Controlled Molecule Generator for Optimizing Multiple Chemical Properties
This addresses the costly challenge in drug discovery of optimizing multiple chemical properties without compromising others, though it appears incremental as it builds on existing Transformer-based methods.
The paper tackles the problem of generating novel molecules optimized for multiple chemical properties simultaneously, which is crucial for drug discovery, and demonstrates that their proposed model significantly outperforms state-of-the-art models.
Generating a novel and optimized molecule with desired chemical properties is an essential part of the drug discovery process. Failure to meet one of the required properties can frequently lead to failure in a clinical test which is costly. In addition, optimizing these multiple properties is a challenging task because the optimization of one property is prone to changing other properties. In this paper, we pose this multi-property optimization problem as a sequence translation process and propose a new optimized molecule generator model based on the Transformer with two constraint networks: property prediction and similarity prediction. We further improve the model by incorporating score predictions from these constraint networks in a modified beam search algorithm. The experiments demonstrate that our proposed model outperforms state-of-the-art models by a significant margin for optimizing multiple properties simultaneously.