Large language models (LLMs), such as GPT4 and LLaMA, are creating significant
advancements in natural language processing, due to their strong text encoding/decoding …

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

Application of deep learning techniques for de novo generation of molecules, termed as
inverse molecular design, has been gaining enormous traction in drug design. The …

Abstract Structure-based drug design uses three-dimensional geometric information of
macromolecules, such as proteins or nucleic acids, to identify suitable ligands. Geometric …

The technological advances of the past century, marked by the computer revolution and the
advent of high-throughput screening technologies in drug discovery, opened the path to the …

Recent advances and accomplishments of artificial intelligence (AI) and deep generative
models have established their usefulness in medicinal applications, especially in drug …

Deep generative models of molecules have grown immensely in popularity, trained on
relevant datasets, these models are used to search through chemical space. The …

Molecular design strategies are integral to therapeutic progress in drug discovery.
Computational approaches for de novo molecular design have been developed over the …

Machine learning has become a crucial tool in drug discovery and chemistry at large, eg, to
predict molecular properties, such as bioactivity, with high accuracy. However, activity …

Artificial intelligence (AI) and machine learning (ML) are expanding in popularity for broad
applications to challenging tasks in chemistry and materials science. Examples include the …