We introduce a design principle to stabilize helically chiral structures from an achiral tetrasubstituted [2.2]paracyclophane by integrating it into a macrocycle. The [2.2]paracyclophane introduces a three-dimensional perturbation into a nearly planar macrocyclic oligothiophene. The resulting helical structure is stabilized by two bulky substituents installed on the [2.2]paracyclophane unit. The increased enantiomerization barrier enabled the separation of both enantiomers. The synthesis of the target helical macrocycle 1 involves a sequence of halogenation and cross-coupling steps and a high-dilution strategy to close the macrocycle. Substituents tuning the energy of the enantiomerization process can be introduced in the last steps of the synthesis. The chiral target compound 1 was fully characterized by NMR spectroscopy and mass spectrometry. The absolute configurations of the isolated enantiomers were assigned by comparing the data of circular dichroism spectroscopy with TD-DFT calculations. The enantiomerization dynamics was studied by dynamic HPLC and variable-temperature 2D exchange spectroscopy and supported by quantum-chemical calculations.
Abstract Identifying druggable ligand‐binding sites on the surface of the macromolecular targets is an important process in structure‐based drug discovery. Deep‐learning models have been shown to successfully predict ligand‐binding sites of proteins. As a step toward predicting binding sites in RNA and RNA‐protein complexes, we employ three‐dimensional convolutional neural networks. We introduce a dataset splitting approach to minimize structure‐related bias in training data, and investigate the influence of protein‐based neural network pre‐training before fine‐tuning on RNA structures. Models that were pre‐trained on proteins considerably outperformed the models that were trained exclusively on RNA structures. Overall, 71 % of the known RNA binding sites were correctly located within 4 Å of their true centres.
Many molecular design tasks benefit from fast and accurate calculations of quantum-mechanical (QM) properties. However, the computational cost of QM methods applied to drug-like molecules currently renders large-scale applications of quantum chemistry challenging. Aiming to mitigate this problem, we developed DelFTa, an open-source toolbox for the prediction of electronic properties of drug-like molecules at the density functional (DFT) level of theory, using Δ-machine-learning. Δ-Learning corrects the prediction error (Δ) of a fast but inaccurate property calculation. DelFTa employs state-of-the-art three-dimensional message-passing neural networks trained on a large dataset of QM properties. It provides access to a wide array of quantum observables on the molecular, atomic and bond levels by predicting approximations to DFT values from a low-cost semiempirical baseline. Δ-Learning outperformed its direct-learning counterpart for most of the considered QM endpoints. The results suggest that predictions for non-covalent intra- and intermolecular interactions can be extrapolated to larger biomolecular systems. The software is fully open-sourced and features documented command-line and Python APIs.
Many molecular design tasks benefit from fast and accurate calculations of quantum-mechanical (QM) properties. However, the computational cost of QM methods applied to drug-like molecules currently renders large-scale applications of quantum chemistry challenging. Aiming to mitigate this problem, we developed DelFTa, an open-source toolbox for the prediction of electronic properties of drug-like molecules at the density functional (DFT) level of theory, using Δ-machine-learning. Δ-Learning corrects the prediction error (Δ) of a fast but inaccurate property calculation. DelFTa employs state-of-the-art three-dimensional message-passing neural networks trained on a large dataset of QM properties. It provides access to a wide array of quantum observables on the molecular, atomic and bond levels by predicting approximations to DFT values from a low-cost semiempirical baseline. Δ-Learning outperformed its direct-learning counterpart for most of the considered QM endpoints. The results suggest that predictions for non-covalent intra- and intermolecular interactions can be extrapolated to larger biomolecular systems. The software is fully open-sourced and features documented command-line and Python APIs.
Certain molecular design tasks benefit from fast and accurate calculations of quantum-mechanical (QM) properties. However, the computational cost of QM methods applied to drug-like compounds currently makes large-scale applications of quantum chemistry challenging. In order to mitigate this problem, we developed DelFTa, an open-source toolbox for predicting small-molecule electronic properties at the density functional (DFT) level of theory, using the Δ-machine learning principle. DelFTa employs state-of-the-art E(3)-equivariant graph neural networks that were trained on the QMugs dataset of QM properties. It provides access to a wide array of quantum observables by predicting approximations to ωB97X-D/def2-SVP values from a GFN2-xTB semiempirical baseline. Δ-learning with DelFTa was shown to outperform direct DFT learning for most of the considered QM endpoints. The software is provided as open-source code with fully-documented command-line and Python APIs.
Abstract Cell‐free enzymatic assays are highly useful tools in early compound profiling due to their robustness and scalability. However, their inadequacy to reflect the complexity of target engagement in a cellular environment may lead to a significantly divergent pharmacology that is eventually observed in cells. The discrepancy that emerges from properties like permeability and unspecific protein binding may largely mislead lead compound selection to undergo further chemical optimization. We report the development of a new intracellular NanoBRET assay to assess MAGL inhibition in live cells. Based on a reverse design approach, a highly potent, reversible preclinical inhibitor was conjugated to the cell‐permeable BODIPY590 acceptor fluorophore while retaining its overall balanced properties. An engineered MAGL‐nanoluciferase (Nluc) fusion protein provided a suitable donor counterpart for the facile interrogation of intracellular ligand activity. Validation of assay conditions using a selection of known MAGL inhibitors set the stage for the evaluation of over 1’900 MAGL drug candidates derived from our discovery program. This evaluation enabled us to select compounds for further development based not only on target engagement, but also on favorable physicochemical parameters like permeability and protein binding. This study highlights the advantages of cell‐based target engagement assays for accelerating compound profiling and progress at the early stages of drug discovery programs.
Abstract Machine learning approaches in drug discovery, as well as in other areas of the chemical sciences, benefit from curated datasets of physical molecular properties. However, there currently is a lack of data collections featuring large bioactive molecules alongside first-principle quantum chemical information. The open-access QMugs (Quantum-Mechanical Properties of Drug-like Molecules) dataset fills this void. The QMugs collection comprises quantum mechanical properties of more than 665 k biologically and pharmacologically relevant molecules extracted from the ChEMBL database, totaling ~2 M conformers. QMugs contains optimized molecular geometries and thermodynamic data obtained via the semi-empirical method GFN2-xTB. Atomic and molecular properties are provided on both the GFN2-xTB and on the density-functional levels of theory (DFT, ω B97X-D/def2-SVP). QMugs features molecules of significantly larger size than previously-reported collections and comprises their respective quantum mechanical wave functions, including DFT density and orbital matrices. This dataset is intended to facilitate the development of models that learn from molecular data on different levels of theory while also providing insight into the corresponding relationships between molecular structure and biological activity.
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