Automatic solid form classification in pharmaceutical drug development

TL;DR

This paper introduces SMolNet, a Siamese network-based classifier that enhances the accuracy and efficiency of XRPD pattern comparison in pharmaceutical solid form screening, especially for unseen compounds.

Contribution

The paper presents a novel self-supervised learning approach for training SMolNet, significantly improving XRPD pattern classification performance for new pharmaceutical compounds.

Findings

Enhanced class separability and precision in XRPD classification

Improved screening efficiency for pharmaceutical compounds

Effective classification of unseen solid forms

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.