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<p>Crystal polymorphism is an important and fascinating aspect of solid state chemistry with far reaching implications in the pharmaceuticals, agrisciences, nutraceuticals, battery and aviation industries. Late appearing more stable polymorphs have caused numerous issues in the pharmaceutical industry. Experimental polymorph screening can be very expensive and time consuming, and sometimes may miss important low energy polymorphs due to an inability to exhaust all crystallization conditions. In this paper, we report a crystal structure prediction (CSP) method with state of the art accuracy and efficiency, validated on a large and diverse dataset including 66 molecules with 137 experimentally known polymorphic forms. The method combines a novel systematic crystal packing search algorithm and the use of machine learning force fields in a hierarchical crystal energy ranking. Our method not only reproduces all the experimentally known polymorphs, but also suggests new low energy polymorphs yet to be discovered by experiment that might pose potential risks to development of the currently known forms of these compounds. In addition, we report the prediction results of a blinded study, results for Target XXXI from the seventh CSP blind test, and demonstrate how the method can be used to accelerate clinical formulation design and derisk downstream processing.</p>

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A robust crystal structure prediction method to support small molecule drug development with large scale validation and blind study

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年3月5日

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  • Dong Zhou,
  • Imanuel Bier,
  • Biswajit Santra,
  • Leif D. Jacobson,
  • Chuanjie Wu,
  • Adiran Garaizar Suarez,
  • Barbara Ramirez Almaguer,
  • Haoyu Yu,
  • Robert Abel,
  • Richard A. Friesner,
  • Lingle Wang

摘要

Crystal polymorphism is an important and fascinating aspect of solid state chemistry with far reaching implications in the pharmaceuticals, agrisciences, nutraceuticals, battery and aviation industries. Late appearing more stable polymorphs have caused numerous issues in the pharmaceutical industry. Experimental polymorph screening can be very expensive and time consuming, and sometimes may miss important low energy polymorphs due to an inability to exhaust all crystallization conditions. In this paper, we report a crystal structure prediction (CSP) method with state of the art accuracy and efficiency, validated on a large and diverse dataset including 66 molecules with 137 experimentally known polymorphic forms. The method combines a novel systematic crystal packing search algorithm and the use of machine learning force fields in a hierarchical crystal energy ranking. Our method not only reproduces all the experimentally known polymorphs, but also suggests new low energy polymorphs yet to be discovered by experiment that might pose potential risks to development of the currently known forms of these compounds. In addition, we report the prediction results of a blinded study, results for Target XXXI from the seventh CSP blind test, and demonstrate how the method can be used to accelerate clinical formulation design and derisk downstream processing.