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<p>De novo design methodologies have the potential to significantly enhance the exploration of chemical space in the search for promising ligands featuring novel chemotypes. This exploration can be directed through various computational strategies. 3D pharmacophore models, which represent the interaction patterns critical for protein–ligand recognition, can serve as valuable tools for the design of novel compounds. A common limitation of many generative approaches is the low synthetic feasibility of the generated molecular structures. In the present study, we developed a method capable of controllably generating compounds with a relatively high degree of synthetic accessibility by leveraging the CReM framework, while explicitly conforming to a specified 3D pharmacophore model. Evaluation of this approach across a diverse set of protein targets and pharmacophore models of varying complexity demonstrated its effectiveness and highlighted its advantages over the PGMG method, which employs a deep neural network architecture to generate ligands that may exhibit desired 3D geometries upon embedding. The proposed method has been implemented as an open-source tool, CReM-pharm, available at <a href="https://github.com/ci-lab-cz/crem-pharm">https://github.com/ci-lab-cz/crem-pharm</a>.</p>

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CReM-pharm: de novo 3D pharmacophore-based design with synthetic accessibility awareness

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年4月15日

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  • Alina Denzler,
  • Dinesh Kumar Sriramulu,
  • Jozef Pecha,
  • Pavel Polishchuk

摘要

De novo design methodologies have the potential to significantly enhance the exploration of chemical space in the search for promising ligands featuring novel chemotypes. This exploration can be directed through various computational strategies. 3D pharmacophore models, which represent the interaction patterns critical for protein–ligand recognition, can serve as valuable tools for the design of novel compounds. A common limitation of many generative approaches is the low synthetic feasibility of the generated molecular structures. In the present study, we developed a method capable of controllably generating compounds with a relatively high degree of synthetic accessibility by leveraging the CReM framework, while explicitly conforming to a specified 3D pharmacophore model. Evaluation of this approach across a diverse set of protein targets and pharmacophore models of varying complexity demonstrated its effectiveness and highlighted its advantages over the PGMG method, which employs a deep neural network architecture to generate ligands that may exhibit desired 3D geometries upon embedding. The proposed method has been implemented as an open-source tool, CReM-pharm, available at https://github.com/ci-lab-cz/crem-pharm.