Accelerating Antimicrobial Discovery With Controllable Deep Generative Models and Molecular Dynamics

Abstract

De novo therapeutic design is challenged by a vast chemical repertoire and multiple constraints such as high broad-spectrum potency and low toxicity. We propose CLaSS (Controlled Latent attribute Space Sampling) — an efficient computational method for attribute-controlled generation of molecules, which leverages guidance from classifiers trained on an informative latent space of molecules modeled using a deep generative autoencoder. We further screen the generated molecules for additional key attributes by using a set of deep learning classifiers in conjunction with novel physicochemical features derived from high-throughput molecular simulations. The proposed approach is employed for designing non-toxic antimicrobial peptides (AMPs) with strong broad-spectrum potency, which are emerging drug candidates for tackling antibiotic resistance. Synthesis and wet lab testing of only twenty designed sequences identified two novel and minimalist AMPs with high potency against diverse Gram-positive and Gram-negative pathogens, including the multidrug-resistant K. pneumoniae, as well as low in vitro and in vivo toxicity.Live-cell confocal imaging revealed that the bactericidal mode of action of the peptides occurs through membrane pore formation. Both antimicrobials mitigate the onset of drug resistance and are effective against antibiotic-resistant strains. The proposed approach thus presents a viable path for faster and efficient discovery of potent and selective broad-spectrum antimicrobials.