Fig. 2

Introduction of MolFilterGAN. a Molecular representation. A molecule is represented as a SMILES string with a length of \(T\). b The generator \({G}_{\theta }\) contains three LSTM cells and one linear layer. Both the input and output of \({G}_{\theta }\) are SMILES strings. c The discriminator \({D}_{\varphi }\). The input is a SMILES string, and the output is the probability that the sample belongs to the positive set. The SMILES string is first embedded into a \(T\hspace{0.17em}\)× k matrix, where \(T\) is the length of the string and k is the size of each embedding vector. Then, multiscale convolution kernels ((1, k), (2, k), (…, k)), max-pooling and a concatenation operation are applied. Finally, a linear layer is used to output the probability. d Adversarial training. The generator is tuned by maximizing the rewards predicted by the discriminator. The discriminator is tuned by minimizing the error of discriminating between “fake” samples from the generator (negative set) and “real” samples (positive set)