Towards foundational models for molecular learning on large-scale multi-task datasets
Recently, pre-trained foundation models have enabled significant advancements in multiple
fields. In molecular machine learning, however, where datasets are often hand-curated, and …
fields. In molecular machine learning, however, where datasets are often hand-curated, and …
Molecular quantum chemical data sets and databases for machine learning potentials
The field of computational chemistry is increasingly leveraging machine learning (ML)
potentials to predict molecular properties with high accuracy and efficiency, providing a …
potentials to predict molecular properties with high accuracy and efficiency, providing a …
Quantum-informed molecular representation learning enhancing ADMET property prediction
We examined pretraining tasks leveraging abundant labeled data to effectively enhance
molecular representation learning in downstream tasks, specifically emphasizing graph …
molecular representation learning in downstream tasks, specifically emphasizing graph …
Reducing the cost of quantum chemical data by backpropagating through density functional theory
A Mathiasen, H Helal, P Balanca, A Krzywaniak… - arXiv preprint arXiv …, 2024 - arxiv.org
Density Functional Theory (DFT) accurately predicts the quantum chemical properties of
molecules, but scales as $ O (N_ {\text {electrons}}^ 3) $. Sch\" utt et al.(2019) successfully …
molecules, but scales as $ O (N_ {\text {electrons}}^ 3) $. Sch\" utt et al.(2019) successfully …
DFT: 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 …
properties crucial for computer-aided drug discovery and other areas of chemical science …