Geometric deep learning (GDL) is based on neural network architectures that incorporate
and process symmetry information. GDL bears promise for molecular modelling applications …

With Artificial Intelligence (AI) increasingly permeating various aspects of society, including
healthcare, the adoption of the Transformers neural network architecture is rapidly changing …

We apply a Transformer architecture, specifically BERT, to learn flexible and high quality
molecular representations for drug discovery problems. We study the impact of using …

Transformer models have emerged as pivotal tools within the realm of drug discovery,
distinguished by their unique architectural features and exceptional performance in …

Deep learning based methods have been widely applied to predict various kinds of
molecular properties in the pharmaceutical industry with increasingly more success. In this …

Rapid and accurate prediction of basic physicochemical parameters of molecules will
greatly accelerate the target-orientated design of novel reactions and materials but has been …

One of the main obstacles to the successful treatment of cancer is the phenomenon of drug
resistance. A common strategy to overcome resistance is the use of combination therapies …

Although the literature contains numerous examples of successful virtual screening
campaigns, the field continues to evolve and deal with new challenges. In this special issue …

In this study, we introduce semi-supervised machine learning models designed to predict
molecular properties. Our model employs a two-stage approach, involving pre-training and …

Finding new enzyme variants with the desired substrate scope requires screening through a
large number of potential variants. In a typical in silico enzyme engineering workflow, it is …