Graph generative models have been utilized for discovering novel molecules with desired chemical properties. Several of these methods use neural networks to map the latent space to chemical properties and explore the latent space efficiently. We propose using multi-target networks to jointly predict several molecular properties and learn better representations by exploiting auxiliary information. Our joint model outperforms existing methods in property prediction and molecular optimization tasks. We also propose a new benchmark to compare generative models for drug discovery.