  Data-driven generation of molecules with desired properties, also known as
inverse molecular design (IMD), has attracted significant attention in recent
years. Despite the significant progress in the accuracy and diversity of
solutions, existing IMD methods lag behind in terms of trustworthiness. The
root issue is that the design process of these methods is increasingly more
implicit and indirect, and this process is also isolated from the native
forward process (NFP), the ground-truth function that models the molecular
dynamics. Following this insight, we propose TrustMol, an IMD method built to
be trustworthy. For this purpose, TrustMol relies on a set of technical
novelties including a new variational autoencoder network. Moreover, we propose
a latent-property pairs acquisition method to effectively navigate the
complexities of molecular latent optimization, a process that seems intuitive
yet challenging due to the high-frequency and discontinuous nature of molecule
space. TrustMol also integrates uncertainty-awareness into molecular latent
optimization. These lead to improvements in both explainability and reliability
of the IMD process. We validate the trustworthiness of TrustMol through a wide
range of experiments.
