2 papers
stat.ML2019
Probabilistic hypergraph grammars for efficient molecular optimization
Egor Kraev, Mark Harley
We present an approach to make molecular optimization more efficient. We infer a hypergraph replacement grammar from the ChEMBL database, count the frequencies of particular rules…
cs.LG2018
Grammars and reinforcement learning for molecule optimization
Egor Kraev
We seek to automate the design of molecules based on specific chemical properties. Our primary contributions are a simpler method for generating SMILES strings guaranteed to be che…