Quantum-assisted fragment-based automated structure generator (QFASG) for small molecule design: an in vitro study

The significance of automated drug design using virtual generative models has steadily grown in recent years. While deep learning-driven solutions have received growing attention, only a few modern AI-assisted generative chemistry platforms have demonstrated the ability to produce valuable structures. At the same time, virtual fragment-based drug design, which was previously less popular due to the high computational costs, has become more attractive with the development of new chemoinformatic techniques and powerful computing technologies. We developed Quantum-assisted Fragment-based Automated Structure Generator (QFASG), a fully automated algorithm designed to construct ligands for a target protein using a library of molecular fragments. QFASG was applied to generating new structures of CAMKK2 and ATM inhibitors. New low-micromolar inhibitors of CAMKK2 and ATM were designed using the algorithm. These findings highlight the algorithm's potential in designing primary hits for further optimization and showcase the capabilities of QFASG as an effective tool in this field.