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 …

Burgeoning developments in machine learning (ML) and its rapidly growing adaptations in
chemistry are noteworthy. Motivated by the successful deployments of ML in the realm of …

Advances in deep neural network (DNN)-based molecular property prediction have recently
led to the development of models of remarkable accuracy and generalization ability, with …

With access to large datasets, deep neural networks (DNN) have achieved human-level
accuracy in image and speech recognition tasks. However, in chemistry data is inherently …

With the recent advancement of deep learning, molecular representation learning--
automating the discovery of feature representation of molecular structure, has attracted …

Recently, supervised machine learning has been ascending in providing new predictive
approaches for chemical, biological, and materials sciences applications. In this …

Chemical databases store information in text representations, and the SMILES format is a
universal standard used in many cheminformatics software. Encoded in each SMILES string …

The development of efficient models for predicting specific properties through machine
learning is of great importance for the innovation of chemistry and material science …

Efficient and accurate prediction of molecular properties, such as lipophilicity and solubility,
is highly desirable for rational compound design in chemical and pharmaceutical industries …

Established molecular machine learning models process individual molecules as inputs to
predict their biological, chemical, or physical properties. However, such algorithms require …