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As lead discovery increasingly targets hard-to-drug proteins, the expansion of chemical space presents unprecedented opportunities for hit identification – yet scalable, effective technologies to exploit these vast spaces remain underdeveloped. We describe recent advances to our FRASE-based hit-finding platform (FRASE-bot), including integration of an AI-powered 3D pharmacophore screening across multi-billion-compound libraries, a Hit-Triage Pretrained Transformer (Hit-TPT), and alchemical binding free energy (ABFE) simulations. We also introduce emerging strategies for leveraging phenotypic data to support both hit identification and lead optimization. The platform's utility is demonstrated across several case studies, including our winning entries in CACHE Challenges #1 and #2.

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Early-Stage Discovery in the Era of Hard-To-Drug Targets and Giga-Scale Chemical Spaces

  • 会议论文

  • 发表日期：

    2026年

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  • Dmitri Kireev

摘要

As lead discovery increasingly targets hard-to-drug proteins, the expansion of chemical space presents unprecedented opportunities for hit identification – yet scalable, effective technologies to exploit these vast spaces remain underdeveloped. We describe recent advances to our FRASE-based hit-finding platform (FRASE-bot), including integration of an AI-powered 3D pharmacophore screening across multi-billion-compound libraries, a Hit-Triage Pretrained Transformer (Hit-TPT), and alchemical binding free energy (ABFE) simulations. We also introduce emerging strategies for leveraging phenotypic data to support both hit identification and lead optimization. The platform's utility is demonstrated across several case studies, including our winning entries in CACHE Challenges #1 and #2.