ArticleJournal of chemical information and modeling2026
VeGA-RX and VeGA-SCX: Controllable SMARTS-Guided Generative Transformers for Precision-Driven
Article in Journal of chemical information and modeling, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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Who cites it
1 citing paper in PubMed.
- Artificial intelligence-assisted lead optimization in drug discovery: bridging computational advances and translational challenges.Medicinal chemistry research : an international journal for rapid communications on design and mechanisms of action of biologically active agents · 2026Review
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3 authors.
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Abstract
Generative models enable large-scale exploration of chemical space; however, achieving controllable generation that balances novelty with preservation of bioactive features remains challenging. In this study, we introduce a controllable framework based on the VeGA architecture, leveraging SMARTS-RX functional descriptors. We present two distinct SMILES-based generative models: VeGA-RX, conditioned on semantic tokens for flexible exploration, and VeGA-SCX, which integrates topological guidance via Bemis-Murcko scaffolds for high-precision generation. We show that sampling temperature modulates chemical quality, with lower temperatures (
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