The ever-increasing accessible chemical space opens the door to the search for new chemical matter for drug discovery. However, this also poses a challenge for computer-aided drug design methods. Quantum mechanical (QM) methods provide a chemically accurate description of molecular properties, albeit restricted to small size systems. The availability of high-quality QM-based descriptors implemented in refined algorithms and combined with efficient computational protocols can help to prioritize hits, avoiding the occurrence of bias artifacts in chemical library screening.

Different efforts are underway to apply accurate methods to the ever-expanding accessible chemical space: (i) the development of computationally efficient semiempirical methods as well as the calibration of multiscale QM/MM methods, (ii) the redefinition of physics-based force fields tailored to QM, suitably refined to provide an accurate description of the complex network of intermolecular interactions, and (iii) the generation of QM-assisted machine learning (ML) models.