From the start of a synthetic chemist's training, experiments are conducted based on recipes
from textbooks and manuscripts that achieve clean reaction outcomes, allowing the scientist …

How do you get into flow? We trained in flow chemistry during postdoctoral research and are
now applying it in new areas: materials chemistry, crystallization, and supramolecular …

Artificial intelligence (AI), machine learning (ML), and data science are leading to a
promising transformative paradigm. ML, especially deep learning and physics-informed ML …

Functionalization of C–H bonds is a key challenge in medicinal chemistry, particularly for
fragment-based drug discovery (FBDD) where such transformations require execution in the …

High-fidelity computer-aided experimentation is becoming more accessible with the
development of computing power and artificial intelligence tools. The advancement of …

Machine learning models are increasingly being utilized to predict outcomes of organic
chemical reactions. A large amount of reaction data is used to train these models, which is in …

The rapid development of novel synthetic routes for pharmaceutical compounds is highly
attractive for overcoming pandemic and epidemic-prone diseases like COVID-19. Herein …

As organic chemistry becomes an increasingly data-rich field, there is a need for methods to
rapidly build and parameterize models for further development. We demonstrate the …

Although kinetic analysis has traditionally been conducted in a batch vessel, continuous-
flow aided kinetic analysis continues to swell in popularity. This can be partly attributed to …

Kinetic data for reactions between phenyl hydrazine and 1, 3-diketones (Knorr pyrazole
synthesis) were acquired by using transient flow methods. Supported by further …