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Convolutional Networks on Graphs for Learning Molecular Fingerprints
A convolutional neural network that operates directly on graphs that allows end-to-end learning of prediction pipelines whose inputs are graphs of arbitrary size and shape is introduced. Expand
Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules
We report a method to convert discrete representations of molecules to and from a multidimensional continuous representation. This model allows us to generate new molecules for efficient explorationExpand
A variational eigenvalue solver on a photonic quantum processor
The proposed approach drastically reduces the coherence time requirements and combines this method with a new approach to state preparation based on ansätze and classical optimization, enhancing the potential of quantum resources available today and in the near future. Expand
Simulation of electronic structure Hamiltonians using quantum computers
Over the last century, a large number of physical and mathematical developments paired with rapidly advancing technology have allowed the field of quantum chemistry to advance dramatically. However,Expand
Scalable Quantum Simulation of Molecular Energies
We report the first electronic structure calculation performed on a quantum computer without exponentially costly precompilation. We use a programmable array of superconducting qubits to compute theExpand
Objective-Reinforced Generative Adversarial Networks (ORGAN) for Sequence Generation Models
This work builds upon previous results that incorporated GANs and RL in order to generate sequence data and test this model in several settings for the generation of molecules encoded as text sequences and in the context of music generation, showing for each case that it can effectively bias the generation process towards desired metrics. Expand
Quantum computational chemistry
With small quantum computers becoming a reality, first applications are eagerly sought. Quantum chemistry presents a spectrum of computational problems, from relatively easy to classicallyExpand
Molecular Sets (MOSES): A Benchmarking Platform for Molecular Generation Models
A benchmarking platform called Molecular Sets (MOSES) is introduced to standardize training and comparison of molecular generative models and suggest to use the results as reference points for further advancements in generative chemistry research. Expand
qHiPSTER: The Quantum High Performance Software Testing Environment
We present qHiPSTER, the Quantum High Performance Software Testing Environment. qHiPSTER is a distributed high-performance implementation of a quantum simulator on a classical computer, that canExpand
Design of efficient molecular organic light-emitting diodes by a high-throughput virtual screening and experimental approach.
An integrated organic functional material design process that incorporates theoretical insight, quantum chemistry, cheminformatics, machine learning, industrial expertise, organic synthesis, molecular characterization, device fabrication and optoelectronic testing is reported. Expand