  Despite the prevalence and many successes of deep learning applications in de
novo molecular design, the problem of peptide generation targeting specific
proteins remains unsolved. A main barrier for this is the scarcity of the
high-quality training data. To tackle the issue, we propose a novel machine
learning based peptide design architecture, called Latent Space Approximate
Trajectory Collector (LSATC). It consists of a series of samplers on an
optimization trajectory on a highly non-convex energy landscape that
approximates the distributions of peptides with desired properties in a latent
space. The process involves little human intervention and can be implemented in
an end-to-end manner. We demonstrate the model by the design of peptide
extensions targeting Beta-catenin, a key nuclear effector protein involved in
canonical Wnt signalling. When compared with a random sampler, LSATC can sample
peptides with $36\%$ lower binding scores in a $16$ times smaller interquartile
range (IQR) and $284\%$ less hydrophobicity with a $1.4$ times smaller IQR.
LSATC also largely outperforms other common generative models. Finally, we
utilized a clustering algorithm to select 4 peptides from the 100 LSATC
designed peptides for experimental validation. The result confirms that all the
four peptides extended by LSATC show improved Beta-catenin binding by at least
$20.0\%$, and two of the peptides show a $3$ fold increase in binding affinity
as compared to the base peptide.
