Molecular machine learning (MML) is an emerging field that combines molecular science and machine learning techniques to design and optimize molecules for specific tasks. MML has gained significant attention in recent years for its potential applications in drug discovery, materials science, and chemical synthesis.

One of the main goals of MML is to develop models that can accurately predict the properties and behaviors of molecules based on their chemical structure. These models are trained using large datasets of known molecules and their corresponding properties. Machine learning algorithms, such as neural networks and support vector machines, are then used to learn patterns and relationships between molecular structure and properties.

In drug discovery, MML has the potential to revolutionize the development of new therapeutics. By predicting the activity and toxicity of molecules, researchers can identify promising drug candidates and prioritize them for further experimental testing. Additionally, MML can be used to design molecules with specific drug-like properties, such as improved solubility and bioavailability.

In materials science, MML can aid in the discovery and design of new materials with desired properties. For example, researchers can use MML to predict the bandgap of a material, which is an important parameter that determines its electronic properties. By optimizing the molecular structure of a material, scientists can tailor its properties for specific applications, such as solar cells or batteries.

In chemical synthesis, MML can help streamline the process of designing and optimizing reaction conditions. By predicting the reactivity and selectivity of different molecules, chemists can optimize reaction parameters and reduce the time and cost associated with experimental trial and error. This can lead to more efficient and sustainable synthesis methods.

However, there are challenges in applying machine learning to molecular systems. One major challenge is the vast chemical space, as the number of possible molecules is astronomically large. Additionally, the lack of large and reliable datasets for training models poses a challenge. Furthermore, the interpretability of machine learning models in the context of chemical systems is an ongoing research area.

Despite these challenges, MML holds great promise for accelerating the discovery and development of new molecules with desired properties. Through the integration of molecular science and machine learning techniques, MML has the potential to revolutionize various fields and enable breakthroughs in areas such as drug discovery, materials science, and chemical synthesis.

分子机器学习（Molecular machine learning，MML）是一个新兴领域，将分子科学和机器学习技能结合起来，以设计和优化能够完成特界说务的分子。
近年来，MML因其在药物创造、材料科学和化学合成等领域的潜在运用而受到广泛关注。

MML的紧张目标之一是开拓能够基于化学构造准确预测分子性子和行为的模型。
这些模型利用大量已知分子及其对应属性的数据集进行演习。
然后利用神经网络和支持向量机等机器学习算法来学习分子构造和性子之间的模式和关系。

在药物创造方面，MML能够彻底改变新药物开拓的模式。
通过预测分子的活性和毒性，研究职员可以识别出有前景的候选药物，并对其进行进一步的实验测试。
此外，MML还可以用于设计具有特定药物样性能的分子，例如改进溶解性和生物利用度。

在材料科学方面，MML可以帮助创造和设计具有所需性子的新材料。
例如，研究职员可以利用MML预测材料的带隙，这是决定电子性子的主要参数。
通过优化材料的分子构造，科学家可以为特定运用定制其性能，例如太阳能电池或电池。

在化学合成方面，MML可以帮助简化设计和优化反应条件的过程。
通过预测不同分子的反应性和选择性，化学家可以优化反应参数，并减少与实验试错干系的韶光和本钱。
这可以导致更高效和可持续的合成方法。

然而，将机器学习运用于分子系统存在一些寻衅。
一个紧张寻衅是巨大的化学空间，由于可能的分子数量是天文数字般弘大的。
此外，缺罕用于演习模型的大型可靠数据集也是一个寻衅。
此外，在化学系统环境中阐明机器学习模型的能力仍旧是一个正在研究的领域。

只管存在这些寻衅，分子机器学习在加速创造和开拓具有所需性子的新分子方面有着巨大的潜力。
通过将分子科学和机器学习技能结合起来，MML有可能彻底改变各个领域，并在药物创造、材料科学和化学合成等领域实现打破。