  Optimizing molecular design and discovering novel chemical structures to meet
certain objectives, such as quantitative estimates of the drug-likeness score
(QEDs), is NP-hard due to the vast combinatorial design space of discrete
molecular structures, which makes it near impossible to explore the entire
search space comprehensively to exploit de novo structures with properties of
interest. To address this challenge, reducing the intractable search space into
a lower-dimensional latent volume helps examine molecular candidates more
feasibly via inverse design. Autoencoders are suitable deep learning
techniques, equipped with an encoder that reduces the discrete molecular
structure into a latent space and a decoder that inverts the search space back
to the molecular design. The continuous property of the latent space, which
characterizes the discrete chemical structures, provides a flexible
representation for inverse design in order to discover novel molecules.
However, exploring this latent space requires certain insights to generate new
structures. We propose using a convex hall surrounding the top molecules in
terms of high QEDs to ensnare a tight subspace in the latent representation as
an efficient way to reveal novel molecules with high QEDs. We demonstrate the
effectiveness of our suggested method by using the QM9 as a training dataset
along with the Self- Referencing Embedded Strings (SELFIES) representation to
calibrate the autoencoder in order to carry out the Inverse molecular design
that leads to unfold novel chemical structure.
