AIDD-study

Paper

Convolutional neural network based on SMILES representation of compounds for detecting chemical motif

FP2VEC: a new molecular featurizer for learning molecular properties

Predicting Chemical Properties using Self-Attention Multi-task Learning based on SMILES Representation

The Effect of Resampling on Data-imbalanced Conditions for Prediction towards Nuclear Receptor Profiling Using Deep Learning

Molecular Generative Model Based on an Adversarially Regularized Autoencoder

MolGPT: Molecular Generation Using a Transformer-Decoder Model

cMolGPT: A Conditional Generative Pre-Trained Transformer for Target-Specific De Novo Molecular Generation

CMGN: a conditional molecular generation net to design target-specific molecules with desired properties

LAIDD lecture

화학정보학개론

신약개발에 필요한 머신러닝 이해

Attention for Deep Learning