Automatic solid form classification in pharmaceutical drug development

Julius Lange, Leonid Komissarov, Rene Lang, Dennis Dimo Enkelmann, Andrea Anelli

Submission Track: Short Paper

Submission Category: Automated Material Characterization

Keywords: Siamese Networks, Organic Crystals, Pharmaceutical Materials, Solid Form Screening

TL;DR: A new siamese network classifies with unprecedented accuracy pharmaceutical materials XRPD patterns.

Abstract: In materials and pharmaceutical development, rapidly and accurately determining the similarity between X-ray powder diffraction (XRPD) measurements is crucial for efficient solid form screening and analysis. We present SMolNet, a classifier based on a Siamese network architecture, designed to automate the comparison of XRPD patterns. Our results show that training SMolNet on loss functions from the self-supervised learning domain yields a substantial boost in performance with respect to class separability and precision, specifically when classifying phases of previously unseen compounds. The application of SMolNet demonstrates significant improvements in screening efficiency across multiple active pharmaceutical ingredients, providing a powerful tool for scientists to discover and categorize measurements with reliable accuracy.

AI4Mat Journal Track: Yes

Submission Number: 77