Abstract Identifying drugs that reverse expression of disease-associated transcriptomic features has been widely explored as a strategy for discovering drug repurposing candidates, but its potential for novel compound discovery and optimization remains largely underexplored. Here, we present a deep learning-based drug discovery platform, guided by transcriptomic features, that screens large compound libraries and optimizes lead compounds. We first develop a model that predicts gene expression changes solely from chemical structures and deploy it to infer the expression changes induced by compounds in large screening libraries. We then refine compound scoring and employ a Monte Carlo Tree Search method for multi-objective optimization. By incorporating Structure-Gene-Activity Relationships, we uncover drug mechanisms directly from transcriptomic data. To demonstrate the utility of the system, we identify and validate compounds for hepatocellular carcinoma (HCC). In HCC, we design a novel compound that improves the IC50 from 4 µM to 0.5 µM, with increased in vitro selectivity, favorable pharmacokinetics and in vivo activity. Citation Format: Jing Xing, Mingdian Tan, Dmitry Leshchiner, Mengying Sun, Shreya Paithankar, Rama Shankar, Erika Lisabeth, Bilal Aleiwi, Ruoqiao Chen, Matthew Giletto, Richard Neubig, Samuel So, Edmund Ellsworth, Mei-Sze Chua, Jiayu Zhou, Bin Chen. Deep learning-based screening and design of novel therapeutics that reverse cancer-associated transcriptional phenotypes abstract. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 975.
Xing et al. (Fri,) studied this question.