MOLINSTINCTS Product Summary

Mol-Instincts is…

A database providing thermo-physico-chemical & transport properties and fundamental molecular information of pure compounds.

Compounds Available:

More than 2.5 million compounds including:

  • Hydrocarbons, hetero-compounds (including atoms other than C and H), free radicals, and drug-like compounds
  • Compounds included in fuels such as gasoline, jet-fuel, diesel, and biodiesel
  • Compounds included in chemical processes such as thermal cracking, catalytic cracking, hydrocracking, desulfurization, isomerization,
    catalytic reforming, combustion, gas-to-liquid (GTL), coal-to-liquid (CTL), and methanol-to-olefin/gasoline (MTO/MTG)

Limitation: The maximum number of carbon atoms currently up to 57 (mostly to 25). Pure compounds with C, H, N, O, and S atoms only.

Property Data & Molecular Information Available:

More than 10 billion sets of data and information including:

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How Mol-Instincts Developed?

All the data and information were determined by…

The most advanced technology based on quantum chemistry, QSPR (Quantitative Structure–Property Relationships), and/or ANN (Artificial Neural Network) followed by 5+ years of professional verification with the corresponding experimental data available to date.

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How to access Mol-Instincts database…

Download and install Mol-Instincts Navigator software, which connects you to the Mol-Instincts database server online worldwide. Search all the compound list . Play with the 20 pre-determined demo compounds plus 10 compounds at your own choice (free). Then, choose a subscription plan fits for your purpose.

 

STORY

  • Property data and molecular information of chemical compounds
  • Compounds property data and the molecular information with innovative estimation technologies
  • Fundamentals of pure compounds by carrying out quantum chemical calculations
  • Special Chemicals, Rare Chemicals for Chemical Engineering
  • ChemEseen Inc - Chemical professionals

How MOLINSTICTS Developed?

  • 首先,我们通过量子化学计算来研究纯化合物的基本特性。
    • 为了进行准确的量子化学计算,需要输入准确的分子结构,因此我们开发了一个构象分析过程,以生成合理的输入结构。
    • 当存在对映异构体时,它们具有相同的性质值,因此我们开发了一种方法将其滤除。
    • 在量子化学方法中,存在超过1,000种不同精度水平和基组的组合。我们对大量组合进行了研究和测试,最终确定了一个能够生成符合我们标准的、结果足够好的过程。

  • 量子化学计算不能直接生成热物理化学性质。我们将导出的量子化学信息与许多传统的热力学和量子化学基础理论进行了结合,例如统计热力学。
  • 将量子化学信息与传统理论结合仍然无法达到令我们满意的预期结果。因此,我们决定采用近期在科学期刊上发表的现代建模方法,例如SVRC (Scaled Variable Reduced Coordinates, 缩放变量简化坐标)。通过采用这种方法,希望进一步改进我们的研究。
  • 尽管有部分不令人满意,但我们通过向模型中添加了QSPR(定量构性关系)和ANN(人工神经网络)的方法来进一步改善结果,同时,密切关注过拟合的可能性。随后,我们开发并添加了预防过拟合算法。
  • 在30多名科学人员的协助下,利用数百万个实验数据点进行了长达5年以上的准确性验证:
    • 我们从各种可能的来源收集实验数据,包括技术期刊、科学书籍、互联网和商业数据库。
    • 收集的实验数据经过筛选和修正,以去除噪声和不可接受的误差。
    • 筛选的实验数据用于验证估算值的准确性。我们每天都会生成并手动检查成千上万张图表。
    • 我们基于化学分析理论(如相似性分析)开发了一套系统流程,并将其应用于准确性验证工作中。