ArticleNature communications2025
A robust crystal structure prediction method to support small molecule drug development with large scale validation and blind study.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- The Applications of Machine Learning in Micro-Nano Materials Research: From High-Throughput Screening to Intelligent Design.Small (Weinheim an der Bergstrasse, Germany) · 2026Review
- Exploring celecoxib polymorph landscape using AIMNet2 machine learning interatomic potential.Chemical science · 2026Article
- Mechanistic Insights into Molecular Modifiers That Promote Urate Crystallization through Solute Assembly Regulation.JACS Au · 2026Article
- From Algorithms to Assets: A Comprehensive Review of AI's Role in Preclinical Drug Discovery and the Hurdles to Clinical Translation.Pharmaceuticals (Basel, Switzerland) · 2026Review
- Crystal engineering for poorly water-soluble drugs: From design to applications.Acta pharmaceutica Sinica. B · 2026Review
- Open Molecular Crystals 2025 (OMC25) dataset and models.Scientific data · 2026Article
- Assessing Polymorph Stability and Phase Transitions at Finite Temperature: Integrating Crystal Structure Prediction, Lattice Dynamics, and Molecular Dynamics.Journal of chemical theory and computation · 2025Article
- Exploring organic chemical space for materials discovery using crystal structure prediction-informed evolutionary optimisation.Nature communications · 2025Article
- Genarris 3.0: Generating Close-Packed Molecular Crystal Structures with Rigid Press.Journal of chemical theory and computation · 2025Article
- Efficient Molecular Crystal Structure Prediction and Stability Assessment with AIMNet2 Neural Network Potentials.Crystal growth & design · 2025Article
- Accurate and efficient machine learning interatomic potentials for finite temperature modelling of molecular crystals.Chemical science · 2025Article
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Authors and funding
11 authors.
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Abstract
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.
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