This repository contains the code to implement BiCEV (Bidirectional Compound-Expression Variational Autoencoder) for generating molecules from given gene expression data.

• Compound and gene expression datasets (including GSE70138, gene-knockdown profiles, and combined signatures of synergistic drug pairs) can be downloaded from the dataset folder in this link: https://kmutt.me/bicev_data.
• Please copy the downloaded datasets to the data folder to implement.
• The weights of CLA and BiCEV models are also provided in the same link in the weights folder.

BiCEV implementation relies on NumPy, Pandas, PyTorch, PyTorch Lightning, RDKit-pypi, cmapPy, and fcd. You may install these dependencies using the following command (recommend for cuda version 12.0):

pip install -r requirements.txt

import pytorch_lightning as pl
from model.cla_model import CLA
cla_model = CLA()
cla_trainer = pl.Trainer(max_epochs=20)
cla_trainer.fit(cla_model)

import pytorch_lightning as pl
from model.cea_model import BiCEV
model = BiCEV(cla_enc_weight='cla_encoder_weight.ckpt',
                cla_dec_weight='cla_decoder_weight.ckpt')
trainer = pl.Trainer(gpus=[0], max_epochs=1)
trainer.fit(model)

• ZINC15: Sterling and Irwin, J. Chem. Inf. Model, 2015 https://pubs.acs.org/doi/abs/10.1021/acs.jcim.5b00559.
• CMAP LINCS 2020: https://clue.io/data/CMap2020#LINCS2020
• LINCS L1000 GSE70138: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE70138
• Subramanian A, et al. A Next Generation Connectivity Map: L1000 Platform And The First 1,000,000 Profiles. Cell. 2017/12/1. 171(6):1437–1452.
• Shayakhmetov R, et al. Molecular Generation for Desired Transcriptome Changes With Adversarial Autoencoders. Frontiers in Pharmacology 2020, 11.