Predicting Antimicrobial Activity of Conjugated Oligoelectrolyte Molecules via Machine Learning.

New antibiotics are needed to battle growing antibiotic resistance, but the development process from hit, to lead, and ultimately to a useful drug takes decades. Although progress in molecular property prediction using machine-learning methods has opened up new pathways for aiding the antibiotics development process, many existing solutions rely on large data sets and finding structural similarities to existing antibiotics. Challenges remain in modeling unconventional antibiotic classes that are drawing increasing research attention. In response, we developed an antimicrobial activity prediction model for conjugated oligoelectrolyte molecules, a new class of antibiotics that lacks extensive prior structure-activity relationship studies. Our approach enables us to predict the minimum inhibitory concentration for E. coli K12, with 21 molecular descriptors selected by recursive elimination from a set of 5305 descriptors. This predictive model achieves an R2 of 0.65 with no prior knowledge of the underlying mechanism. We find the molecular representation optimum for the domain is the key to good predictions of antimicrobial activity. In the case of conjugated oligoelectrolytes, a representation reflecting the three-dimensional shape of the molecules is most critical. Although it is demonstrated with a specific example of conjugated oligoelectrolytes, our proposed approach for creating the predictive model can be readily adapted to other novel antibiotic candidate domains.

High-throughput computation and evaluation of raman spectra.

Raman spectroscopy is used ubiquitously in the characterization of condensed materials, spanning from biomaterials, minerals to polymers, as it provides a unique fingerprint of local bonding and environment. In this work, we design and demonstrate a robust, automatic computational workflow for Raman spectra that employs first-principle calculations based on density functional perturbation theory. A set of computational results are compared to Raman spectra obtained from established experimental databases to estimate the accuracy of the calculated properties across chemical systems and structures. Details regarding the computational methodology and technical validation are presented along with the format of our publicly available data record.