Search Results for author:
Found 8 papers, 6 papers with code
Recent advances in the Self-Referencing Embedding Strings (SELFIES) library
String-based molecular representations play a crucial role in cheminformatics applications, and with the growing success of deep learning in chemistry, have been readily adopted into machine learning pipelines.
On scientific understanding with artificial intelligence
Imagine an oracle that correctly predicts the outcome of every particle physics experiment, the products of every chemical reaction, or the function of every protein.
SELFIES and the future of molecular string representations
We hope that these proposals will inspire several follow-up works exploiting the full potential of molecular string representations for the future of AI in chemistry and materials science.
JANUS: Parallel Tempered Genetic Algorithm Guided by Deep Neural Networks for Inverse Molecular Design
Inverse molecular design, i. e., designing molecules with specific target properties, can be posed as an optimization problem.
Assigning Confidence to Molecular Property Prediction
Introduction: Computational modeling has rapidly advanced over the last decades, especially to predict molecular properties for chemistry, material science and drug design.
Curiosity in exploring chemical space: Intrinsic rewards for deep molecular reinforcement learning
However, the search space is vast and efficient exploration is desirable when using reinforcement learning agents.
Augmenting Genetic Algorithms with Deep Neural Networks for Exploring the Chemical Space
Challenges in natural sciences can often be phrased as optimization problems.
Self-Referencing Embedded Strings (SELFIES): A 100% robust molecular string representation
SELFIES can be directly applied in arbitrary machine learning models without the adaptation of the models; each of the generated molecule candidates is valid.
