{
    "protocol": "The synthesis of hydrophilic polymers with functional groups involves constructing conditional recurrent neural networks (cRNNs) by setting the internal states of long short-term memory cells (LSTMs) after input conditions. The process utilizes molecular descriptors, which can include physicochemical properties derived from the molecular structure using software tools like the RDKit Python library. For example, the physchem-based (PCB) model incorporates six scalar properties: logP, TPSA, molecular weight, drug-likeness (QED), number of hydrogen-bond acceptors (HBA), and donors (HBD), concatenated with bioactivity predictions from QSAR models. The model is trained using randomized SMILES strings as targets, with inference triggered by a specific starting character and stopping upon the generation of a designated character."
}