Bridging the Phenotype-Target Gap for Molecular Generation via Multi-Objective Reinforcement Learning
Summary: arXiv:2509.21010v2 Announce Type: replace-cross
The de novo generation of drug-like molecules capable of inducing desirable phenotypic changes is receiving increasing attention in the field of drug discovery. Traditional methods have largely depended on expression profiles to guide the generation of these molecules but have failed to consider the perturbative effects that these molecules can have on different cellular contexts. To address this significant gap, researchers have introduced SmilesGEN, an innovative generative model built on a variational autoencoder (VAE) architecture that aims to create molecules with potential therapeutic effects.
Introduction to SmilesGEN
SmilesGEN represents a pioneering approach to molecular generation by integrating a pre-trained drug VAE, known as SmilesNet, with an expression profile VAE, referred to as ProfileNet. This dual-model architecture allows for a comprehensive modeling of the interplay between drug-induced perturbations and transcriptional responses, all within a unified latent space.
Key Features of SmilesGEN
- Joint Modeling: SmilesGEN effectively combines the capabilities of two distinct models to enhance the quality of generated molecules.
- Perturbation Management: ProfileNet is specifically designed to reconstruct pre-treatment expression profiles while eliminating the drug-induced perturbations in the latent space.
- Guided Generation: SmilesNet utilizes desired expression profiles to inform the generation of drug-like molecules.
Empirical Validation
The empirical experiments conducted by the research team have demonstrated that SmilesGEN outperforms existing state-of-the-art models. Key findings include:
- Higher validity of generated molecules.
- Increased uniqueness and novelty compared to previously established models.
- Superior Tanimoto similarity to known ligands targeting relevant proteins.
Applications and Future Directions
Further evaluation of SmilesGEN has been conducted in the context of scaffold-based molecule optimization and the generation of therapeutic agents. The results confirmed its exceptional performance in creating molecules that exhibit greater similarity to approved drugs.
Overall, SmilesGEN establishes a robust framework that leverages gene signatures for the generation of drug-like molecules, showcasing promising potential to induce desirable cellular phenotypic changes. This innovative approach may pave the way for more effective drug discovery processes in the future.
Accessing SmilesGEN
The source code and datasets associated with SmilesGEN are publicly available for researchers and developers interested in exploring this advanced model. Access the resources at: https://github.com/hliulab/SmilesGEN.