Multi-objective drug design with a scaffold-aware variational autoencoder

Abstract

Designing molecules with multiple properties of interest is a fundamental challenge in drug development. To tackle this, we have developed ScafVAE, an innovative scaffold-aware variational autoencoder designed for the in silico graph-based generation of multi-objective drug candidates. By integrating our proposed bond scaffold-based generation with perplexity-inspired fragmentation, we expand the accessible chemical space of the conventional fragment-based approach while preserving its high chemical validity. ScafVAE was pre-trained on a large dataset of molecules and further augmented through contrastive learning and molecular fingerprint reconstruction, resulting in high accuracy in predicting various computationally and experimentally measured molecular properties. Only a few of its parameters are task-specific, facilitating easy adaptation to new desired properties. ScafVAE was employed to generate dual-target drug candidates against drug resistance in cancer therapy, considering four distinct resistance mechanisms, with or without additional properties such as drug-likeness or toxicity. The generated molecules exhibited strong binding strength to target proteins using molecular docking or experimentally measured affinity while maintaining optimized extra properties. Further molecular dynamics simulations confirmed the stable binding interactions between the generated molecules and target proteins. These findings position ScafVAE as a promising alternative to conventional generation approaches.

Graphical abstract: Multi-objective drug design with a scaffold-aware variational autoencoder

Supplementary files

Article information

Article type
Edge Article
Submitted
27 Dec 2024
Accepted
20 May 2025
First published
25 Jun 2025
This article is Open Access

All publication charges for this article have been paid for by the Royal Society of Chemistry
Creative Commons BY-NC license

Chem. Sci., 2025, Advance Article

Multi-objective drug design with a scaffold-aware variational autoencoder

T. Dong, L. You and C. Y. Chen, Chem. Sci., 2025, Advance Article , DOI: 10.1039/D4SC08736D

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