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Found 3 papers, 1 papers with code
ACEGEN: Reinforcement learning of generative chemical agents for drug discovery
In recent years, reinforcement learning (RL) has emerged as a valuable tool in drug design, offering the potential to propose and optimize molecules with desired properties.
Large-scale Pretraining Improves Sample Efficiency of Active Learning based Molecule Virtual Screening
Accurate model can achieve high sample efficiency by finding the most promising compounds with only a fraction of the whole library being virtually screened.
A Transformer-based Generative Model for De Novo Molecular Design
In the scope of drug discovery, the molecular design aims to identify novel compounds from the chemical space where the potential drug-like molecules are estimated to be in the order of 10^60 - 10^100.
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