Geometric deep learning-guided Suzuki reaction conditions assessment for applications in medicinal chemistry.

Suzuki cross-coupling reactions are considered a valuable tool for constructing carbon-carbon bonds in small molecule drug discovery. However, the synthesis of chemical matter often represents a time-consuming and labour-intensive bottleneck. We demonstrate how machine learning methods trained on high-throughput experimentation (HTE) data can be leveraged to enable fast reaction condition selection for novel coupling partners.

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

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.

Deposition chamber technology as building blocks for a standardized brain-on-chip framework.

The in vitro modeling of human brain connectomes is key to exploring the structure-function relationship of the central nervous system. Elucidating this intricate relationship will allow better studying of the pathological mechanisms of neurodegeneration and hence result in improved drug screenings for complex neurological disorders, such as Alzheimer's and Parkinson diseases. However, currently used in vitro modeling technologies lack the potential to mimic physiologically relevant neural structures.

[Mission (im)possible : Setting up a neurological center 12,000 km away with telemedicine].

Background: Specialized neurological treatment decreases the mortality and morbidity of stroke patients. In many regions of the world an extensive coverage is not available. The cooperation between the Krankenhaus Nordwest (KHNW, Frankfurt, Germany) and the Government of Brunei Darussalam describes the set-up process of a specialized neurological center, including stroke unit, science and rehabilitation center.

Treatment of breast cancer with simultaneous integrated boost in hybrid plan technique : Influence of flattening filter-free beams.

Objective: The present study compares in silico treatment plans using hybrid plan technique during hypofractionated radiation of mammary carcinoma with simultaneous integrated boost (SIB). The influence of 6 MV photon radiation in flattening filter free (FFF) mode against the clinical standard flattening filter (FF) mode is to be examined.

Patients And Methods: RT planning took place with FF and FFF radiation plans for 10 left-sided breast cancer patients.