Molecular generation
The technique of using machine learning models to generate new molecular structures, widely applied in drug discovery and materials design; a core challenge is generalization under low-data conditions.
Timeline (last 90 days)
With only 1000 molecules of training data, randomized SAFE representations increase the yield of genuinely novel molecules by more than 450x.
Under matched corpus size and compute, randomized SAFE's true yield is 2.8x that of randomized SMILES.
In low-data molecule generation, prioritizing representations that support fragment-level randomization (such as SAFE) and enabling both atom-order and fragment-order randomization is more effective than choosing only one.
All Facts (3)
With only 1000 molecules of training data, randomized SAFE representations increase the yield of genuinely novel molecules by more than 450x.
50%UnverifiedUnder matched corpus size and compute, randomized SAFE's true yield is 2.8x that of randomized SMILES.
50%UnverifiedIn low-data molecule generation, prioritizing representations that support fragment-level randomization (such as SAFE) and enabling both atom-order and fragment-order randomization is more effective than choosing only one.
50%