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Found 8 papers, 6 papers with code
MIST-CF: Chemical formula inference from tandem mass spectra
Importantly, MIST-CF learns in a data dependent fashion using a Formula Transformer neural network architecture and circumvents the need for fragmentation tree construction.
Single-model uncertainty quantification in neural network potentials does not consistently outperform model ensembles
In this work, we examine multiple UQ schemes for improving the robustness of NN interatomic potentials (NNIPs) through active learning.
Generating Molecular Fragmentation Graphs with Autoregressive Neural Networks
The accurate prediction of tandem mass spectra from molecular structures has the potential to unlock new metabolomic discoveries by augmenting the community's libraries of experimental reference standards.
Prefix-Tree Decoding for Predicting Mass Spectra from Molecules
Computational predictions of mass spectra from molecules have enabled the discovery of clinically relevant metabolites.
FLIP: Benchmark tasks in fitness landscape inference for proteins
Machine learning could enable an unprecedented level of control in protein engineering for therapeutic and industrial applications.
Machine learning modeling of family wide enzyme-substrate specificity screens
However, the adoption of biocatalysis is limited by our ability to select enzymes that will catalyze their natural chemical transformation on non-natural substrates.
Mol2Image: Improved Conditional Flow Models for Molecule to Image Synthesis
In this paper, we aim to synthesize cell microscopy images under different molecular interventions, motivated by practical applications to drug development.
Improved Conditional Flow Models for Molecule to Image Synthesis
In this paper, we aim to synthesize cell microscopy images under different molecular interventions, motivated by practical applications to drug development.
