Effective representation of molecules is a crucial factor affecting the performance of artificial
intelligence models. This study introduces a flexible, fragment-based, multiscale molecular …

Background In recent years, in silico molecular design is regaining interest. To generate on
a computer molecules with optimized properties, scoring functions can be coupled with a …

Abstract Machine learning models have found numerous successful applications in
computational drug discovery. A large body of these models represents molecules as …

Accurate molecular property or activity prediction is one of the main goals in computer-aided
drug design. Quantitative structure-activity relationship (QSAR) modeling and machine …

Molecular generative models trained with small sets of molecules represented as SMILES
strings can generate large regions of the chemical space. Unfortunately, due to the …

Molecular string representations are a key asset in cheminformatics and are becoming
increasingly relevant to the general chemical community, due to the steadily growing impact …

A key component of automated molecular design is the generation of compound ideas for
subsequent filtering and assessment. Recently deep learning approaches have been …

Deep neural networks have become increasingly important in recent years for creating
molecules with desirable properties. In general, SMILES strings are used to train deep …

Chemical language models (CLMs) can be employed to design molecules with desired
properties. CLMs generate new chemical structures in the form of textual representations …

In the past few years, we have witnessed a renaissance of the field of molecular de novo
drug design. The advancements in deep learning and artificial intelligence (AI) have …