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content type paperpublished September 2026 SimpleDesign: A Joint Model for Protein Sequence and Structure Codesign AuthorsJiarui Lu†, Yuyang Wang, Yizhe Zhang, Jiatao Gu, Navdeep Jaitly, Joshua M. Susskind, Miguel Ángel Bautista View publication Proteins are fundamental to biological processes, with their function determined by the complex interplay between the amino acid sequence and the three-dimensional structure. Developing generative models capable of understanding this intrinsically multi-modal relationship is crucial for fields like drug discovery and protein engineering. Existing models often rely on a multi-stage training process where autoencoders that tokenize data into latent representations are trained in a first stage. Secondly, a generative model is trained on the latent representation of the autoencoder(s), i.e. generative modeling in a latent space. We hypothesize that this multi-stage training is not necessary to obtain performant co-design models and thus present SimpleDesign, an effective multi-modal protein design model trained directly in the data space. SimpleDesign leverages a single-stage end-to-end objective that combines discrete cross-entropy for sequences and a regression objective for structures. In order to effectively model the difference in sequence and structure modalities, we instantiate the framework with Transformer-based multimodal backbones that allows modality-specific processing while keeping global self-attention over both modalities. We train SimpleDesign on over 2M sequence-structure pairs achieving competitive performance across co-design and unconditional sequence/structure generation benchmarks. † Mila, Université de Montréal ** Work done while at Apple SimpleFold: Folding Proteins is Simpler than You Think September 24, 2025research area Methods and Algorithmsconference ICLR, conference ICML Protein folding models have achieved groundbreaking results since the introduction of AlphaFold2, typically built via a combination of integrating domain-expertise into its architectural designs and training pipelines. Nonetheless, given the success of generative models across different but related problems, it is natural to question whether these architectural designs are a necessity to build performant models. In this paper, we introduce… Read more INRFlow: Flow Matching for INRs in Ambient Space June 27, 2025research area Methods and Algorithmsconference ICML Flow matching models have emerged as a powerful method for generative modeling on domains like images or videos, and even on irregular or unstructured data like 3D point clouds or even protein structures. These models are commonly trained in two stages: first, a data compressor is trained, and in a subsequent training stage a flow matching generative model is trained in the latent space of the data compressor. This two-stage paradigm sets… Read more