Advancing molecular graphs with descriptors for the prediction of chemical reaction yields.

Chemical yield is the percentage of the reactants converted to the desired products. Chemists use predictive algorithms to select high-yielding reactions and score synthesis routes, saving time and reagents. This study suggests a novel graph neural network architecture for chemical yield prediction.

Ensembling machine learning models to boost molecular affinity prediction.

This study unites six popular machine learning approaches to enhance the prediction of a molecular binding affinity between receptors (large protein molecules) and ligands (small organic molecules). Here we examine a scheme where affinity of ligands is predicted against a single receptor - human thrombin, thus, the models consider ligand features only. However, the suggested approach can be repurposed for other receptors.

Toward efficient generation, correction, and properties control of unique drug-like structures.

Efficient design and screening of the novel molecules is a major challenge in drug and material design. This paper focuses on a multi-stage pipeline, in which several deep neural network models are combined to map discrete molecular representations into continuous vector space to later generate from it new molecular structures with desired properties. Here, the Attention-based Sequence-to-Sequence model is added to "spellcheck" and correct generated structures, while the oversampling in the continuous space allows generating candidate structures with desired distribution for properties and molecular descriptors, even for a small reference datasets.