Details

Autor(en) / Beteiligte

• Atz, Kenneth
• Isert, Clemens
• Böcker, Markus N. A
• Jiménez-Luna, José
• Schneider, Gisbert

Titel

Δ-Quantum machine-learning for medicinal chemistry

Ist Teil von

• Physical chemistry chemical physics : PCCP, 2022-05, Vol.24 (18), p.1775-1783

Ort / Verlag

England: Royal Society of Chemistry

Erscheinungsjahr

2022

Quelle

MEDLINE

Beschreibungen/Notizen

• 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. 3D message-passing neural networks for Δ-quantum machine-learning enable fast access to DFT-level QM properties for drug-like molecules.