deep learning antimicrobial peptides Antimicrobial peptides - Antimicrobialpeptide genes Machine learning Harnessing Deep Learning for the Design and Discovery of Antimicrobial Peptides

Discovery ofantimicrobial peptidesin the global microbiome with machinelearning The escalating crisis of antibiotic resistance has spurred an urgent need for novel therapeutic strategies. Antimicrobial peptides (AMPs), a vital component of innate immunity, are emerging as promising alternatives to traditional antibiotics. These short peptides exhibit potent activity against a broad spectrum of microorganisms, including bacteria and fungi, often through non-specific mechanisms that limit the development of resistance. However, the traditional methods for discovering and designing effective antimicrobial peptides are often time-consuming and resource-intensive. This is where the power of deep learning and machine learning is revolutionizing the fieldExplainable deep learning and virtual evolution identifies ....

Deep learning models are demonstrating remarkable success in accelerating the design, prediction, and discovery of novel antimicrobial peptides作者：C Wang·2021·被引用次数：111—In this study, a long short-term memory (LSTM) generative model and a bidirectional LSTM classification model were constructed to design short novel AMP .... These computational approaches leverage vast datasets to identify patterns and predict the efficacy of peptide sequences, significantly streamlining the drug discovery processDeep‐Learning Driven Identification of Novel Antimicrobial .... Researchers are developing sophisticated deep learning models capable of not only identifying potential antimicrobial peptides but also designing new ones with enhanced properties. For instance, de novo design of antimicrobial peptides is becoming increasingly feasible, allowing for the creation of molecules tailored for specific targets or exhibiting bifunctional activities, such as those active against both bacteria and viruses作者：D Veltri·2018·被引用次数：563—In this work, we utilizedeep learningto recognizeantimicrobialactivity. We propose a neural network model with convolutional and recurrent layers that ....

The application of machine learning in this domain extends to various aspects of antimicrobial peptide research. Machine learning algorithms can be employed to predict the minimum inhibitory concentration (MIC) of peptides, a crucial parameter for assessing their potency. Furthermore, machine learning can aid in understanding the structure-activity relationships (SARs) of antimicrobial peptides, enabling researchers to fine-tune their physicochemical properties and thereby improve their therapeutic utility. This includes methods for predicting membrane-permeating activity and identifying short antimicrobial peptides with high efficacy.

Several deep learning frameworks and models have been developed to tackle the challenges in antimicrobial peptide research. These include approaches like DLFea4AMPGen, which focuses on de novo design, and iAMPCN, a deep-learning approach for identifying antimicrobial peptides. Other notable advancements include EvoGradient, an AI-based explainable deep learning model that predicts antimicrobial peptide potency and facilitates virtual modification, and AMPGP, an advanced deep learning framework for generating high-quality antimicrobial peptide sequences. AMPlify, an attentive deep learning model, is contributing to the discovery of novel antimicrobial peptides through in silico methods.

Beyond prediction and design, machine learning is also being utilized for the discovery of antimicrobial peptides within vast biological repositories, such as the global microbiome. Approaches like the one presented by Santos-Júnior et al. leverage machine learning to predict antimicrobial peptides within these complex datasets. Similarly, deep learning techniques are being harnessed to mine proteomes of extinct organisms for the discovery of antibiotic peptides, showcasing the broad applicability of these computational tools.

The integration of deep learning and machine learning into antimicrobial peptide research offers a powerful pathway to combatting the growing threat of antibiotic resistance.dsAMP and dsAMPGAN: Deep Learning Networks for ... By enabling faster, more efficient, and more targeted design and discovery, these technologies are poised to revolutionize the development of new antimicrobial therapies. The ongoing research in areas such as geometric deep learning and peptide LLM (Large Language Models for peptides) further highlights the dynamic and rapidly evolving nature of this field, promising even more innovative solutions in the near future.