A Geometric Deep Learning Framework for 3D Biomolecular Compression, Generation, and Accelerated Simulation
Name
Costa-allanc-PhD-MAS-2025-thesis.pdf
Size
47.12 MB
Format
Adobe PDF
Checksum (MD5)
58d5d1381f84dd028a12e4f19ed06e27
Author(s)
dos Santos Costa, Allan
Advisor(s)
Jacobson, Joseph
Date Issued
February 2026
Publisher
Massachusetts Institute of Technology
Abstract
Biomolecules organize over vast scales of length and time, forming the bridge from physical principles to functional complexity. While this layered structure defies classical bioinformatics and traditional simulation approaches, breakthroughs in deep learning suggest a promising path for tackling the multi-resolution organization and dynamics of biomolecules. In this thesis, we advance these methods with geometric neural architectures and generative algorithms for learning 3D biomolecular data at coarser, more tractable and effective spatiotemporal scales. Leveraging the physical symmetries of 3D molecules, we draw on irreducible representations of the Euclidean group to construct coarse atomistic encodings at the protein or RNA residue level. From these representations, we build a suite of Euclidean-equivariant neural networks that enable generation, design, and accelerated simulation. First, we introduce autoencoders for deep protein coarse-graining and compression, uncovering structured latent spaces conducive to efficient generation. Then, we explore a multimodal model for co-generating sequence and all-atom structure of RNA, enabling efficient, simultaneous optimization in structural design. Finally, we train large-jump transfer operators for accelerated protein dynamics simulation, profiling quality tradeoffs. We show that the learned simulators generalize with kinetic accuracy across proteins, while achieving orders-of-magnitude speedup over traditional methods. Taken together, these efforts advance a paradigm grounded in physical symmetries for treating 3D biomolecular data, establishing foundations for efficient exploration of vast molecular design spaces and accessible simulation of dynamics.
MIT Department
Program in Media Arts and Sciences (Massachusetts Institute of Technology)
Terms of Use
In Copyright - Educational Use Permitted
Copyright retained by author(s)
Persistent DSpace Link
