Department of Materials and Environmental Chemistry, Stockholm University, Stockholm SE-106 91, Sweden.
J Chem Inf Model. 2024 May 13;64(9):3799-3811. doi: 10.1021/acs.jcim.3c01606. Epub 2024 Apr 16.
Adsorption free energies of 32 small biomolecules (amino acids side chains, fragments of lipids, and sugar molecules) on 33 different nanomaterials, computed by the molecular dynamics - metadynamics methodology, have been analyzed using statistical machine learning approaches. Multiple unsupervised learning algorithms (principal component analysis, agglomerative clustering, and K-means) as well as supervised linear and nonlinear regression algorithms (linear regression, AdaBoost ensemble learning, artificial neural network) have been applied. As a result, a small set of biomolecules has been identified, knowledge of adsorption free energies of which to a specific nanomaterial can be used to predict, within the developed machine learning model, adsorption free energies of other biomolecules. Furthermore, the methodology of grouping of nanomaterials according to their interactions with biomolecules has been presented.
采用分子动力学-元动力学方法计算了 32 种小生物分子（氨基酸侧链、脂质片段和糖分子）在 33 种不同纳米材料上的吸附自由能，并应用统计机器学习方法进行了分析。应用了多种无监督学习算法（主成分分析、凝聚聚类和 K-均值）以及有监督的线性和非线性回归算法（线性回归、AdaBoost 集成学习、人工神经网络）。结果确定了一小部分生物分子，如果知道它们与特定纳米材料的吸附自由能，则可以在开发的机器学习模型中预测其他生物分子的吸附自由能。此外，还提出了根据纳米材料与生物分子的相互作用对纳米材料进行分组的方法。
