De novo drug design is a stationary way to build novel ligands in the confined pocket of
receptor by assembling the atoms or fragments, while molecular dynamics (MD) simulation …

Fragment-based drug (or lead) discovery (FBDD or FBLD) has developed in the last two
decades to become a successful key technology in the pharmaceutical industry for early …

We here present AutoGrow4, an open-source program for semi-automated computer-aided
drug discovery. AutoGrow4 uses a genetic algorithm to evolve predicted ligands on demand …

Recently, deep generative models have been regarded as promising tools in fragment-
based drug design (FBDD). Despite the growing interest in these models, they still face …

The interplay between life sciences and advancing technology drives a continuous cycle of
chemical data growth; these data are most often stored in open or partially open databases …

The combination of library-based screening and artificial intelligence (AI) has been
accelerating the discovery and optimization of hit ligands. However, the potential of AI to …

The integration of deep learning-based molecular generation models into drug discovery
has garnered significant attention for its potential to expedite the development process …

Fragment based drug design has emerged as an effective alternative to high throughput
screening for the identification of lead compounds in drug discovery in the past fifteen years …

In order to foster innovation and improve the effectiveness of drug discovery, there is a
considerable interest in exploring unknown 'chemical space'to identify new bioactive …

Computer-assisted molecular design supports drug discovery by suggesting novel
chemotypes and compound modifications for lead structure optimization. While the aspect of …