Learning for Thermodynamic Ensembles of Cyclic PeptidesICML Workshop on Graph Foundation Models
- 2026Scaling SMILES-Based Chemical Language Models for Therapeutic Peptide EngineeringJournal of Chemical Information and Modeling
- 2026Inherent Limitations of Zero-shot Mutation Prediction in Protein EngineeringSubmitted to Patterns (Cell Press)
- 2025Peptide-Aware Chemical Language Model Successfully Predicts Membrane Diffusion of Cyclic PeptidesJournal of Chemical Information and Modeling
- 2025Antibacterial Microcins are Ubiquitous and Functionally Diverse Across Bacterial CommunitiesNature Communications
- 2023Using Alternative SMILES Representations to Identify Novel Functional AnaloguesPatterns (Cell Press)
04 / In the lab & beyond
Science happens in community.
2022 — Present
Graduate Research Assistant
The University of Texas at AustinAdvancing AI-guided protein engineering with the Wilke Lab and collaborators across computational biology and molecular engineering.
2024 - Present
Research Collaboration
Novo Nordisk A/SContributed to research on peptide-specific molecular representations and predictive modeling for therapeutic peptide engineering.
2023 — 2026
Co-Founder & Organizer
Biology & Machine Learning SocietyBuilt a student-led forum for AI in biology and organized an international protein engineering hackathon with more than 40 teams.
2025 — Present
Community Contributions
Volunteered TimeContributing to and reviewing for Terminal Bench Science, supporting data analysis for Nucleate HQ, and mentoring post-bacc students.
05 / Toolkit
Methods & tools
01
Modeling
Transformers · GNNs · Transfer Learning · Meta Learning · Probabilistic Modeling · Model Evaluation
02
Scientific computing
Python · PyTorch · Lightning · RDKit · NumPy · Pandas · R
03
Infrastructure
HPC · Slurm · TACC · AWS · git · reproducible data pipelines
04
Communication
Technical writing · LaTeX · teaching · scientific visualization · conversational Spanish