Vorhersage der MOF‐Synthese durch automatisches Data‐Mining und maschinelles Lernen

Trotz großer Fortschritte auf dem Gebiet der metallorganischen Gerüststrukturen (MOF) ist das volle Potential des Maschinellen Lernens (ML) für die Vorhersage von MOF-Syntheseparametern bisher noch nicht erschlossen. In diesem Beitrag wird dargestellt, wie Methoden des ML für die Rationalisierung und Beschleunigung von MOF-Entwicklungsverfahren eingesetzt werden können, indem die Synthesebedingungen der MOFs direkt anhand ihrer Kristallstruktur vorhergesagt werden. Unser Ansatz stützt sich auf: i) die Erstellung der ersten MOF-Synthese-Datenbank durch automatische Extraktion der Syntheseparameter aus der Fachliteratur, ii) das Trainieren und die Optimierung von ML-Modellen mit Daten der MOF-Datenbank und iii) die ML basierte Vorhersage der Synthesebedingungen neuer MOF-Strukturen. Schon jetzt übertreffen die Ergebnisse der Vorhersagemodelle die Vorhersagen menschlicher ExpertInnen, welche in einer Befragung ermittelt wurden. Die automatisierte Synthesevorhersage ist über ein Web-Tool unter https://mof-synthesis.aimat.science verfügbar

MOF Synthesis Prediction Enabled by Automatic Data Mining and Machine Learning**

Despite rapid progress in the field of metal–organic frameworks (MOFs), the potential of using machine learning (ML) methods to predict MOF synthesis parameters is still untapped. Here, we show how ML can be used for rationalization and acceleration of the MOF discovery process by directly predicting the synthesis conditions of a MOF based on its crystal structure. Our approach is based on: i) establishing the first MOF synthesis database via automatic extraction of synthesis parameters from the literature, ii) training and optimizing ML models by employing the MOF database, and iii) predicting the synthesis conditions for new MOF structures. The ML models, even at an initial stage, exhibit a good prediction performance, outperforming human expert predictions, obtained through a synthesis survey. The automated synthesis prediction is available via a web‐tool on https://mof‐synthesis.aimat.science

Beneficial Effect of Successful Simultaneous Pancreas-Kidney Transplantation on Plasma Profile of Metalloproteinases in Type 1 Diabetes Mellitus Patients

It is not fully elucidated whether the restoring of normal glucose metabolism after successful simultaneous pancreas-kidney transplantation (SPK) improves vascular wall morphology and function in type 1 diabetic (T1D) patients. Therefore, we compared arterial stiffness, assessed by pulse wave velocity (PWV), carotid intima-media thickness (IMT), and biomarkers of arterial wall calcification in T1D patients after SPK or kidney transplantation alone (KTA). In 39 SPK and 39 KTA adult patients of similar age, PWV, IMT, circulating matrix metalloproteinases (MMPs) and calcification biomarkers were assessed at median 83 months post transplantation. Additionally, carotid plaques were visualized and semi-qualitatively classified. Although PWV and IMT values were similar, the occurrence of atherosclerotic plaques (51.3 vs. 70.3%, p < 0.01) and calcified lesions (35.9 vs. 64.9%, p < 0.05) was lower in SPK patients. There were significantly lower concentrations of MMP-1, MMP-2, MMP-3, and osteocalcin in SPK subjects. Among the analyzed biomarkers, only logMMP-1, logMMP-2, and logMMP-3 concentrations were associated with log HbA1c. Multivariate stepwise backward regression analysis revealed that MMP-1 and MMP-3 variability were explained only by log HbA1c. Normal glucose metabolism achieved by SPK is followed by the favorable profile of circulating matrix metalloproteinases, which may reflect the vasoprotective effect of pancreas transplantation