ArticleACS medicinal chemistry letters2025
Identification of a Novel Indolizine RORγT Inverse Agonist Using the AI-Driven Drug Design Platform.
Article in ACS medicinal chemistry letters, 2025. 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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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
- Multi-objective optimization in the context of generative chemistry.Nature communications · 2026Review
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9 authors.
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Abstract
Automated multiparameter optimization (MPO) at the point of initial drug design is a powerful emerging approach to improve and expedite drug development. We employed the AI-driven drug design (AIDD) platform to design novel RORγT ligands optimized using QSAR activity models, machine learning ADMET properties, 3D pharmacophore similarity, and synthetic difficulty predictions. We calculated several measures of novelty postdesign and then employed multicriteria decision analysis (MCDA) to select compounds for synthesis for this important drug target. We found that 19/27 (70%) of the selected compounds inhibited RORγT activity in a cell-based assay by at least 25% at 20 μM. The most potent compound had a measured IC
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