RUI: D3SC: Advancement of Excitation Energy Transfer Modeling with Deep Learning Algorithms

蒙茅斯大学的Dmitri Kosenkov获得了化学系化学理论，模型和计算方法项目的奖项，以通过结合深度学习技术来推进复杂分子系统中电子激发能量转移动力学建模的计算方法。激发能量转移是自然和人工系统中光捕获和利用的一个基本重要过程。现代技术，如光化学和分子传感器，涉及能量转移。特别是，了解分子荧光探针中的激发能量转移机制对于靶向蛋白质，DNA序列和细胞内环境的传感至关重要。模拟激发能传递过程的挑战与分子系统的大尺寸和复杂结构有关。Kosenkov教授及其同事正在将机器学习融入计算化学技术中，以克服这些障碍。这种方法可以加速计算并降低计算成本。科学知识在化学生物学、人工光合作用、有机光合作用和光疗法等领域具有发展潜力。Kosenkov教授在他的研究中涉及蒙茅斯大学的本科生，蒙茅斯大学是一所主要的本科院校。Kosenkov小组通过计算化学软件和基于研究的实验室项目向更广泛的科学界传播计算方法，这些项目可以在美国和世界各地的学院和大学实施。最后，这项工作支持蒙茅斯大学的本科生和高中生的计算化学研讨会，在那里，计算化学，化学生物学和机器学习领域的领先科学家分享他们的知识和经验。该研究的重点是新型生物正交硼二吡咯亚甲基衍生的探针，为细胞器的可视化，细胞内药物分布提供了革命性的工具，并通过在光激活下控制细胞毒性单线态氧的产生为潜在的光疗开辟了可能性。激发能量转移是这些分子荧光探针工作的关键过程。 Kosenkov教授和他的合作者正在开发一种原创的计算方法，将量子动力学理论与深度学习算法相结合。能量转移的动力学模型与量子主方程方法。深度学习组件能够高通量筛选电子激发能量和耦合，进一步用于量子动力学模拟。该方法是有利的，因为它的灵活性和适用性，以广泛的分子系统。这些方法能够阐明和比较不同化学结构的分子荧光探针的激发能量转移机制。此外，软件工具模拟了广泛的分子系统，并可以允许随着领域的发展进一步迭代改进。教育部分的重点是开发和传播一系列以研究为基础的多周计算实验室单元，主要用于物理和计算化学课程。这些实验室单元涵盖了深度学习算法的基础知识及其在化学中的应用，包括荧光探针的建模。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Dmitri Kosenkov of Monmouth University is supported by an award from the Chemical Theory, Models and Computational Methods program in the Division of Chemistry to advance computational methods for modeling electronic excitation energy transfer dynamics in complex molecular systems by the incorporation of deep learning techniques. Excitation energy transfer is a fundamentally important process for light harvesting and utilization in natural and artificial systems. Modern technologies, such as photovoltaics and molecular sensors, involve energy transfer. In particular, understanding excitation energy transfer mechanisms in molecular fluorescent probes is crucial for targeting proteins, DNA sequences, and sensing of intracellular environments. The challenges in modeling excitation energy transfer processes is associated with the large sizes and complex structures of the molecular systems. Professor Kosenkov and coworkers are incorporating machine learning into computational chemistry techniques to overcome these obstacles. This approach may accelerate computations and reduce computational cost. The scientific knowledge has the potential to advance in the fields of chemical biology, artificial photosynthesis, organic photovoltaics, and phototherapies. Professor Kosenkov involves undergraduate students at Monmouth University, a primarily undergraduate institution, in his research. The Kosenkov group disseminates the computational methods to the broader scientific community via computational chemistry software and research-based laboratory projects that can be implemented in colleges and universities in the United States and worldwide. Finally, the work supports computational chemistry workshops for undergraduate and high school students at Monmouth University, where leading scientists in the fields of computational chemistry, chemical biology, and machine learning share their knowledge and experience. The research is focused on novel bioorthogonal boron dipyrromethene-derived probes that offer a revolutionary tool for visualization of cell organelles, intracellular drug distribution, and open a possibility for potential phototherapy through controlled generation of cytotoxic singlet oxygen under light activation. Excitation energy transfer is a key process for operation of these molecular fluorescent probes. Professor Kosenkov and his collaborators are developing an original computational methodology that integrates quantum dynamics theories with deep learning algorithms. The dynamics of energy transfer is modeled with the quantum master equation methods. The deep learning component enables high-throughput screening of electronic excitation energies and couplings further used in quantum dynamics simulations. The methodology is advantageous due to its flexibility and applicability to a wide range of molecular systems. The methods enable elucidation and comparison of the mechanisms of excitation energy transfer for molecular fluorescent probes of varied chemical structures. Moreover, the software tools simulate a wide range of molecular systems and may allow for further iterative improvements as the field develops. The educational component is focused on development and dissemination of a sequence of research-based multi-week computational laboratory units, primarily for physical and computational chemistry courses. These laboratory units cover basics of deep leaning algorithms and their applications in chemistry including modeling of fluorescent probes.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.