Δ-Quantum machine-learning for medicinal chemistry
By Kenneth Atz, Clemens Isert, Markus N A Böcker, José Jiménez-Luna, Gisbert Schneider. 2021
37 citations recorded by the source corpus.
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.
Subfields: Computational Theory and Mathematics, Materials Chemistry, Physical and Theoretical Chemistry
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Citations in the corpus
- Enabling late-stage drug diversification by high-throughput experimentation with geometric deep learning (2023, methodology): “Additionally, for molecular graphs that contained electronic features, the atomic partial charges were calculated on the fly using DelFTa software 66–68 , obtaining DFT-level (ωB97X-D/def2-SVP (refs.”
- Machine Learning for Fast, Quantum Mechanics-Based Approximation of Drug Lipophilicity (2023, unknown): “77,96,97 The higher predictive accuracy of Chemprop models over the classical ML models only manifests itself at training set sizes above ∼ 1000 data points, indicative of the commonly observed behavior of deep learning models requiring a sufficient amount of training data for good modeling…”