Artificial Neural Networks (NN) are already heavily involved in methods and applications for
frequent tasks in the field of computational chemistry such as representation of potential …

Artificial intelligence (AI)-driven methods can vastly improve the historically costly drug
design process, with various generative models already in widespread use. Generative …

Structure-based methods in drug discovery have become an integral part of the modern
drug discovery process. The power of virtual screening lies in its ability to rapidly and cost …

Deep learning has achieved impressive results in various fields such as computer vision
and natural language processing, making it a powerful tool in biology. Its applications now …

The application of ab initio molecular dynamics (AIMD) for the explicit modeling of reactions
at solid–liquid interfaces in electrochemical energy conversion systems like batteries and …

Geometric deep learning models, which incorporate the relevant molecular symmetries
within the neural network architecture, have considerably improved the accuracy and data …

Forecasting the interaction between compounds and proteins is crucial for discovering new
drugs. However, previous sequence-based studies have not utilized three-dimensional (3D) …

Biological intelligence is remarkable in its ability to produce complex behaviour in many
diverse situations through data efficient, generalisable and transferable skill acquisition. It is …

Molecular docking, a key technique in structure-based drug design, plays pivotal roles in
protein–ligand interaction modeling, hit identification and optimization, in which accurate …

Abstract Model quality evaluation is a crucial part of protein structural biology. How to
distinguish high-quality models from low-quality models, and to assess which high-quality …