Details

Autor(en) / Beteiligte

• Tang, Yidan
• Moretti, Rocco
• Meiler, Jens

Titel

Recent Advances in Automated Structure-Based De Novo Drug Design

Ist Teil von

• Journal of chemical information and modeling, 2024-03, Vol.64 (6), p.1794-1805

Ort / Verlag

United States: American Chemical Society

Erscheinungsjahr

2024

Quelle

MEDLINE

Beschreibungen/Notizen

• As the number of determined and predicted protein structures and the size of druglike ‘make-on-demand’ libraries soar, the time-consuming nature of structure-based computer-aided drug design calls for innovative computational algorithms. De novo drug design introduces in silico heuristics to accelerate searching in the vast chemical space. This review focuses on recent advances in structure-based de novo drug design, ranging from conventional fragment-based methods, evolutionary algorithms, and Metropolis Monte Carlo methods to deep generative models. Due to the historical limitation of de novo drug design generating readily available drug-like molecules, we highlight the synthetic accessibility efforts in each category and the benchmarking strategies taken to validate the proposed framework.