Journal: ChemRxiv

Abbreviation

Publisher

American Chemical Society

Journal Volumes

ISSN

2573-2293

Description

Filters

2019 - 201922020 - 20253
Reset filters

Search Results

Publications 1 - 5 of 5
  • Nano-3D-printed Photochromic Objects
    Item type: Working Paper
    Ulrich, Sebastian; Wang, Xiaopu; Rottmar, Markus; et al. (2020)
    ChemRxiv
    A new class of photoresist is described for direct laser writing of photoswitchable 3D microstructures. The material comprising off-stoichiometric thiol-ene photo-clickable resins enables rapid two-photon laser processing of highly complex structures and facile post-modification with photoswitches. The microstructures were functionalized with a series of donor-acceptor Stenhouse adducts (DASAs) photoswitches with different excitation wavelength. The versatility of thiol–ene photo-click reaction enabled fine-tuning of the network structure and physical properties as well as the type and concentration of DASA photoswitches. When exposed to visible light, these microstructures exhibit excellent photo-responsiveness and undergo reversible color-changing via photoisomerization of DASA moieties. We describe that the weak fluorescence of DASAs can be used as a reporter of photoswitching, color changes, and thermal recovery, allowing the reading of DASA-containing sub-micrometric structures in 3D. This work delivers a new approach for custom microfabrication of 3D photochromic objects with molecularly engineered color and responsiveness.
  • Generating customized compound libraries for drug discovery with machine intelligence
    Item type: Working Paper
    Moret, Michael; Friedrich, Lukas; Grisoni, Francesca; et al. (2019)
    ChemRxiv
    Generative machine learning models sample drug-like molecules from chemical space without the need for explicit design rules. A deep learning framework for customized compound library generation is presented, aiming to enrich and expand the pharmacologically relevant chemical space with new molecular entities 'on demand'. This de novo design approach was used to generate molecules that combine features from bioactive synthetic compounds and natural products, which are a primary source of inspiration for drug discovery. The results show that the data-driven machine intelligence acquires implicit chemical knwoledge and generates novel molecules with bespoke properties and structural diversity. The method is available as an open-access tool for medicinal and bioorganic chemistry.
  • Transferring Knowledge from MM to QM: A Graph Neural Network-Based Implicit Solvent Model for Small Organic Molecules
    Item type: Working Paper
    Katzberger, Paul; Pultar, Felix; Riniker, Sereina; et al. (2025)
    ChemRxiv
    The conformational ensemble of a molecule is strongly influenced by the surrounding environment. Correctly modeling the effect of any given environment is, hence, of pivotal importance in computational studies. Machine learning (ML) has been shown to be able to model these interactions probabilistically, with successful applications demonstrated for classical molecular dynamics. While first instances of ML implicit solvents for quantum-mechanical (QM) calculations exist, the high computational cost of QM reference calculations hinders the development of a generally applicable ML implicit solvent model for QM calculations. Here, we present a novel way of developing such a general machine-learned QM implicit solvent model, which relies neither on QM reference calculations for training nor experimental data, by transferring knowledge obtained from classical interactions to QM. This strategy makes the obtained graph neural network (GNN) based implicit solvent model (termed QM-GNNIS) independent of the chosen functional and basis set. The performance of QM-GNNIS is validated on NMR and IR experiments, demonstrating that the approach can reproduce experimentally observed trends unattainable by state-of-the-art implicit-solvent models.
  • Discontinuous Behavior of the Pauli Potential in Density Functional Theory as a Function of the Electron Number
    Item type: Working Paper
    Kraisler, Eli; Schild, Axel (2019)
    ChemRxiv
  • Stepwise and Reversible Assembly of 2Fe–2S Rhombs to 8Fe–8S Clusters and Their Topological Interconversions
    Item type: Working Paper
    Grunwald, Liam; Weber, Micha; Seng, Henrik; et al. (2024)
    ChemRxiv
    Among all enzymatic metallocofactors, those found in nitrogenases, the P- and L-/M-clusters, stand out for their structural complexity. They are assembled by proteins of the Nif gene cluster from Fe2S2 rhombs—the smallest building blocks in FeS cluster chemistry—through a sequence of reactions constructing a Fe8S8 precursor. This fundamental transformation is unknown in chemical synthesis, impeding our understanding of how enzymes selectively build such elaborate inorganic molecules. Here, we report the rational stepwise assembly of [Fe8S8]n+ (n=2,4,6) clusters from [Fe2S2]2+ rhombs, within an extensive cyclic synthetic network. We identify a [Fe8S8]4+ cluster of unique topology, for which we coin the term “interlocked” double-cubane (ildc). This topology is not unprecedented in enzymes, as the ildc is a molecular analogue of the K-cluster, a proposed biosynthetic precursor to both the P- and M-clusters. Its synthesis, along with the characterization of all related intermediates, offers key insights into the mechanisms governing the assembly of these cofactors, advancing our understanding of both enzymatic and synthetic FeS cluster construction.
Publications 1 - 5 of 5