“分子机器学习”领域的40个英文关键词

给出“分子机器学习”领域的40个英文关键词：要求每个英文关键词有音标，还给出中文翻译。

1. Machine learning [məˈʃiːn ˈlɜːrnɪŋ] - 机器学习
2. Molecular - [məˈlɛkjələr] - 分子的
3. Artificial intelligence - [ˌɑːrtɪˈfɪʃəl ɪnˈtɛlɪdʒəns] - 人工智能
4. Deep learning - [diːp ˈlɜːrnɪŋ] - 深度学习
5. Chemical - [ˈkɛmɪkəl] - 化学的
6. Neural network - [ˈnʊrəl ˈnɛtˌwɜːrk] - 神经网络
7. Quantum computing - [ˈkwɑːntəm kəmˈpjuːtɪŋ] - 量子计算
8. Bioinformatics - [ˌbaɪoʊˌɪnfərˈmætɪks] - 生物信息学
9. Computational - [ˌkɑːmpjəˈteɪʃənəl] - 计算的
10. Algorithms - [ˈælɡəˌrɪðəmz] - 算法
11. Protein - [ˈproʊˌtiːn] - 蛋白质
12. Big data - [bɪɡ ˈdeɪtə] - 大数据
13. Quantum chemistry - [ˈkwɑːntəm ˈkɛmɪstri] - 量子化学
14. Drug discovery - [drʌɡ dɪsˈkʌvəri] - 药物发现
15. Convolutional neural network - [ˌkɑːnvəˈluːʃənəl ˈnʊrəl ˈnɛtˌwɜːrk] - 卷积神经网络
16. Machine vision - [məˈʃiːn ˈvɪʒən] - 机器视觉
17. Molecular dynamics - [məˈlɛkjəl dɪˈnæmɪks] - 分子动力学
18. Pattern recognition - [ˈpætərn ˌrɛkəɡˈnɪʃən] - 模式识别
19. Quantum machine learning - [ˈkwɑːntəm məˈʃiːn ˈlɜːrnɪŋ] - 量子机器学习
20. Gene expression - [dʒiːn ɪkˈsprɛʃən] - 基因表达
21. Reinforcement learning - [ˌriːɪnˈfɔːrsmənt ˈlɜːrnɪŋ] - 强化学习
22. Molecular recognition - [məˈlɛkjəl ˌrɛkəɡˈnɪʃən] - 分子识别
23. Data mining - [ˈdeɪtə ˈmaɪnɪŋ] - 数据挖掘
24. Quantum information - [ˈkwɑːntəm ˌɪnfərˈmeɪʃən] - 量子信息
25. Genetic algorithms - [dʒəˈnɛtɪk ˈælɡəˌrɪðəmz] - 遗传算法
26. Spectroscopy - [ˌspɛkˈtrɑːskəpi] - 光谱学
27. Molecular modeling - [məˈlɛkjəl ˈmɑːdəlɪŋ] - 分子建模
28. Pattern analysis - [ˈpætərn əˈnæləsis] - 模式分析
29. Quantum algorithms - [ˈkwɑːntəm ˈælɡəˌrɪðəmz] - 量子算法
30. Drug design - [drʌɡ dɪˈzaɪn] - 药物设计
31. Statistical learning - [stəˈtɪstɪkəl ˈlɜːrnɪŋ] - 统计学习
32. Molecular simulation - [məˈlɛkjəl ˌsɪmjəˈleɪʃən] - 分子模拟
33. Recurrent neural network - [rɪˈkɜːrənt ˈnʊrəl ˈnɛtˌwɜːrk] - 循环神经网络
34. Cheminformatics - [ˌkɛmɪnˈfɔːrmætɪks] - 化学信息学
35. Data analysis - [ˈdeɪtə əˈnæləsɪs] - 数据分析
36. Quantum machine vision - [ˈkwɑːntəm məˈʃiːn ˈvɪʒən] - 量子机器视觉
37. Molecular recognition force field - [məˈlɛkjəl ˌrɛkəɡˈnɪʃən fɔːrs fiːld] - 分子识别力场
38. Anomaly detection - [əˈnɑːməli dɪˈtɛkʃən] - 异常检测
39. Predictive modeling - [prɪˈdɪktɪv ˈmɑːdəlɪŋ] - 预测建模
40. Chemical informatics - [ˈkɛmɪkəl ˌɪnfərˈmætɪks] - 化学信息学

以上40个单词，写一篇关于“分子机器学习”的英文综述，并翻译成中文，至少500字

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术结合起来，以设计和优化能够完成特定任务的分子。近年来，MML因其在药物发现、材料科学和化学合成等领域的潜在应用而受到广泛关注。

MML的主要目标之一是开发能够基于化学结构准确预测分子性质和行为的模型。这些模型利用大量已知分子及其对应属性的数据集进行训练。然后使用神经网络和支持向量机等机器学习算法来学习分子结构和性质之间的模式和关系。

在药物发现方面，MML能够彻底改变新药物开发的模式。通过预测分子的活性和毒性，研究人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，例如改善溶解性和生物利用度。

在材料科学方面，MML可以帮助发现和设计具有所需性质的新材料。例如，研究人员可以使用MML预测材料的带隙，这是决定电子性质的重要参数。通过优化材料的分子结构，科学家可以为特定应用定制其性能，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，并减少与实验试错相关的时间和成本。这可以导致更高效和可持续的合成方法。

然而，将机器学习应用于分子系统存在一些挑战。一个主要挑战是巨大的化学空间，因为可能的分子数量是天文数字般庞大的。此外，缺乏用于训练模型的大型可靠数据集也是一个挑战。此外，在化学系统环境中解释机器学习模型的能力仍然是一个正在研究的领域。

尽管存在这些挑战，分子机器学习在加速发现和开发具有所需性质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术结合起来，MML有可能彻底改变各个领域，并在药物发现、材料科学和化学合成等领域实现突破。