Generative machine learning models sample molecules from chemical space without the
need for explicit design rules. To enable the generative design of innovative molecular …

Over the past decade, the amount of biomedical data available has grown at unprecedented
rates. Increased automation technology and larger data volumes have encouraged the use …

Abstract Machine learning (ML) promises to tackle the grand challenges in chemistry and
speed up the generation, improvement and/or ordering of research hypotheses. Despite the …

Conformer generation, the assignment of realistic 3D coordinates to a small molecule, is
fundamental to structure-based drug design. Conformational ensembles are required for …

This study introduces a new de novo design algorithm called GENERA that combines the
capabilities of a deep-learning algorithm for automated drug-like analogue design, called …

Automatic design of organic materials requires black-box optimization in a vast chemical
space. In conventional molecular design algorithms, a molecule is built as a combination of …

The aim of computational molecular design is the identification of promising hypothetical
molecules with a predefined set of desired properties. We address the issue of accelerating …

Chemical engineers rely on models for design, research, and daily decision-making, often
with potentially large financial and safety implications. Previous efforts a few decades ago to …

This microperspective covers the most recent research outcomes of artificial intelligence (AI)
generated molecular structures from the point of view of the medicinal chemist. The main …

Owing to the increase in freely available software and data for cheminformatics and
structural bioinformatics, research for computer-aided drug design (CADD) is more and …