We present a Transformer-based artificial neural network to convert images of organic structures to molecular structures. We demonstrate that the Transformer-based architecture can gather chemical insights from our generator with almost absolute confidence.

We demonstrated that the parametric t-SNE combined with reaction difference fingerprints could provide a tool for the projection of chemical reactions onto a low-dimensional manifold for easy exploration of reaction space.

We developed a Transformer-based artificial neural architecture to translate between SMILES and IUPAC chemical notations: Struct2IUPAC and IUPAC2Struct.

We present graph-convolutional neural networks for the prediction of binding constants of protein-ligand complexes.

We applied recommender systems methods for the prediction of the antiviral activity class (active/inactive) for compounds extracted from ChEMBL.

We performed a comparative study of prediction multitask toxicity for a broad chemical space using different descriptors and modeling algorithms and applied multitask learning for a large toxicity data set extracted from the Registry of Toxic Effects of Chemical Substances (RTECS).

We review the recent developments in multi-learning approaches as well as cover the freely available tools and packages that can be used to perform such studies.

A parametric t-SNE approach based on deep feed-forward neural networks was applied to the chemical space visualization problem.

We present a new method for predicting complex physical-chemical properties of organic molecules. The approach utilizes 3D convolutional neural network (ActivNet4) that uses solvent spatial distributions around solutes as input.