[HTML][HTML] Challenges and compromises: Predicting unbound antibody structures with deep learning

A Greenshields-Watson, O Vavourakis… - Current Opinion in …, 2025 - Elsevier
Therapeutic antibodies are manufactured, stored and administered in the free state; this
makes understanding the unbound form key to designing and improving development …

Scalable emulation of protein equilibrium ensembles with generative deep learning

S Lewis, T Hempel, J Jiménez Luna, M Gastegger… - bioRxiv, 2024 - biorxiv.org
Following the sequence and structure revolutions, predicting the dynamical mechanisms of
proteins that implement biological function remains an outstanding scientific challenge …

Outsourced diffusion sampling: Efficient posterior inference in latent spaces of generative models

S Venkatraman, M Hasan, M Kim, L Scimeca… - arxiv preprint arxiv …, 2025 - arxiv.org
Any well-behaved generative model over a variable $\mathbf {x} $ can be expressed as a
deterministic transformation of an exogenous ('outsourced') Gaussian noise variable …

Reinforcement Learning for Sequence Design Leveraging Protein Language Models

J Subramanian, S Sujit, N Irtisam, U Sain… - arxiv preprint arxiv …, 2024 - arxiv.org
Protein sequence design, determined by amino acid sequences, are essential to protein
engineering problems in drug discovery. Prior approaches have resorted to evolutionary …

Generative Flow Networks: Theory and Applications to Structure Learning

T Deleu - arxiv preprint arxiv:2501.05498, 2025 - arxiv.org
Without any assumptions about data generation, multiple causal models may explain our
observations equally well. To avoid selecting a single arbitrary model that could result in …