Enabling late-stage drug diversification by high-throughput experimentation with geometric deep learning

• Published in: Nature chemistry Vol. 16; no. 2; pp. 239 - 248
• Main Authors: Nippa, David F., Atz, Kenneth, Hohler, Remo, Müller, Alex T., Marx, Andreas, Bartelmus, Christian, Wuitschik, Georg, Marzuoli, Irene, Jost, Vera, Wolfard, Jens, Binder, Martin, Stepan, Antonia F., Konrad, David B., Grether, Uwe, Martin, Rainer E., Schneider, Gisbert
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 01.02.2024; NATURE PORTFOLIO; Nature Publishing Group
• Subjects: 631/114/1305; 639/166/898; 639/638/309/2419; 639/638/403/933; 639/638/549/973; Analytical Chemistry; Biochemistry; Chemical synthesis; Chemistry; Chemistry and Materials Science; Chemistry, Multidisciplinary; Chemistry/Food Science; Complexity; Computer applications; Deep Learning; Diversification; Drug development; Experimentation; High-Throughput Screening Assays; Inorganic Chemistry; Organic Chemistry; Physical Chemistry; Physical Sciences; Regioselectivity; Science & Technology; Substrates; FUNCTIONALIZATION; PLATFORM; DESIGN; C-H BOND; EVOLUTION; GENERATION; CHEMISTRY INFORMER LIBRARIES; PREDICTION
• ISSN: 1755-4330; 1755-4349; 1755-4349
• DOI: 10.1038/s41557-023-01360-5

Late-stage functionalization is an economical approach to optimize the properties of drug candidates. However, the chemical complexity of drug molecules often makes late-stage diversification challenging. To address this problem, a late-stage functionalization platform based on geometric deep learning and high-throughput reaction screening was developed. Considering borylation as a critical step in late-stage functionalization, the computational model predicted reaction yields for diverse reaction conditions with a mean absolute error margin of 4–5%, while the reactivity of novel reactions with known and unknown substrates was classified with a balanced accuracy of 92% and 67%, respectively. The regioselectivity of the major products was accurately captured with a classifier F -score of 67%. When applied to 23 diverse commercial drug molecules, the platform successfully identified numerous opportunities for structural diversification. The influence of steric and electronic information on model performance was quantified, and a comprehensive simple user-friendly reaction format was introduced that proved to be a key enabler for seamlessly integrating deep learning and high-throughput experimentation for late-stage functionalization. Late-stage functionalization of complex drug molecules is challenging. To address this problem, a discovery platform based on geometric deep learning and high-throughput experimentation was developed. The computational model predicts binary reaction outcome, reaction yield and regioselectivity with low error margins, enabling the functionalization of complex molecules without de novo synthesis.