利用可控的深层生成模型和分子动力学加速抗菌剂的发现
Accelerating Antimicrobial Discovery with Controllable Deep Generative Models and Molecular Dynamics
摘要：
从头治疗设计受到大量化学成分和多种限制的挑战，例如，广谱高效、低毒。我们提出了CLaSS（受控潜在属性空间采样）-一种用于属性控制分子生成的有效计算方法，该方法利用了在使用深度生成自动编码器建模的分子信息潜在空间上训练的分类器的指导。我们通过使用深度学习分类器结合来自原子模拟的新特征来筛选生成的分子的其他关键属性。所提出的方法被证明用于设计具有强广谱效力的无毒抗菌肽（AMP），这是解决抗生素耐药性的新兴候选药物。仅对20个设计序列进行合成和测试，就鉴定出两种新型和最低限度的AMP，其对多种革兰氏阳性和革兰氏阴性病原体具有高效力，包括一种多重耐药和一种耐药的K.肺炎，通过膜孔形成。两种抗菌剂在体外和体内均表现出较低的毒性，并可缓解耐药性的发生。因此，所提出的方法为更快、更有效地发现强效和选择性广谱抗菌剂提供了一条可行的途径。
英文摘要：
De novo therapeutic design is challenged by a vast chemical repertoire and multiple constraints, e.g., 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 screen the generated molecules for additional key attributes by using deep learning classifiers in conjunction with novel features derived from atomistic simulations. The proposed approach is demonstrated for designing non-toxic antimicrobial peptides (AMPs) with strong broad-spectrum potency, which are emerging drug candidates for tackling antibiotic resistance. Synthesis and testing of only twenty designed sequences identified two novel and minimalist AMPs with high potency against diverse Gram-positive and Gram-negative pathogens, including one multidrug-resistant and one antibiotic-resistant K. pneumoniae, via membrane pore formation. Both antimicrobials exhibit low in vitro and in vivo toxicity and mitigate the onset of drug resistance. The proposed approach thus presents a viable path for faster and efficient discovery of potent and selective broad-spectrum antimicrobials.
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