Novel generative active learning algorithms for exploring the space of antimicrobial peptides to respond to antibiotics resistance

点击翻译按钮获取中文摘要

We propose to develop and evaluate a new machine learning (ML) methodology for active exploration of the vast space of potential antimicrobial peptide (AMP) sequences for the purpose of contributing to the design of a rapid, cost-effective and versatile pipeline for discovering new antibiotic activities. AMPs form a class of molecules that are widely used in nature to combat pathogens and are now used to address antimicrobial resistance (AMR), the evolutionary process by which pathogens have been mutating to become resistant to all known antibiotics. AMR is a major and growing public health concern, with 10 million deaths per year expected by 2050 with business as usual. Antibiotic administration to livestock animals accounts for 66% of all antibiotics used in the world, and is a major driver of antibiotic resistance. It is thus imperative that alternative antibiotic strategies are developed for livestock.We are developing a new active learning, reinforcement learning and generative modeling approaches for drug design and molecular genetic methodologies to experimentally evaluate the ML-generated candidates with the aim of designing new AMPs against major bacterial pathogens in livestock animals. We will integrate the whole sequence of interactions between the ML system generating candidate AMPs and the biological assays to screen the predicted candidates. These generate-and-test cycles are reiterated to help novel ML methods based on generative flow networks to discover structure in the data by appropriately exploring a diverse set of promising regions of bioactive peptide space. Because peptide sequence diversity is enormous (e.g., there are 20^50 possible 50-mer peptides), ML tools hold the promise of efficiently searching this space but require new algorithmic innovations to succeed, especially to address the need for generating diverse batches of candidates. The proposed synthetic biology methods for the construction and rapid screening of large peptide libraries is crucial for ML-driven AMP discovery. Active AMPs that inhibit bacterial growth will be identified by DNA sequencing to inform the next round of ML. We will generate peptide libraries for cyclized AMPs, which often have improved stability and more drug-like properties. We will then apply ML methods to design more potent combinations of AMPs. This ML-based antibiotic discovery pipeline will provide a powerful new approach to combat the global threat of antimicrobial resistance.