Discovery of Self-Assembling π-Conjugated Peptides By Active Learning-Directed Coarse-Grained Molecular Simulation

In this work we integrate coarse-grained molecular dynamics simulation, deep representational learning, and Bayesian optimization to discover pi-conjugated peptides capable of self-assembling into biocompatible optoelectronic nanoaggregates. The pi-conjugated peptides studied in this work are triblock molecules consisting of a central aromatic core flanked by peptide wings. This class of molecules have surfaced as an extensible building block for self-assembling electronics as they have experimentally been demonstrated to form mesoscopic fibers micrometers in length and nanometers in diameter, where overlaps between pi-orbitals in these supramolecular assemblies lead to the emergence optical and electronic properties. Edisonian trial-and-error discovery of these molecules through either experiment or simulation is rendered impossible due to the combinatorial exploration in the molecular design space of pi-cores and peptide wings. We efficiently navigate the design space in search of high-performing candidates by deploying an active learning procedure which integrates three machine learning components: (i) an unsupervised deep representation learning approach to learn continuous low-dimensional embeddings of the discrete molecular design space, (ii) a supervised surrogate model using Gaussian process regression to predict molecular performance measured in simulation as a function of this embedded space, and (iii) a Bayesian optimization of the surrogate model to dictate which molecules should be evaluated next. Using this protocol, we derive a converged surrogate model for predicting molecular performance of one particular peptide family comprising tetrapeptide wings and an oligophenylenevinylene pi core after sampling only 2.3% of the design space. We identify molecules we predict to possess unprecedented self-assembly behavior and optoelectronic activity while uncovering design rules to guide the rational engineering of these molecular systems.