Scholarlink

<p>Machine learning approaches for conceptualizing and designing in silico compounds have attracted significant attention. However, the applicability of these compounds is often challenged by synthetic viability and cost-effectiveness. Researchers introduced proxy-scores, known as synthethic accessiblity scoring, to quantify the ease of synthesis for virtual molecules. Despite their utility, existing synthetic accessibility tools have notable limitations: they overlook compound purchasability, lack physical interpretability, and often rely on imperfect computer-aided synthesis planning algorithms. We introduce <i>MolPrice</i>, an accurate and fast model for molecular price prediction. Utilizing self-supervised contrastive learning, <i>MolPrice</i> autonomously generates price labels for synthetically complex molecules, enabling the model to generalize to molecules beyond the training distribution. Our results show that <i>MolPrice</i> reliably assigns higher prices to synthetically complex molecules than to readily purchasable ones, effectively distinguishing different levels of synthetic accessibility. Furthermore, <i>MolPrice</i> achieves competitive performance on literature benchmarks for synthetic accessibility. To demonstrate its practical utility, we conduct a virtual screening case study, illustrating how <i>MolPrice</i> successfully identifies purchasable molecules from a large candidate library. <i>MolPrice</i> bridges the gap between generative molecular design and real-world feasibility by integrating cost-awareness into synthetic accessibility assessment, making it a powerful model to accelerate molecular discovery.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

MolPrice: assessing synthetic accessibility of molecules based on market value

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年9月29日

查看全文
  • Friedrich Hastedt,
  • Klaus Hellgardt,
  • Sophia Yaliraki,
  • Dongda Zhang,
  • Antonio del Rio Chanona

摘要

Machine learning approaches for conceptualizing and designing in silico compounds have attracted significant attention. However, the applicability of these compounds is often challenged by synthetic viability and cost-effectiveness. Researchers introduced proxy-scores, known as synthethic accessiblity scoring, to quantify the ease of synthesis for virtual molecules. Despite their utility, existing synthetic accessibility tools have notable limitations: they overlook compound purchasability, lack physical interpretability, and often rely on imperfect computer-aided synthesis planning algorithms. We introduce MolPrice, an accurate and fast model for molecular price prediction. Utilizing self-supervised contrastive learning, MolPrice autonomously generates price labels for synthetically complex molecules, enabling the model to generalize to molecules beyond the training distribution. Our results show that MolPrice reliably assigns higher prices to synthetically complex molecules than to readily purchasable ones, effectively distinguishing different levels of synthetic accessibility. Furthermore, MolPrice achieves competitive performance on literature benchmarks for synthetic accessibility. To demonstrate its practical utility, we conduct a virtual screening case study, illustrating how MolPrice successfully identifies purchasable molecules from a large candidate library. MolPrice bridges the gap between generative molecular design and real-world feasibility by integrating cost-awareness into synthetic accessibility assessment, making it a powerful model to accelerate molecular discovery.