5 research outputs found
SchNetPack 2.0: A neural network toolbox for atomistic machine learning
SchNetPack is a versatile neural networks toolbox that addresses both the
requirements of method development and application of atomistic machine
learning. Version 2.0 comes with an improved data pipeline, modules for
equivariant neural networks as well as a PyTorch implementation of molecular
dynamics. An optional integration with PyTorch Lightning and the Hydra
configuration framework powers a flexible command-line interface. This makes
SchNetPack 2.0 easily extendable with custom code and ready for complex
training task such as generation of 3d molecular structures
Pretrained models: "Inverse design of 3d molecular structures with conditional generative neural networks"
The rational design of molecules with desired properties is a long-standing challenge in chemistry. Generative neural networks have emerged as a powerful approach to sample novel molecules from a learned distribution. Here, we propose a conditional generative neural network for 3d molecular structures with specified chemical and structural properties. This approach is agnostic to chemical bonding and enables targeted sampling of novel molecules from conditional distributions, even in domains where reference calculations are sparse. We demonstrate the utility of our method for inverse design by generating molecules with specified motifs or composition, discovering particularly stable molecules, and jointly targeting multiple electronic properties beyond the training regime.BMBF, 01IS18037A, Verbundprojekt BIFOLD-BZML: Berlin Institute for the Foundations of Learning and DataBASLEARN – TU Berlin/BASF Joint Lab for Machine Learnin
Generated molecules: "Inverse design of 3d molecular structures with conditional generative neural networks"
The rational design of molecules with desired properties is a long-standing challenge in chemistry. Generative neural networks have emerged as a powerful approach to sample novel molecules from a learned distribution. Here, we propose a conditional generative neural network for 3d molecular structures with specified chemical and structural properties. This approach is agnostic to chemical bonding and enables targeted sampling of novel molecules from conditional distributions, even in domains where reference calculations are sparse. We demonstrate the utility of our method for inverse design by generating molecules with specified motifs or composition, discovering particularly stable molecules, and jointly targeting multiple electronic properties beyond the training regime.BMBF, 01IS18037A, Verbundprojekt BIFOLD-BZML: Berlin Institute for the Foundations of Learning and DataBASLEARN – TU Berlin/BASF Joint Lab for Machine Learnin