Generate Novel Molecules With Target Properties Using Conditional Generative Models
This work addresses the need for efficient molecule generation in drug discovery, though it appears incremental as it builds on existing conditional generative approaches.
The paper tackles the problem of generating novel small molecules with desired properties for drug discovery by introducing a conditional generative model, achieving improved performance over previous methods on metrics like Molecular weight, LogP, and Quantitative Estimation of Drug-likeness.
Drug discovery using deep learning has attracted a lot of attention of late as it has obvious advantages like higher efficiency, less manual guessing and faster process time. In this paper, we present a novel neural network for generating small molecules similar to the ones in the training set. Our network consists of an encoder made up of bi-GRU layers for converting the input samples to a latent space, predictor for enhancing the capability of encoder made up of 1D-CNN layers and a decoder comprised of uni-GRU layers for reconstructing the samples from the latent space representation. Condition vector in latent space is used for generating molecules with the desired properties. We present the loss functions used for training our network, experimental details and property prediction metrics. Our network outperforms previous methods using Molecular weight, LogP and Quantitative Estimation of Drug-likeness as the evaluation metrics.