PCF-VAE: posterior collapse free variational autoencoder for de novo drug design

Generating novel molecular structures with desired pharmacological and physicochemical properties is challenging due to the vast chemical space, complex optimization requirements, predictive limitations of models, and data scarcity. This study focuses on investigating the problem of posterior collapse in variational autoencoders, a deep learning technique used for de novo molecular design. Various generative variational autoencoders were employed to map molecule structures to a continuous latent space and vice versa, evaluating their performance as structure generators. Most state-of-the-art approaches suffer from posterior collapse, limiting the diversity of generated molecules. To address this challenge, a novel approach termed PCF-VAE was introduced to mitigate the issue of posterior collapse, reduce the complexity of SMILES representations, and enhance diversity in molecule generation. In comparison to state-of-the-art models, PCF-VAE has been evaluated and compared in the MOSES benchmark at different diversity levels. Depending on the diversity level, PCF-VAE has a validity of 98.01% at D = 1, 97.10% at D = 2, and 95.01% at D = 3. It is important to note that PCF-VAE effectively generates molecules with a 100% unique structure. Both intDiv and intDiv2 are measures of internal diversity; intDiv2 ranges from 85.87 to 86.33% and intDiv ranges from 85.87 to 89.01%. Additionally, at D = 1, D = 2, and D = 3, PCF-VAE generates 93.77%, 94.71%, and 95.01% novel molecules, respectively. The results indicate that this research provides valuable insights into the challenges of molecular generation and contributes to the design of novel molecules with desired properties.