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计算化学的深度学习：化合物表示、ADMET轮廓和自动优化
Deep learning for computational chemistry: compound representation, ADMET profiles and automatic optimization |
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| 课程网址: |
http://videolectures.net/icgeb_montanari_computational_chemistry/
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| 主讲教师: |
Floriane Montanari
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| 开课单位: |
拜耳股份公司
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| 开课时间: |
2019-06-28
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| 课程语种: |
英语
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| 中文简介: |
小分子药物研发的主要挑战之一是高效地发现具有理想性质的新化合物。这些性质可以是物理化学性质（如logD或溶解度）、药代动力学性质（如渗透性、清除率或代谢稳定性）或药效学性质（如感兴趣目标的生物活性）。长期以来，计算化学一直参与药物发现过程，从命中选择到先导优化。在硅方法中，可以在化学物质合成之前进行快速且经济高效的过滤步骤。在这里，我们讨论了如何利用深度学习进行化学建模的许多不同方面。在化学信息学中，第一步是用计算机可读的方式描述化学物质。我们提出了两种常用圆形指纹的替代方案：分子图上的图卷积和SMILES符号上的序列到序列自动编码器。然后，我们演示了将不同的ADMET端点组合在一个多任务深度学习模型中，与单个任务替代方案相比，如何提高预测性能，尤其是在更难建模的端点上。最后，将我们的化学空间可逆编码与改进的预测模型和优化算法相结合，我们演示了如何根据多个（预测的）分子特性优化查询化合物。我们希望，我们的方法将通过提出合成思想和处理多目标优化，支持药物化学家加速和改进先导优化过程。
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| 课程简介: |
One of the main challenges in small molecule drug discovery is efficiently finding novel chemical compounds with desirable properties. Such properties can be physico-chemical (like logD or solubility), pharmacokinetic (like permeability, clearance or metabolic stability) or pharmacodynamic (like biological activity on targets of interest). Computational chemistry has since long been involved in the drug discovery process from hit selection to lead optimization. In silico methods allow for fast and cost-effective filtering steps before the chemical matter is even synthesized. Here, we discuss how deep learning can be utilized for many different aspects of modeling in chemistry. In cheminformatics, the first step is to describe the chemical matter in a computer-readable way. We present two alternatives to the commonly applied circular fingerprints: graph convolutions on the molecular graph and a sequence-to-sequence autoencoder on SMILES notations. We then demonstrate how combining different ADMET endpoints together in one multitask deep learning model can boost the predictive performance compared to its single-task alternatives, especially on endpoints that are more difficult to model. Finally, combining our reversible encoding of the chemical space with improved predictive models and an optimization algorithm, we demonstrate how a query compound can be optimized with respect to multiple (predicted) molecular properties. We hope that our method will support medicinal chemists in accelerating and improving the lead optimization process by proposing synthesis ideas and handling multi-objective optimization.
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| 关 键 词: |
计算化学的深度学习; 数据科学; 化合物表示; ADMET轮廓和自动优化
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| 课程来源: |
视频讲座网
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| 数据采集: |
2022-10-14：cyh
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| 最后编审: |
2022-10-14：cyh
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| 阅读次数: |
37
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