Denovo generation of bioactive and drug-like hit molecules is a pivotal goal in computer-aided drug discovery. While artificial intelligence (AI) has proven adept at generating molecules with desired chemical properties, previous studies often overlook the influence of disease-specific cellular environments. This study introduces GxVAEs, a novel AI-driven deep generative model designed to produce hit molecules from transcriptome profiles using dual variational autoencoders (VAEs). The first VAE, ProfileVAE, extracts latent features from transcriptome profiles to guide the second VAE, MolVAE, in generating hit molecules. GxVAEs aim to bridge the gap between molecule generation and the biological context of disease, producing molecules that are biologically relevant within specific cellular environments or pathological conditions. Experimental results and case studies focused on hit molecule generation demonstrate that GxVAEs surpass current state-of-the-art methods, in terms of reproducibility of known ligands. This approach is expected to effectively find potential molecular structures with bioactivities across diverse disease contexts.

中文翻译：

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AI 驱动的转录组谱引导的苗头化合物生成


Denovo 生成生物活性和药物样苗头化合物分子是计算机辅助药物发现的关键目标。虽然人工智能 （AI） 已被证明擅长生成具有所需化学性质的分子，但以前的研究往往忽视了疾病特异性细胞环境的影响。本研究介绍了 GxVAEs，这是一种新颖的人工智能驱动的深度生成模型，旨在使用双变分自动编码器 （VAE） 从转录组图谱中生成命中分子。第一个 VAE （ProfileVAE） 从转录组图谱中提取潜在特征，以指导第二个 VAE （MolVAE） 生成命中分子。GxVAEs 旨在弥合分子生成与疾病的生物学背景之间的差距，产生在特定细胞环境或病理条件下具有生物学相关性的分子。专注于苗头化合物分子生成的实验结果和案例研究表明，就已知配体的可重复性而言，GxVAEs 超越了当前最先进的方法。这种方法有望有效地找到在不同疾病背景下具有生物活性的潜在分子结构。