Recent emergence of high-throughput drug screening assays sparkled an intensive development of machine learning methods, including models for prediction of sensitivity of cancer cell lines to anti-cancer drugs, as well as methods for generation of potential drug candidates. However, a concept of generation of compounds with specific properties and simultaneous modeling of their efficacy against cancer cell lines has not been comprehensively explored. To address this need, we present VADEERS, a Variational Autoencoder-based Drug Efficacy Estimation Recommender System. The generation of compounds is performed by a novel variational autoencoder with a semi-supervised Gaussian Mixture Model (GMM) prior. The prior defines a clustering in the latent space, where the clusters are associated with specific drug properties. In addition, VADEERS is equipped with a cell line autoencoder and a sensitivity prediction network. The model combines data for SMILES string representations of anti-cancer drugs, their inhibition profiles against a panel of protein kinases, cell lines biological features and measurements of the sensitivity of the cell lines to the drugs. The evaluated variants of VADEERS achieve a high r=0.87 Pearson correlation between true and predicted drug sensitivity estimates. We train the GMM prior in such a way that the clusters in the latent space correspond to a pre-computed clustering of the drugs by their inhibitory profiles. We show that the learned latent representations and new generated data points accurately reflect the given clustering. In summary, VADEERS offers a comprehensive model of drugs and cell lines properties and relationships between them, as well as a guided generation of novel compounds.

VADEERS是一种基于变分自编码器的药物疗效评估推荐系统，实现了对具有特定属性的化合物的生成和其对癌细胞敏感性的模型建模，并达到了高度相关的预测效果。

基于GMM先验的生成式推荐系统用于癌症药物生成和敏感性预测