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  1. Journal of Chemical Theory and Computation

    High-barrier isomerization beyond the free-energy surface: AIMMD's symbolic committor reveals a stepwise S-shaped path in retinal and p-coumaric acid

    In high-barrier cis-trans isomerization the committor, the probability that a configuration reaches product before reactant, is nearly a step function with intermediate values only in a narrow band around the transition state. Combining AIMMD path sampling with symbolic regression, researchers learn the committor from unbiased molecular dynamics trajectories for N-retinylidene-lysine and the p-coumaric acid anion in vacuo and distill it into closed-form expressions. Four proper dihedrals around the reactive bond must couple nonlinearly to reproduce the S-shaped stepwise path seen in the transition-path ensemble, whereas the minimum free-energy path is a straight line.

    Freie Universität BerlinGoethe University FrankfurtUniversity of Trento
    Kai Töpfer（First author）Bettina G Keller（Corresponding author）Roberto Covino（Corresponding author）
    AIMMDCommittor LearningSymbolic RegressionCis-Trans Isomerization
  2. Nature Computational Science

    Excited-state surfaces without point-by-point solves: transferable deep quantum Monte Carlo cuts cost by up to two orders of magnitude

    Molecular photochemistry needs entire ground- and excited-state potential energy surfaces, yet solving the electronic Schrödinger equation point by point is expensive, and cheap single-reference methods fail at conical intersections. Transferable deep quantum Monte Carlo optimizes one neural-network wavefunction jointly across many molecular geometries and electronic states, sharing parameters between states and ordering them dynamically by per-geometry energy. On ethylene, the carbon dimer, the methylenimmonium cation and a 96-electron rubredoxin active-site model, it reproduces tailored quantum-chemistry references while cutting cost by up to about two orders of magnitude relative to single-point calculations.

    Rice UniversityFreie Universität Berlin
    Zeno Schätzle（First author）Frank Noé（Corresponding author）
    Transferable Deep-Learning Quantum Monte CarloExcited-State Potential Energy SurfacesVariational Monte CarloDynamic State OrderingNeural Network Wavefunction
  3. medRxiv

    Ewing sarcoma survival splits by neighborhood, not mutation: a 135-patient spatial map yields four prognostic types, with interferon-reactive tumors faring best

    Ewing sarcoma is genomically near-uniform yet patients fare very differently. Single-cell and spatial transcriptomics of tumors and their microenvironment in 135 patients identified five tumor-cell transcriptional states and eight niches defined by neighboring cell configurations, and niche composition sorted patients into Fusion Active, Proliferative, Immune Reactive and TME-enriched types. The Immune Reactive type, marked by interferon-activated tumor cells, had favorable survival, and the survival differences reproduced in two independent bulk transcriptomic cohorts.

    Charité - Universitätsmedizin BerlinUniversity Hospital HeidelbergUniversity Hospital MünsterHumboldt-Universität zu BerlinHospital Universitario La PazHeinrich Heine University DüsseldorfEssen University HospitalHeidelberg University
    Heng Luo（First author）Moritz Gerstung（Corresponding author）
    Spatial Niche OrganizationSpatial transcriptomicsEwing SarcomaIntratumor Heterogeneity
  4. Nature Methods

    Spatial proteomics gets an open-source path from whole-slide image to mass spec, with neighborhood sampling about 100 times faster

    Deep visual proteomics (DVP) lacked an open-source end-to-end workflow, which limited adoption and systematic benchmarking. openDVP links image processing, cell phenotyping, laser microdissection and mass-spectrometry proteomics into a modular pipeline, with a fast-track route that needs no image-analysis expertise. Across archival placenta, early lung cancer and relapsed breast cancer tissue, neighborhood-guided sampling collected tissue about 100 times faster than single-cell cutting and raised protein identifications by an average of 26%.

    Charité - Universitätsmedizin BerlinHumboldt-Universität zu BerlinFreie Universität BerlinLudwig-Maximilians-Universität München
    Jose Nimo（First author）Fabian Coscia（Corresponding author）
    Cellular Neighborhood-Guided ProteomicsMass Spectrometry ProteomicsDeep Visual Proteomics
  5. arXiv

    CCNN built on cup and cap products matches other simplicial complex neural networks on TopoBench

    Addressing the limitation that most topological deep learning approaches, being based on boundary operators and Hodge Laplacians, cannot lift or lower features and signals across more than one dimension per layer, this work proposes the adoption of cup and cap products and formulates the Cup and Cap Topological Neural Network (CCNN), a topological deep learning architecture designed to learn node-based variables (0-cochains) while accounting for many-body interactions such as triangles in the data, validated on the TopoBench datasets where it shows competitiveness with respect to other simplicial complex neural networks.

    University of CambridgeSapienza University of RomeFreie Universität Berlin
    Marta Niedostatek（First author）
    Cup and Cap Topological Neural NetworkTopological Deep Learning

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