nabla^2DFT：一個包含類似藥物分子的通用量子化學數據集，並作為神經網絡潛力的基準。

nabla^2DFT: A Universal Quantum Chemistry Dataset of Drug-Like Molecules and a Benchmark for Neural Network Potentials

June 20, 2024

作者: Kuzma Khrabrov, Anton Ber, Artem Tsypin, Konstantin Ushenin, Egor Rumiantsev, Alexander Telepov, Dmitry Protasov, Ilya Shenbin, Anton Alekseev, Mikhail Shirokikh, Sergey Nikolenko, Elena Tutubalina, Artur Kadurin

cs.AI

摘要

計算量子化學方法提供了準確的分子性質近似值，對於計算輔助藥物發現和化學科學的其他領域至關重要。然而，高計算複雜度限制了其應用的可擴展性。神經網絡勢（NNPs）是量子化學方法的一個有前途的替代方案，但它們需要大量和多樣化的數據集進行訓練。本研究提出了一個基於nablaDFT的新數據集和基準，名為nabla^2DFT。它包含兩倍於分子結構、三倍於構象、新的數據類型和任務，以及最先進的模型。該數據集包括能量、力、17個分子性質、哈密頓和重疊矩陣，以及一個波函數對象。所有計算均在每個構象的DFT水平（omegaB97X-D/def2-SVP）下進行。此外，nabla^2DFT是第一個包含大量類似藥物分子鬆弛軌跡的數據集。我們還引入了一個新的基準，用於評估NNPs在分子性質預測、哈密頓預測和構象優化任務中的表現。最後，我們提出了一個可擴展的框架，用於訓練NNPs，並在其中實現了10個模型。

English

Methods of computational quantum chemistry provide accurate approximations of molecular properties crucial for computer-aided drug discovery and other areas of chemical science. However, high computational complexity limits the scalability of their applications. Neural network potentials (NNPs) are a promising alternative to quantum chemistry methods, but they require large and diverse datasets for training. This work presents a new dataset and benchmark called nabla^2DFT that is based on the nablaDFT. It contains twice as much molecular structures, three times more conformations, new data types and tasks, and state-of-the-art models. The dataset includes energies, forces, 17 molecular properties, Hamiltonian and overlap matrices, and a wavefunction object. All calculations were performed at the DFT level (omegaB97X-D/def2-SVP) for each conformation. Moreover, nabla^2DFT is the first dataset that contains relaxation trajectories for a substantial number of drug-like molecules. We also introduce a novel benchmark for evaluating NNPs in molecular property prediction, Hamiltonian prediction, and conformational optimization tasks. Finally, we propose an extendable framework for training NNPs and implement 10 models within it.