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 …

Quantitative estimates of reaction barriers are essential for developing kinetic mechanisms
and predicting reaction outcomes. However, the lack of experimental data and the steep …

Synthetic polymers are versatile and widely used materials. Similar to small organic
molecules, a large chemical space of such materials is hypothetically accessible …

REINVENT 4 is a modern open-source generative AI framework for the design of small
molecules. The software utilizes recurrent neural networks and transformer architectures to …

Deep learning has become a powerful and frequently employed tool for the prediction of
molecular properties, thus creating a need for open-source and versatile software solutions …

The generation of attractive scaffolds for drug discovery efforts requires the expeditious
synthesis of diverse analogues from readily available building blocks. This endeavor …

Knowledge of critical properties, such as critical temperature, pressure, density, as well as
acentric factor, is essential to calculate thermo-physical properties of chemical compounds …

Characterizing uncertainty in machine learning models has recently gained interest in the
context of machine learning reliability, robustness, safety, and active learning. Here, we …

Deep graph neural networks are extensively utilized to predict chemical reactivity and
molecular properties. However, because of the complexity of chemical space, such models …

Abstract Machine Learning for ligand based virtual screening (LB-VS) is an important in-
silico tool for discovering new drugs in a faster and cost-effective manner, especially for …