nabla^2DFT: A Universal Quantum Chemistry Dataset of Drug-Like
  Molecules and a Benchmark for Neural Network Potentials
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.
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Kuzma Khrabrov
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2024-06-21T10:22:40.149Z
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Kuzma Khrabrov
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Anton Ber
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2024-06-21T10:22:48.347Z
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Anton
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Artem Tsypin
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2024-06-21T10:22:56.898Z
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Artem Tsypin
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Konstantin Ushenin
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2024-06-21T10:23:03.134Z
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Konstantin Ushenin
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kostaNew
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Egor Rumiantsev
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EgorRumiantsev
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EgorRum
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Alexander Telepov
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2024-06-21T10:23:13.760Z
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Alexander Telepov
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Dmitry Protasov
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2024-06-21T10:23:20.726Z
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Dmitry Protasov
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Ilya Shenbin
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Anton Alekseev
667536bfa845e4470f3a1909
Mikhail Shirokikh
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Sergey Nikolenko
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2024-06-21T10:23:37.613Z
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Sergey Nikolenko
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Elena Tutubalina
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2024-06-21T10:24:19.860Z
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Elena Tutubalina
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tlenusik
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Artur Kadurin
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2024-06-21T11:47:39.887Z
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Artur Kadurin
user
AKadurin
nabla^2DFT: A Universal Quantum Chemistry Dataset of Drug-Like
  Molecules and a Benchmark for Neural Network Potentials
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.
A new dataset and benchmark, $\nabla^2$DFT, enhances neural network potentials for molecular property prediction, Hamiltonian prediction, and conformational optimization with extensive, diverse molecular data.
neural network potentials nablaDFT molecular structures conformations molecular properties Hamiltonian overlap matrices wavefunction density functional theory relaxation trajectories drug-like molecules