Department of Biomedical Engineering, Wroclaw University of Science and Technology, 50-370, Wroclaw, Poland.
Department of Experimental Physics, Wroclaw University of Science and Technology, 50-370, Wroclaw, Poland.
Sci Rep. 2024 Feb 26;14(1):4641. doi: 10.1038/s41598-024-55418-6.
Antimicrobial resistance presents a pressing challenge to public health, which requires the search for novel antimicrobial agents. Various experimental and theoretical methods are employed to understand drug-target interactions and propose multistep solutions. Nonetheless, efficient screening of drug databases requires rapid and precise numerical analysis to validate antimicrobial efficacy. Diptool addresses this need by predicting free energy barriers and local minima for drug translocation across lipid membranes. In the current study employing Diptool free energy predictions, the thermodynamic commonalities between selected antimicrobial molecules were characterized and investigated. To this end, various clustering methods were used to identify promising groups with antimicrobial activity. Furthermore, the molecular fingerprinting and machine learning approach (ML) revealed common structural elements and physicochemical parameters in these clusters, such as long carbon chains, charged ammonium groups, and low dipole moments. This led to the establishment of guidelines for the selection of effective antimicrobial candidates based on partition coefficients (logP) and molecular mass ranges. These guidelines were implemented within the Reinforcement Learning for Structural Evolution (ReLeaSE) framework, generating new chemicals with desired properties. Interestingly, ReLeaSE produced molecules with structural profiles similar to the antimicrobial agents tested, confirming the importance of the identified features. In conclusion, this study demonstrates the ability of molecular fingerprinting and AI-driven methods to identify promising antimicrobial agents with a broad range of properties. These findings deliver substantial implications for the development of antimicrobial drugs and the ongoing battle against antibiotic-resistant bacteria.
抗菌药物耐药性对公共卫生构成了紧迫挑战，这需要寻找新的抗菌药物。人们采用各种实验和理论方法来了解药物-靶标相互作用，并提出多步骤解决方案。然而，要对药物数据库进行有效的筛选，需要进行快速、准确的数值分析来验证抗菌效果。Diptool 通过预测药物穿过脂质膜的自由能势垒和局部最小值来满足这一需求。在当前这项研究中，我们利用 Diptool 的自由能预测，对选定的抗菌分子之间的热力学共性进行了特征分析和研究。为此，我们使用了各种聚类方法来识别具有抗菌活性的有希望的群组。此外，分子指纹图谱和机器学习方法（ML）揭示了这些簇中的共同结构元素和物理化学参数，例如长碳链、带电荷的铵基团和低偶极矩。这导致了根据分配系数（logP）和分子质量范围选择有效抗菌候选物的指南的建立。这些指南在强化学习结构进化（ReLeaSE）框架内实施，生成具有所需特性的新化学物质。有趣的是，ReLeaSE 生成的分子具有与测试的抗菌剂相似的结构特征，这证实了所确定特征的重要性。总之，本研究表明，分子指纹图谱和人工智能驱动的方法能够识别具有广泛特性的有前途的抗菌药物。这些发现对抗生素耐药菌的不断斗争和抗菌药物的开发具有重要意义。
